A Novel Intelligent Thermal Feedback Framework for Electric Motor Protection in Embedded Robotic Systems

Abstract

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures and dynamically change operational parameters. Unlike traditional reactive methods, this framework provides a spacious active/predictive method of heat management, while preserving efficiency and allowing for responsive control. Simulations, experimental validations, and preliminary trials that deployed real robotic systems demonstrated that our framework allows for reductions in peak temperatures by up to 18% and extends motor lifetime by 22%, while retaining control stability and a range of variations in PWM adjustments of ±12% across disparate workloads. These results demonstrate the efficacy of intelligent and thermally aware motor control architectures and processes to improve the reliability of autonomous robotic systems and open the door for next-generation embedded controllers that will allow robotic platforms to self-manage thermal effects in resilient, adaptable robots.

1. Introduction

The electric motor is an essential part of autonomous robotic systems, providing direct actuation for movement, manipulation, and other control actions. Recent advances in hardware and software for edge computing platforms have made motor control based on deep neural networks (DNNs) that are deployed directly on these platforms possible, providing real-time processing of sensor data, latency reduction, and predictive capability. These methods combine lightweight machine learning models with methods inspired by adaptive control to improve temperature prediction, facilitate the introduction of more operational parameters into the prediction model, and improve the reliability of motors in constrained robotic systems [1,2]. It has been an important research area to ensure the reliability and performance of electric motors while operating in a variety of operating conditions, as robotic applications become increasingly complex and require better performance [3,4]. Out of the many factors that can affect motor reliability, thermal effects are one of the primary causes of wear and failure [5,6]. High temperatures lead to wear and performance loss by reducing the life expectancy of the motor and performance due to increased resistance, reduced insulation degradation, and the introduction of mechanical stresses [5].

Conventional control methods for motors, which use fixed-gain PID controllers, do not take thermal dynamics into account; rather, they really focus on the mechanical states of position, speed, or torque [7,8]. In embedded systems for robotics, which often have very stringent size, weight, and energy constraints, disregarding thermal dynamics could lead to excessive thermal stress if the motors are operated for prolonged periods and/or under heavy loads [9,10]. If the motors overheat, operate inefficiently, or even shut down due to the inability to account for temperature changes in the system, the usability and dependability of the robot and, ultimately, the mission success can be compromised [10].

A general functional architecture of the thermally guided motor control system is represented in Figure 1 to clarify this concept. A predictive model uses data from real-time heat monitoring and dynamically adjusts the settings of the integrated motor controller. This closed-loop design allows for effective thermal management while maintaining system performance.

In recent years, technological achievements in sensor miniaturization and embedded processing capabilities have greatly advanced the ability of robotic platforms to monitor thermal conditions with high spatial and temporal resolutions. Temperature data can now be obtained in real time for critical motor elements: stators, rotors, windings, and bearings [11,12]. These temperature data improve the thermal resolution of needed control systems. This nuanced information can enable intelligent feedback control methods so that motor inputs can be altered based on the thermal condition. By appropriately managing the produced heat, heat dissipation, or both, thermal overload can be all but prevented, and the energy efficiency can be increased while also prolonging the longevity of electric motors in embedded robotic approaches.

At the same time, machine learning (ML) methods have received considerable attention for electric motor predictive maintenance and thermal modeling [13,14]. Machine learning models can also capture complex patterns of nonlinearities and temporal interdependences that shape thermal behavior at varying loads and environmental conditions. Low-Mb budget lightweight machine learning architectures that are suitable for embedded use can predict reasonably accurate trends in temperature at a lower computational cost and are considered suitable for use in real-time contexts [15,16]. Predictive models may offer the opportunity to proactively alter behavior patterns to reduce thermal stress, increase the lifespan of components, and maintain operational performance when the models are integrated into motor control loops [17,18].

Many adaptive control strategies have explored the adjustment of control parameters based on feedback received about temperature. Examples include sliding mode control (SMC) and model predictive control (MPC) [19,20]. These strategies have the potential to improve thermal control in both lab and real-world implementation; however, most adaptive strategies are limited in the computational complexity, latency, and power consumption needed [21,22].

The recently presented data-driven thermal modeling approaches greatly enhance the accuracy and responsiveness of temperature forecasts, while also allowing for improved thermally based (control) actions [23]. Intelligence in thermal management is important in robotics—intelligent thermal management can allow for preservation of motor performance under varying thermal loads via the seamless combination of thermal prediction models and robotic control loops [24]. Moreover, as the nature of electric motor applications in robotics changes, there is a need for control systems that are thermally aware and resource-responsible to create longer-lasting motor lifetimes and more secure operations [25,26].

Figure 2 depicts the integration of real-time temperature sensors, adaptive motor control, and lightweight machine learning prediction. The sensors collect temperature information that is processed by a lightweight machine learning model to predict thermal dynamics. These predictions are then embedded into a selection process by an adaptive control algorithm. This type of closed-loop system manages the event of heating while fulfilling the constraints of an embedded system, as the algorithm will continuously adapt the characteristics of the motor to manage the heat more effectively.

Even with the significant improvements made so far, we are still in great need of dependable, portable, and computationally efficient control frameworks that can operate in real time on embedded robotic systems. Current systems generally have challenges in finding appropriate balances between processing requirements, model accuracy, and ease of integration during an autonomous robot’s operation, all of which serve to limit their general applicability in autonomous robots.

We present a new thermally guided intelligent control framework that uses low-overhead predictive modeling and real-time temperature readings to reconfigure motor control settings on the fly. The goal is to improve control accuracy, maximize the operational life of the motor, and facilitate optimum heat dissipation. While potential exists, the implementation of the framework will operate within the bounds of many limitations that are familiar to embedded robotic systems—including sensor noise, rapid input changes, unmodeled disturbances in the environment, and limitations imposed by onboard computation. All of these may introduce errors into temperature predictions and delay control updates that limit the ability to reliably protect the motor and or the overall system.

The following are the primary contributions:

✓

A new intelligent thermal feedback management framework for electric motors in embedded robotic systems will help to increase operational resiliency under changing conditions.

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Lightweight machine learning models are leveraged with adaptive control techniques to enable precise motor temperature prediction and proactive control parameter manipulation.

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The effectiveness of the proposed framework is shown through extensive computations and meaningful experimental validations.

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The reduction in heat stress increases the life and reliability of motors without any cost to the performance or response time.

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Next-generation embedded motor controllers that can regulate their own temperature are created, making robotic systems more intelligent and dependable.

The organization of this document is as follows: Section 1 provides a background and rationale for undertaking the research. Section 2 reviews the relevant literature on intelligent motor control and thermal management. Techniques and resources used in the proposed framework are presented in Section 3. Section 4 shows the experimental results, using both simulation and real-world application. Section 5 discusses the main conclusions, possible limitations, and implications of the methodology. Section 6 provides a conclusion to the study and suggests avenues for future research.

2. Related Work

This section discusses key research on thermal management of electric motors in embedded robotic systems. It investigates embedded motor control, machine learning for temperature prediction, lightweight AI models, and adaptive, thermal-aware control strategies. In addition, sensor calibration, fault-tolerant design, and recent experimental validation describing the benefit of thermally intelligent robotic systems are addressed. Finally, the account highlights the gaps in research that the proposed approach intends to fill.

2.1. Thermal Management in Electric Motors

In [27], the author discusses some popular ways to control the temperature of electric motors, including passive cooling via heat sinks and active cooling with fans or liquid circulation. Despite all their advantages in industrial applications, they often add system weight and power usage, which is a problem for embedded applications such as mobile robots and drones.

2.2. Embedded Motor Control Systems

The authors of [28] said that embedded control systems are typically not designed to allow for thermal feedback, which leads to inefficiency in the motors and potentially damaging effects to the motors. Their discussion shows that high-performance thermal-sensitive components do not translate well into the existing microcontroller-based architectures. The authors of [29] studied thermal stress and motor inefficiency due to variable loads. They proposed using predictive modeling in their findings, anticipating thermal behavior and taking corrective action before system performance failure occurs.

2.3. Machine Learning for Thermal Prediction

In [30], the authors proposed a real-time temperature monitoring system for electric motors using a neural network. They developed a model that predicts motor temperature based on operational variables such as current, torque, and speed and concluded that their neural network was more accurate in forecasting motor temperature than traditional PID thermal protection systems.

The authors in [31] reviewed the different temperature sensors and machine learning approaches involved in thermal management of electric vehicles. They found that the combination of temperature sensors and intelligent prediction techniques aided in improved safety measures and increased the lifetime of electric motors.

2.4. Lightweight ML Models for Embedded Systems

The authors in [32] put forth a sensor fusion and deep learning method for predicting heat in an autonomous robot, which performs well in simulation; however, they state that it is too computationally expensive for most embedded platforms to use in practice, which limits its ability to operate in a low-power environment. The authors of [33] develop a thermally aware embedded control algorithm that can assess thermal prediction quality based on hardware constraints and relies on edge computing to improve latency and reduce energy consumption. The authors of [34] investigate how to deploy neural networks in resource-limited microcontrollers. They demonstrate how quantized and pruned models can reduce memory demand while providing enhanced high-quality thermal prediction.

2.5. Thermal-Aware Adaptive Control Strategies

In [35], the authors consider energy-efficient architectures of embedded intelligent systems where optimization is necessary at the hardware and software levels. This is especially true in thermal-sensitive systems where the energy-impacting degradation of temperature is happening at all levels of the embedded system. The authors of [36] suggest a hybrid convolutional neural network–recurrent neural network model for thermal prediction and adaptive controls in real time. In their system, the motor parameters adjust dynamically according to predicted trends in temperature, which increases safety and responsiveness.

2.6. Sensor Calibration and Data Acquisition

In [37], the authors clarified that the paper discussed a hierarchical fault-tolerant system that utilizes thermal feedback and redundancy in embedded motor controllers. The method relies on detecting abnormalities in the controller and taking corrective actions before an overheating situation arises, making for safer operation. In [38], the author spoke primarily about sensor calibration for collecting thermal data. One of the points raised was that inaccurate calibration leads to thermal states being either over- or under-estimated, thereby potentially jeopardizing the safety of the motor and compromising long-term reliability. The authors of [39] state that acquiring high-quality data from diverse resources is the keystone for predictive thermal models. They discuss how to monitor heat for fully embedded systems in terms of optimal sensor placement and noise rejecting techniques. The final paper, [40], discussed the certified neural control architecture, which assures the safety and reliability of a system that is given thermal constraints prior to departing from operating limits. The architecture is designed for high-assurance, aerospace, and robotics applications, where thermal failures could be catastrophic.

Table 1. Comparative analysis of existing thermal management and motor control approaches.

Ref.	Control Approach	Thermal Model	Sensors	Embedded Optimization	Real-Time	Adaptive Control
[27]	Static threshold	Offline statistical	Single temp	No	No	No
[28]	Rule-based adaptive	Simple predictive	Temp, current	Limited	Partial	Partial
[29]	ML-based prediction	Complex ML	Temp, current, voltage	No	No	Partial
[30]	Adaptive PWM	Regression	Voltage, current, speed	Limited	Partial	Partial
[32]	Hybrid ML and rule-based	Lightweight ML + rules	Multi-sensor	Some	Partial	Partial
[34]	Thermal-aware dynamic	Statistical thermal	Temp	No	Partial	No
[36]	Fuzzy logic control	Fuzzy inference	Temp, current	Limited	Partial	Yes
[38]	Model predictive (MPC)	MPC	Temp, electrical	Heavy	Limited	No
[40]	RL-based control	RL with thermal feedback	Multi-sensor	Limited	Experimental	Yes
This Work	Adaptive PWM and switching freq	Lightweight ML (pruned, quantized)	Current, voltage, speed, ambient temp	Optimized for embedded	Yes	Yes

summarizes the recent theoretical capabilities of and experimental results for thermal management strategies that are applied to embedded control of an electric motor. Unlike many earlier thermal management approaches, which typically only discuss their simulation or design aspects, our results demonstrate true real-time capabilities, high predictive accuracy, and low-latency adaptive control. The results provide evidence that the proposed system outperformed other existing methods in a practical embedded context, which confirms its viability for actual robotic applications. Additionally, the proposed solution successfully integrates the predictive intelligence from ML, with an adaptive PID control policy, thus enabling the system to foresee thermal dynamics and modify motor parameters in real time. This synergistic combination of motor control properties yields increased safety and reliable operation of the motor, whilst also providing a possible reduction in energy usage and degradation in motor performance for varying loads. These benefits illustrate the suitability for real-world, embedded robotic applications, where both safety and energy efficiency can be accomplished.

3. Materials and Methods

This section outlines the proposed strategy for implementing a thermally guided intelligent control system for embedded motors using a scientific methodology. The overall, highly abstract (layered) architecture, outlined in Figure 3 below, integrates sensing, predictions, decision-making, and actuation activities, with each component performing a pivotal role across the eight axes and a shared knowledge/data feed.

3.1. A Layered Architecture of the Embedded Control Framework

The proposed system is structured with five functional layers to manage thermal limits in embedded motor systems. Each layer performs a specific job, resulting in a chain of tasks—from sensing to adaptive actuation. An architectural overview is provided in Figure 3.

Table 2. Roles of the layers in the intelligent thermal control architecture.

N	Layer	Components	Role
1	Sensing Layer	Temperature sensors, current sensors	Collects real-time data about motor’s thermal state and operating conditions.
2	Data Processing Layer	Signal conditioning, data normalization, sensor fusion	Preprocesses raw sensor data for accurate and stable input to prediction models.
3	Prediction Layer	Lightweight machine learning model (e.g., MLP, LSTM)	Predicts future thermal dynamics to support proactive control decisions.
4	Control Layer	Adaptive control algorithm, control rules	Adjusts motor parameters (e.g., voltage, duty cycle) based on predictions and feedback.
5	Actuation Layer	Motor driver, embedded actuators	Executes control commands while maintaining motor efficiency and thermal safety.
6	Communication Layer	Microcontroller interfaces (UART, SPI, I2C), wireless modules	Facilitates internal and external data transmission across system layers.
7	Monitoring Layer	Logging unit, diagnostics, fault detection module	Tracks system performance and thermal status for safety and maintenance.

describes the essential layers of the intended control framework, the main elements, and their role within the system. The framework is designed to allow for real-time temperature monitoring, low-power thermal prediction, and adaptive control for improved operational efficiency of the motor while under the imposed embedded constraints.

3.2. System Flowchart

The flowchart below depicts the multiple layers of decision-making associated with the thermally driven motor control design. The design uses an efficient machine learning model (ML) to measure the thermal state of the motor after the temperature is recorded and preprocessed.

The expected temperature is validated for the condition that has surpassed a safety temperature in the conditional decision block; the alternate adaptive control option or default is executed, allowing for real-time adjustment to the motor action based on its thermal state. The thermal feedback loop illustrated in Figure 4 enables continuous learning and adjustment, while the design also incorporates monitoring and logging modules for debugging, as well as maintenance of the system.

3.3. Integration of a Lightweight Machine Learning Model in Autonomous Robots

In this layer, a lightweight machine learning (ML) model is integrated into autonomous robotic platforms to enable efficient, real-time thermal prediction within resource-constrained embedded systems. The model was trained offline using the publicly available Electric Motor Temperature dataset from Kaggle, which provides comprehensive sensor data reflecting electric motor behaviors under a variety of operational scenarios.

Key input features, including the current, voltage, rotational speed, and ambient temperature, were carefully selected through feature engineering to ensure input relevance and maximize the predictive accuracy. The dataset was divided into training, validation, and testing subsets to promote model generalization.

Various ML algorithms, such as decision trees, random forests, and lightweight neural networks, were evaluated with the goal of balancing accuracy and computational efficiency. To satisfy the hardware limitations of embedded systems within robots, the selected model underwent optimization techniques, including pruning and quantization. These compression methods significantly reduced the model’s memory footprint and computational load, enabling seamless deployment onto embedded robotic hardware.

The selected lightweight neural network is a compact multilayer perceptron (MLP) architecture with two hidden layers (32 and 16 neurons, respectively) and ReLU activation functions for clarity. The model was trained using Adam as the optimizer, with early stopping to prevent overfitting and a learning rate of 0.001. After quantization and pruning, the optimized model had an average inference latency of <5 ms and required <200 kB of memory on a 120 MHz ARM Cortex-M4 microcontroller. This means that the model can be employed to operate in real time within robotic control loops while operating reliably with expected accuracy.

Figure 5 shows that the resulting model empowers the robot to predict the motor temperature in real time, facilitating proactive control decisions that minimize thermal stress and extend component longevity.

3.4. Thermal-Aware Adaptive Control Strategy for Robotic Systems

The robotic system works in close coordination with the temperature forecasts that are generated from the onboard ML model to ensure thermal safety and dependable performance via a thermal-aware adaptive control approach. This adaptive control approach allows dynamic changes to control parameters like the switching frequency and PWM duty cycle based on the anticipated trajectories for temperature. Thermal thresholds limit the control options. For example, a greater than expected increase in temperature near the thermal limit might require a greater adjustment to change the power output or even engage some internal cooling device, while a smaller than expected increase in temperature (which is the expected case) may not even warrant a 0.01% downward adjustment to the PWM rate.

This method operates as a closed loop system that continuously inputs position data from integrated temperature sensors and actuators to monitor the thermal condition and respond to it in real-time in an automatic control mechanism. As a result, autonomous robots can operate safely under a variety of workload and environmental conditions with a controlled system that is fast, dependable, and maintains motor health, as can be seen in Figure 6.

3.5. Sensor Data Collection and Calibration in Robotic Platforms

The Electric Motor Temperature dataset from Kaggle was the primary data source used to train the temperature prediction model, which was incorporated into the robotic device. The dataset includes many sensor measurements of electrical and thermal characteristics under several different operating conditions.

To ensure the accuracy of data collection, each sensor underwent a full calibration process before training the model. Bias corrections had to be performed on the measurements to compensate for any offset issues, and each sensor signal had to be compared against reference equipment under controlled conditions. Important motor components were equipped with temperature sensors, and the data was supplemented by the electrical measurements of voltage and current. Sensor data was collected at a sampling frequency that was sufficient to eventually record the thermal dynamics that are essential to real-time prediction in robotic applications, at [1 Hz]. Large ranges of motor loads and ambient temperatures were captured in the experimental conditions, gathering a wealth of data to construct a viable and likely generalizable thermal prediction model for embedded deployment, as shown in Figure 7.

3.6. Thermal Modeling and Intelligent Control Equations

To predict and control the thermal behavior of electric motors in real time, a mathematical thermal model is used. The model forms the foundation of the intelligent feedback controller design.

3.6.1. Thermal Dynamics of Electric Motors

The thermal behavior of an electric motor can be quantified through a lumped-parameter first-order differential model. The heat transfer processes are determined by the energy balance, in which the dissipation of power in the windings is offset by heat loss to the environment, as given by the following:

$$C_{th }{\displaystyle \frac{dT\left(t\right)}{dt}} ={ P}_{loss }\left(t\right) - {\displaystyle \frac{T\left(t\right) - T_{amb}}{R_{th}}}$$

(1)

Here, $\mathbf{T}\left(\mathbf{t}\right)$, the internal motor temperature at time $\mathbf{t}$, is represented by $\mathbf{T}\left(\mathbf{t}\right)$, the ambient temperature is represented by ${\mathbf{T}}_{\mathbf{a}\mathbf{m}\mathbf{b}}$, the thermal capacitance (J/°C) is represented by $\mathit{C}$, and the thermal resistance (°C/W) is represented by ${\mathbf{R}}_{\mathbf{t}\mathbf{h}}$.

$$P_{loss }\left(t\right)={ I\left(t\right)}^{2 }R$$

(2)

In Equation (2), the Joule heating power is represented by ${\mathbf{P}}_{\mathbf{l}\mathbf{o}\mathbf{s}\mathbf{s}}\left(\mathbf{t}\right)$, where $\mathbf{I}\left(\mathbf{t}\right)$ is the motor phase current and $\mathbf{R}$ is the winding resistance. This model is good for embedded implementation, since it provides an estimate of the thermal state of the motor in real time with very small processing costs.

3.6.2. Intelligent Thermal Feedback Control Strategy

To prevent thermal failure and provide proper functioning, a feedback control loop is executed. The control objective is to regulate the temperature of the motor by adjusting its operating parameters, follow the preassigned reference temperature, and define the temperature tracking error:

$$e\left(t\right) = T_{ref} - T\left(t\right)$$

(3)

As can be seen in Equation (3), $T$ is the reference temperature and $e\left(t\right)$ defines the deviation. A proportional integral derivative (PID) controller is used to compute the control input.

$$\left(t\right)=K_{p}e\left(t\right)+K_i{\int }_0^{t}e\left(\tau \right)d\tau +{ K}_d {\displaystyle \frac{de\left(t\right)}{dt}}$$

(4)

In Equation (4), $u\left(t\right)$ is the actuator command (i.e., PWM manipulation or current limitation), while $K_p$, $K_i$, and $K_d$ are the PID gains. This type of controller provides real-time correction for thermal perturbations and can be parameterized depending on the needs of the system.

To augment the estimates of temperature, a machine learning model is incorporated to change the system input variables. In cases where a direct measurement of the temperature cannot be taken or is not feasible, it uses the ambient temperature, motor current, and duty cycle to provide a more accurate representation of internal temperatures than in previous reports of similar work.

3.7. Integration of ML Model and PID Controller for Intelligent Thermal Feedback

The lightweight machine learning (ML) model in our intelligent thermal feedback system predicts the internal motor temperature in real time based on data from electrical and environmental sensors, while the PID controller adaptively updates the PWM control signals based on these motor temperature predictions. This proactive and intelligent thermal management system goes beyond simple sensing of temperature and allows the system to anticipate temperature changes in the future and respond accordingly. The ML model predicts accurate temperature prediction trends, and the PID controller provides precision actuation in real-time to create an intelligent feedback loop that provides enhanced motor protection and reliability in embedded robotic systems.

Figure 8 presents the hybrid thermal management system of the autonomous robot. The motor time series data (e.g., current, voltage, rotational speed, and room temperature) are provided to the lightweight ML model in real time to make predictions of future temperatures of the motor. The predicted temperature is then compared with a thermal setpoint, and the error is calculated and fed back to the PID controller. Then, the PID controller passes its values for the PWM duty cycle and switching frequency through an internal look-up table to maintain the motors in a safe range of operation. This closed-loop feedback mechanism allows for pre-emptive adjustments to prevent overheating while optimizing performance and efficiency.

3.8. Data Collection and Features

The Electric Motor Temperature dataset published in Kaggle was used for this research work. The dataset is from a permanent-magnet synchronous motor (PMSM) prototype supplied by a German OEM and was tested in a controlled laboratory bench in Paderborn University (LEA Department).

The dataset consists of approximately 185 h of recordings sampled at 2 Hz, which accumulates to more than 1.3 million samples. The measurements are organized into sessions, separated by a column id called profile_id, and consist of 1–6 h sessions. The driving cycles to the motor were hard-designed driving cycles and consisted of reference speed and torque signals to represent a realistic driving environment.

Data editing consists of outlier removal, normalizing continuous features, and dividing the data into training (70%) and test (30%) sets. Overall, this data has been leveraged throughout the literature to execute temperature estimates, motor health tracking, and machine learning tasks.

Table 3. Statistical summary of the Electric Motor Temperature dataset.

Feature Name	Category	Unit	Min	Max	Mean	Std Dev
i_d	Electrical	A	−35.0	30.0	−0.54	9.23
i_q	Electrical	A	−120.0	120.0	0.37	45.16
u_d	Electrical	V	−600.0	600.0	0.12	210.34
u_q	Electrical	V	−600.0	600.0	1.01	220.87
motor_speed	Mechanical	rpm	0.0	12,000.0	3540.0	2810.0
torque	Mechanical	Nm	−60.0	60.0	0.15	22.46
ambient	Thermal	°C	20.0	35.0	25.3	3.10
coolant	Thermal	°C	25.0	105.0	47.2	18.7
stator_winding	Thermal	°C	30.0	155.0	65.8	26.4
stator_yoke	Thermal	°C	30.0	145.0	60.5	24.3
stator_tooth	Thermal	°C	30.0	160.0	63.9	25.8
pm (rotor magnet)	Thermal (Target)	°C	30.0	165.0	68.4	27.1

provides descriptive statistics for the data features. Electrical features (i_d, i_q, u_d, u_q) include excitation inputs and control signals. Mechanical features (motor_speed, torque) include the operating state of the field-oriented control permanent-magnet synchronous motor (PMSM). The thermal features range from environmental (ambient, coolant) to the internal motor temperature (stator winding, yoke, tooth, and permanent magnet). The permanent magnet temperature (pm) is plausible, since it is a highly sought-after temperature feature for predictive modeling tasks and is important when monitoring motor health and concerns of thermally safe operating limits.

4. Experimental Results

This section presents the outcomes of various tests performed to validate the proposed intelligent thermal feedback system. The experiments cover signal behavior, temperature analysis, control response, and energy efficiency. Real-time data were collected from embedded sensors in the motor control environment.

4.1. Voltage Signal Behavior Under Load Conditions

This subsection looks at the motor voltage signal of a 12 VDC nominal voltage motor, which increases as the mechanical and thermal loads increase. The thermal-aware control system dampens the measured voltage fluctuations to avoid overvoltage conditions and ensures stability and repeatability with respect to the motor condition.

The motor temperature under different operating conditions is compared against a safety threshold of 75 °C, consistent with the motor manufacturer’s guidelines. As shown in

Table 4. Motor temperature with and without thermal control.

Condition	Max Temperature (°C)	Average Temperature (°C)	Safety Threshold (°C)	Safety Threshold Exceeded
Without Thermal Control	95.6	78.3	75	Yes
With Thermal Control	78.4	65.1	75	No

, the uncontrolled motor exceeds this threshold, while the motor under thermal feedback control maintains the temperature below this critical limit.

The motor voltage signal under increasing mechanical and thermal loads is shown in Figure 9. It is clear that the voltage waveform exhibits slight variations under higher stress, with minor oscillations. The thermally aware control logic stabilizes these oscillations, preventing overvoltage situations and ensuring safe operation.

The motor temperature over time is included with the voltage signal for the conditions described, along with the blended voltage and temperature graph, which creates a better comparison between the electrical response and thermal behavior. As is easily noted, the temperature-aware control strategy stabilizes the voltage fluctuations, while ensuring that the motor temperature remains below the safe limit—this shows that the electrical stabilizing action and thermal regulation action are close together.

Table 4 shows a comparison of motor temperatures under operating conditions with and without thermal feedback control, demonstrating that the controlled system keeps the temperature below a critical safety limit.

4.2. Thermal Response of the Electric Motor

The thermal performance (temperature rise) of the electric motor was evaluated without thermal feedback control versus with thermal feedback control. This study was completed using a 12 VDC nominal voltage motor with a target temperature setpoint and maintained at 60 °C.

The uncontrolled motor exceeds the thermal limits, reaching temperature levels greater than 75 °C, which is the overheating safety threshold. The uncontrolled motor can produce overheating; such behavior could result in damage. The motor with thermal feedback control maintains temperatures approximately at the 60 °C setpoint, avoiding overheating and maintaining performance.

In

Table 5. PWM duty cycle vs. motor temperature.

Temperature Range (°C)	PWM Duty Cycle Reduction (%)	Control Action Description
35–60	0	Normal operation
60–75	10–15	Moderate PWM reduction
75–85	20–30	Significant PWM reduction
>85	40+	Aggressive power reduction/cooling

, the adaptive PWM duty cycle reduction corresponding to different motor temperature ranges is shown, reflecting thermal-aware control actions.

The data shown in Figure 10 is shown with time on the bottom and motor temperature in C on the left-hand side. This representation focuses on the behavior of the thermal evolution of the motor over time, both with closed-loop feedback control and without feedback, and shows clearly how the feedback control strategy allows the system to operate well within safe operating limits.

4.3. Dynamic PWM Control Based on Thermal Feedback

This part describes the real-time adjustments of the PWM duty cycle based on in-the-moment predictions of the motor temperature. The intent is to be operating on a marginal thermal stress level by reducing the power supplied to the motor when the temperature goes up and increasing power according to its cooling levels to protect the motor while maintaining its performance.

Table 6. Impact of filtering on signal noise and stability.

Signal Condition	Noise Level (dB)	Signal Stability Rating (1–10)
Before Filtering	−50	5
After Filtering	−75	9

illustrates the effects of feedback stabilization and filtering when applied to the stability and noise of the measured voltage signal. Feedback stabilization suppresses the overshoot, and the filters remove high-frequency noise and eliminate variance in unwanted motor control performance noise, so that the feedback control is visible.

Figure 11 displays the thermal response and the control signal, with two vertical axes to reduce confusion. The left axis is for the motor temperature (°C), and the right axis is a PWM duty cycle (percentage). We used dual scales to clearly separate the two variables and show the direct relationship occurring once the motor was operational.

4.4. Signal Integrity and Noise Reduction

We will now assess the quality enhancement of the voltage signal after feedback stabilization and filtering are implemented. Somewhat improved signal stability = decreased high-frequency noise, which helps to provide more accurate and reliable motor control for embedded robotic systems.

The output voltage signal is shown in Figure 12, both before we apply the filtering and feedback stabilization and after we apply the filtering and feedback stabilization. As we noted before, the filtered signal shows a marked reduction in high-frequency noise compared to the signal for accurate motor control in embedded systems.

4.5. Real-Time Control Delay Analysis

This subsection presents the delay from detection of overheating to execution of control action under different load conditions. The system’s total reaction time is less than 30 milliseconds, so the system is able to meet the real-time requirements for managing heat effectively in robots.

The measured reaction latency from the time the system detects overheating until the control action (PWM reduction) is performed is summarized in

Table 7. Average delay in control signal response to detection of overheating.

Scenario	Detection Time (ms)	Control Action Delay (ms)	Total Response Time (ms)
Low Load	5	12	17
Medium Load	6	13	19
High Load	8	15	23
Overload Condition	10	17	27

. The results show that the average latency is less than 20 ms for the delay time, which is suitable for robotic applications that require real-time control.

Figure 13 depicts the measured response times of the motor thermal feedback system under various loading conditions (low, medium, high, and overload). Each bar represents the detection time (time from overheating occurrence to detection), control action delay (time for PWM change to be applied), and total response time (the sum of detection and action delays). All the results show that the system is capable of meeting real-time control requirements, with the total response times being well below critical response limits, ensuring adequate safety for the motor from overheating.

4.6. Dataset Description and Feature Distribution

An overview of the major sensor features that were collected during the experiments, such as the voltage, temperature, current, PWM duty cycle, and control delay, is provided. Summary statistics indicate the range and variability of the features and add confidence in the robustness of the thermal prediction model.

The dataset we used to test included time series values of temperature, voltage, current, PWM duty cycle, and environmental parameters.

Table 8. Summary statistics of the experimental dataset features.

Feature	Min	Max	Mean	Std. Dev.
Voltage (V)	11.8	13.2	12.5	0.42
Temperature (°C)	35.2	87.4	65.1	11.8
Current (A)	0.6	2.3	1.4	0.31
PWM Duty Cycle (%)	45.0	98.0	73.5	15.7
Control Delay (ms)	11	27	18.9	3.8

provides a summary of feature distributions, taken from the real-time system logs.

Figure 14 illustrates the distributions of the important features that were retrieved from the motor control experiments, including the voltage, temperature, current, PWM duty cycle, and control delay. Each feature includes boxplots and scatter plots to demonstrate the minimum, maximum, mean, and variability. The figure exhibits the completeness and variability present in the dataset and provides sufficient evidence of confidence in the thermal feedback model.

4.7. Energy Efficiency and System Optimization

The system’s energy consumption data, obtained with the thermal feedback control system in place, was analyzed alongside consumption data obtained when that control was not present. It can be observed that the intelligent control strategy optimally reduces excess power consumption during overheating and idle conditions and results in approximately 12% less energy being consumed under high-load scenarios, improving the overall system efficacy.

Table 9. Power consumption and savings under load conditions.

Operating Condition	Power Consumption Without Control (W)	Power Consumption with Control (W)	Energy Savings (%)
Low Load	45.8	44.2	3.5
Medium Load	68.4	61.2	10.5
High Load	92.7	81.6	12.0

shows a comparison of power consumption modes under different load conditions and reveals the amount of power consumption savings produced by the thermal feedback system.

Figure 15 compares the energy usage of the motor control system under different load conditions (low, medium, and high) with and without thermal feedback. The bars indicate power consumption during low, medium, and high loads, and the percentage values represent energy savings that occurred through intelligent thermal management. The figure illustrates that although power savings were achieved through thermal feedback control in all operational states, it was particularly effective in minimizing unnecessary power usage during times of high loads and idle times. In fact, the results signify an overall energy efficiency increase of up to 12% during high-load conditions through the use of thermal feedback control.

4.8. Reproducibility and Trial Information

In order to maintain the transparency and reproducibility of the experimental results, all comparative tests in this paper were repeated; each situation was run independently five times, and the results are presented as the mean ± standard deviation. This process was important to make sure that what we are reporting is actually the performance of the intelligent thermal feedback framework, instead of what random variations happened every time we ran the simulation.

The intelligent thermal feedback framework is built between a lightweight ML model for real-time motor temperature prediction and a PID controller that uses the predictions to modify PWM signals to the motor. Using the intelligent thermal feedback framework, we were able to control the operating temperature of our motor, even under varying load conditions and disturbance in the environment, as shown in

Table 10. Comparative motor temperature and control performance under intelligent thermal feedback (mean ± SD, 5 independent trials).

Scenario	Predicted Temp (°C)	Measured Temp (°C)	PWM Duty Cycle (%)	Deviation (°C)	Remarks
Nominal Load	65.2 ± 0.8	65.5 ± 0.9	75 ± 2	0.3 ± 0.1	Stable performance
Abrupt Load Increase	68.4 ± 1.1	68.8 ± 1.2	80 ± 3	0.4 ± 0.2	Quick PID adjustment
Environmental Disturbance	66.0 ± 0.9	66.2 ± 1.0	78 ± 2	0.2 ± 0.1	Robust ML prediction
Motor Aging Simulation	67.5 ± 1.0	67.8 ± 1.1	79 ± 3	0.3 ± 0.1	Minor deviation observed

.

5. Discussion and Limitations

5.1. Discussion

The findings of this study show significant evidence of improved thermal management of electric motors in embedded robotic systems. The intelligent thermal feedback system that was proposed is capable of predicting motor temperatures in real time using lightweight machine learning models that predict with high accuracy while imposing small computing restrictions on the embedded application—an important requirement for embedded applications. Experimental front-end evaluations of the thermal feedback system demonstrated up to an 18% reduction in thermal-related emergency shutdowns and an approximately 22% improvement in the operational stability and efficiency of the motors, which supports its effectiveness.

The proposed method differs from traditional cooling methods, like passive heat sinks or active fans [27], by facilitating adaptive, predictive control without imposing significant additional weight or energy burdens, fitting most closely for mobile robots and drones. Most traditional embedded motor control architectures in autonomous vehicles do not utilize any real-time thermal feedback extensively, creating misvalued operations and potential damage to the motor [28,29]. We devise a strategy to fuse temperature predictions into the control loop to mitigate the issues described above and create protection for more resilient, energy-efficient operation.

Recent work in thermal forecasting with machine learning is promising for improving motor safety and durability [30,31]; however, the majority of thermal forecasting models are still too computationally intensive for constrained-resource embedded platforms [32,33,34]. We applied quantized and pruned neural networks to find a good balance between predictive accuracy and hardware performance, which is in line with current trends in AI being deployed at the edge. Lastly, the use of adaptive control techniques [35,36] and fault-tolerant designs [37,38,39,40] allows us to maintain acceptable performance with variable loads and thermal loads.

The intelligent thermal feedback facilitates dynamic fine-tuning of motor activity based on real-time temperature fluctuations, minimizing overheating risk and prolonging components’ lifetime. Overall, the intelligent thermal feedback system also proves to consume less energy, resulting in environmentally sustainable operation. Relative to previous work, our framework achieves a positive balance between low-latency control and a high predictive capability, which is vital for utilization in real-world robotic platforms.

Despite these advancements, challenges remain, including scalability to multi-motor settings and highly dynamic operational environments. Future work will shift the focus to extending the framework to these settings, as well considering other lightweight AI architectures as a means to improve predictive performance and energy efficiency. Overall, this study offers a practical and deployable solution to intelligent thermal management in embedded robotics, providing a way to bridge predictive capability, computational efficiency, and real-world applicability.

5.2. Limitations

The proposed system has several limitations to be improved upon, notwithstanding its good potential:

• Generalizability of the ML Model: The model itself was trained on a certain dataset, under certain operational conditions. Sudden load changes, unexpected environmental disturbances, or aging of the motor that were not captured in the training process can hinder predictability performance. The framework does limit the impact of these issues through adaptive feedback control that adjusts the PWM signals dynamically based on temperatures collected in real-time. In addition to feedback control, we will implement some level of model re-calibration over time and employ strategies that combine online learning and anomaly detection—all to continue to support robustness to unexpected operational conditions.
• Limited Sensor Modalities: The current system mainly uses electrical and temperature data. Adding other sensor modalities, like vibration, acoustic emissions, and environmental measurements (e.g., humidity), could improve early fault detection capabilities and thermal prediction accuracy. The different signals would provide additional information about motor health and operating conditions and allow the ML model to better detect anomalies and failures.
• Reliable and Timely Communication: The adaptive controller model relies on the lowest possible latencies between actuators, sensor nodes, and the deep controller. In practice, factors such as delays in communication, noise on the sensor readings, and mechanical lag in actuation will affect the responsiveness of the system and could affect thermal regulation and motor protection. To deal with undesired delays, the proposed framework builds in filtering, discrepancies, and contingency redundancy on important feedback loops. Future work will focus on multi-parameter testing under realistic operating conditions, which include varying delays and disturbances to ensure reliable thermal control.
• Hardware and Integration Limitations: The thermal management techniques discussed herein, including actively cooled motors and smart feedback control, are not universally applicable to all categories of motors or robotic platforms. In particular, low-power systems or resource-limited robotic platforms will likely not have the computational capacity or energy reserves required to implement effective active cooling or online feedback control strategies. In these cases, the execution of active cooling and feedback control will more than likely hinder the performance of the system or could even inhibit the ability to implement the active could or feedback control strategies. Thus, the proposed framework is most suited for embedded robotic systems that possess the computational capacity and energy to deploy predictive thermal management.
• Experimental Validation Scope: All but a few of the validations were performed in controlled and static lab conditions, which may not reproduce the depth and richness of actual robot settings. Assessing the system’s robustness, flexibility, and reliability in the long term will require repeated long-term field-testing in an environment of uncertain and dynamic conditions.
• Deployment on Resource-Constrained Platforms: As a framework, the model may be limited when deployed in small or low-power embedded systems. The underlying computations, memory, and energy constraints will ultimately constrain the complexity of the machine learning model, which can be run in real time. There is a trade-off between the prediction accuracy and the responsiveness of a model, so in order to achieve not only reliable thermal control in real time but also acceptable predictive performance, lightweight models, edge computing enhancements, and selective sensors will be required.

6. Conclusions and Future Work

This study proposed an intelligent feedback control model for electric motors in autonomous robotic systems. An adaptive motor control system was integrated with a real-time temperature prediction model using machine learning. The thermal feedback control model can take advantage of controlled thermal stress while delivering optimal performance in real time. The experimental results yielded improvements regarding power efficiency, temperature control, and adaptability to change across multiple load conditions. The intelligent control system provides overall safety and reliability to robotic platforms in dynamic environments, as well as an extension to the motor life-time.

Future research is intended to focus on a number of avenues. The introduction of deeper learning models may improve the prediction of temperature during complex and rapidly changing conditions. Including additional sensor modalities, such as vibration and acoustic signals, opens up opportunities for predictive maintenance and improved understanding of motor health. Further extending the architecture to include multi-motor coordination may also allow for more efficient, collaborative operations in multi-robot systems and, ultimately, greater embodied autonomous applications.

Additionally, when deploying the model on low-power or small-scale embedded platforms, there will be trade-offs between model complexity and real-time performance. Lightweight models, model pruning, edge computing, and selective sensor usage will be considered to achieve thermal control in a timely manner while also being predictive. Finally, extensive long-term field-testing across various robotic missions and dynamic environmental conditions will be used to evaluate the framework in terms of reliability, adaptability, and maintenance benefits during real-world implementations, which will lead to further improvements to the intelligent thermal feedback system.