Search Results (420)

In this paper, a novel robust optimal control strategy is proposed for permanent magnet synchronous motors (PMSMs), simultaneously addressing two critical challenges in speed regulation: flux linkage degradation during long-term operation and abrupt load torque variations. The robust optimal control strategy is implemented through a combination of feedforward control and feedback control. A novel Variable-Gain Proportional Disturbance Observer (VGPDO) is proposed to simultaneously estimate time-varying flux linkage and torque disturbances in PMSM systems. The estimated disturbances are then compensated via a feedforward control loop, significantly improving the system’s robustness against parameter variations and external load changes. An optimal controller based on an actor-critic neural network provides feedback for optimal control performance. The uniform ultimate boundedness (UUB) of the proposed strategy is proved through Lyapunov stability analysis, and comprehensive simulation studies demonstrate the efficacy of both the proposed VGPDO and the proposed robust optimal control strategy. Full article

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured accurately under high-frequency swinging conditions. Therefore, the problem of the dynamic detection accuracy of the starting torque of the bearing on a high-frequency swinging friction and wear tester was proposed to be investigated in this paper, and a dynamic simulation model of the swinging system of the tester was constructed. With the combination of the inertia torque test and the least square method, a mathematical model of the inertia torque was developed and the influence of the inertia torque on the results of the dynamic detection of the starting torque was revealed. At the same time, an error compensation procedure for the on-line dynamic detection of the starting torque was written. This research shows that the inertia torque of the swing system of the tester has a great influence on the detection accuracy of the starting torque. As the swing frequency increases, the inertia torque increases, and the dynamic detection accuracy of the starting torque is reduced. The dynamic detection error of the starting torque of the bearing can be efficiently compensated by the error compensation procedure, and then the detection accuracy can be improved. This research provides a good theory for the design of SSPBs and the reasonable control of the starting torque during the use of the bearings, and it is valuable for engineering practice. Full article

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations. Full article

This paper presents a coordinated control strategy for an electro-hydraulic composite braking system in in-wheel motor electric vehicles to enhance regenerative energy recovery and braking safety. A novel hydraulic control unit (HCU) without a pressure-reducing valve is designed to simplify structure and maximize energy utilization. Based on the ideal braking force distribution, a force allocation strategy coordinates motor and hydraulic braking across modes, ensuring motor torque can compensate total braking torque when wheel lock occurs. An anti-lock braking (ABS) strategy relying solely on motor torque adjustment is proposed, keeping hydraulic torque constant while rapidly stabilizing slip within 13–17%, thereby avoiding interference between hydraulic and motor braking. A joint Simulink–AMESim–CarSim platform evaluates the strategy under varying conditions, and real-vehicle tests in regenerative mode confirm feasibility and smooth switching. Results show the proposed approach achieves target braking intensity, improves energy recovery, reduces torque oscillations and valve actions, and maintains stability. The study offers a practical solution for integrating regenerative braking and ABS in in-wheel motor EVs, with potential for hardware-in-the-loop validation and advanced stability control applications. Full article

For an induction motor operating as a symmetric three-phase system, the performance of indirect field-oriented vector control relies heavily on the accuracy of the rotor time constant. Any inaccuracies result in severe torque errors and compromise dynamic performance because of the coupling between the flux and torque controls. Although conventional IFOC methods are intended to compensate for the rotor time constant error, they rely on induction machine parameters such as the mutual and leakage inductances. This paper proposes an online method for tuning the rotor time constant independent of other parameters. First, an active power model of three-phase symmetric induction motor is selected to estimate the stator resistance based on a model reference adaptive system, which requires only the rotor time constant. Additionally, high-frequency current injection and torque ripple estimation without phase delay or amplitude decay are introduced to compensate for the rotor time constant. When a high-frequency current is injected, the rotor time constant and stator resistance can be simultaneously tuned without depending on other parameters. A high-frequency current is injected only when a rotor time constant error is detected from the estimated stator resistance. This behavior is enabled by the correlation between the stator resistance and the rotor time constant. Simulation results using MATLAB/Simulink regarding the symmetric three-phase induction motor validate the proposed method. Full article

For the combination of finite elements and control circuits, the calculation is complex and time-consuming, making direct optimization impractical. In this paper, a new distributed node and magnetic circuit model is proposed to simulate the spatial and temporal variation of the distributed air-gap magnetic density with the current and rotor angle and solve the electromagnetic force wave variation. Compared to other distributed flux-linkage models, the proposed model not only considers the radial magnetic path but also connects adjacent magnetic paths tangentially. The inclusion of this tangential path enhances the mutual interaction between magnetic circuits, leading to a more accurate model. Based on the control circuit model, the electromagnetic force wave changes caused by the harmonic currents under various circuits and operating conditions are calculated, the topology is analyzed and optimized to mitigate critical harmonics, the electromagnetic force wave is reduced, and finally, the model accuracy is verified experimentally. While most distributed flux-linkage models are applied to the optimization of motor performance metrics such as the magnetomotive force (MMF), power, and torque, this paper applies the model to the optimization of the magnetic field strength, the harmonic content, and the corresponding noise, vibration, and harshness (NVH), demonstrating a broader range of applications. This method can be coupled with the control circuit to analyze the changes in electromagnetic force waves and quickly optimize them, improving the accuracy and efficiency of research and development. Full article

Natural and unconstrained locomotion remains a fundamental challenge in creating truly immersive virtual reality (VR) experiences. This paper presents the design and control of a novel robotic omnidirectional treadmill (ODT) based on the bilateral symmetry of two cooperative five-bar planar mechanisms designed to replicate realistic walking mechanics. The central contribution is a human in the loop control strategy designed to achieve stable walking in place. This framework employs a specific control strategy that actively repositions the footplates along a dynamically defined ‘Line of Movement’ (LoM), compensating for the user’s motion to ensure the midpoint between the feet remains stabilized and symmetrical at the platform’s geometric center. A comprehensive dynamic model of both the ODT and a coupled humanoid robot was developed to validate the system. Numerical simulations demonstrate robust performance across various gaits, including turning and catwalks, maintaining the user’s locomotion center with a maximum resultant drift error of 11.65 cm, a peak value that occurred momentarily during a turning motion and remained well within the ODT’s safe operational boundaries, with peak errors along any single axis remaining below 9 cm. The system operated with notable efficiency, requiring RMS torques below 22 Nm for the primary actuators. This work establishes a viable dynamic and control architecture for foot-tracking ODTs, paving the way for future enhancements such as haptic terrain feedback and elevation simulation. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

The conventional sliding mode control (SMC) strategy for direct torque control of switched reluctance motors suffers from severe chattering and prolonged dynamic response. Accordingly, an enhanced SMC strategy is proposed to mitigate motor chattering and suppress torque ripple. On the basis of the conventional exponential approximation rate, a compensation factor and a fractional order are incorporated. Meanwhile, the sigmoid function, characterized by superior smoothness, is employed to replace the sign function that induces severe chattering, thereby attenuating the motor torque ripple. At the same time, in response to the challenge of parameter tuning arising from motor nonlinearity and the abundance of parameters, the sparrow search algorithm (SSA) is employed to optimize the controller parameters. The motor control models before and after the improvement are constructed in MATLAB/Simulink, and the sparrow search algorithm (SSA) is employed to optimize the controller parameters for both cases. Comparative results indicate that the improved control strategy and parameter optimization method can effectively suppress motor torque ripple and enhance the dynamic response characteristics of the system under various operating conditions and rotational speeds. Full article

To improve the reliability and fault tolerance of electric helicopter propulsion systems, this paper presents a master–slave fault-tolerant control method based on an extended state observer (ESO) for dual-redundant electric propulsion systems that addresses dynamic coupling disturbances. First, the control architecture puts the master motor in speed loop mode and puts the slave motor in torque loop mode with an ESO to estimate disturbances and compensate for mechanical coupling torque through feedforward control based on Lyapunov stability theory. Second, a least squares parameter identification method establishes a current-torque mapping model to ensure consistent dual-motor output. Then, fault-tolerant switching is implemented, transitioning from normal torque mode coordination to independent speed mode with adaptive PI adjustment during faults. Experimental validation shows that the total torque stabilizes at 240 N·m, and the synchronization error remains within ±0.5 N·m during normal operation. Under single-motor fault scenarios, the ESO detects disturbances within 15 ms with >95% accuracy. The system speed decreases to a minimum of 2280 rpm (5% deviation) and recovers within 3.5 s. Compared to traditional PI control, this method improves torque synchronization by 65.4%, speed stability by 62.6%, and dynamic response by 51.2%. Finally, the results validate that the method effectively suppresses coupling interference and meets aviation safety standards, providing reliable, fault-tolerant solutions for electric helicopter propulsion. Full article

In order to enhance the tracking accuracy and disturbance rejection capability in the speed loop of an optoelectronic tracking servo control system, a parameter self-adjusting disturbance rejection generalized predictive control method (STGPC) based on a continuous-time model is proposed in this paper. First, a dynamic model of the servo turntable system is established, and a linear extended state observer (LESO) is designed to perform real-time estimation of internal and external disturbances in the system. Second, a generalized predictive control law incorporating the predictive model, performance metrics, and rolling optimization is systematically derived, where the reference trajectory is generated by a tracking differentiator and the system state is provided in real time by the LESO. Furthermore, a gradient descent method is innovatively introduced to achieve adaptive adjustment in the predictive time domain, and the stability of the closed-loop system is rigorously proven based on Lyapunov theory. Finally, simulation experiments were conducted to verify the tracking performance, disturbance rejection performance, and time-domain parameter self-adjustment effects of the control method. Simulation results show that compared with PID control and traditional linear generalized predictive control (LGPC), the proposed STGPC method reduces speed tracking residuals by 73.79% and 51.04%, respectively, enhances disturbance suppression capability for speed vibration disturbances by 50.55% and 47.55%, respectively, and enhances compensation capability for friction torque disturbances by 68.03% and 59.33%, respectively. The system demonstrates outstanding velocity tracking accuracy and disturbance rejection while exhibiting good robustness against system parameter perturbations. Full article

Multiphase electric drives (EDs) offer important advantages for high-demand applications. However, they require appropriate high-performance control strategies. In this context, finite-control-set model predictive control (FCS-MPC) emerges as a promising strategy, offering a notable flexibility to implement multiobjective regulation schemes. When applied to multiphase EDs, standard FCS-MPC exhibits degraded current quality at low and medium control frequencies. Multivector solutions address this issue by properly combining multiple voltage vectors within a single control period to create the so-called virtual voltage vectors (VVVs). In this way, this approach achieves flux and torque regulation while minimizing current injection into the secondary subspace. For this purpose, the VVV synthesis typically prioritizes active vectors with low contribution in secondary subspaces, avoiding the average deception phenomenon. VVV solutions commonly enable an open-loop regulation of secondary currents. Nevertheless, the absence of closed-loop control in the secondary subspace hinders the compensation of nonlinearities, machine asymmetries, and unbalanced conditions in the ED. Considering this scenario, this work implements a multivector FCS-MPC recovering closed-loop control for the secondary subspace. The capability of the proposal to mitigate secondary current injection and compensate for possible dissymmetries is experimentally evaluated in a six-phase ED. Its performance is compared against a benchmark technique in which secondary current regulation is handled in open-loop mode. The proposed control solution significantly improves in current quality, achieving a reduction in harmonic distortion of 54% at medium speed. Full article

To accommodate the contact force between a robot and its environment, this paper presents an adaptive control framework for the impedance control of a manipulator with time delay estimation (TDE). To simplify the complex system model and yield adaptive feedback compensation, a voltage-based motor control approach was presented. Compared to the torque-based control model, the voltage-based control model is computationally more efficient and practically feasible. The proposed adaptive law was designed to compensate for the errors produced by the TDE. Through a stability analysis, the control framework was verified by semi-global uniform ultimate boundedness (SGUUB) stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. Full article

Control valves are widely applied in nuclear power, offshore oil/gas extraction, and chemical engineering, but suffer from issues like pressure oscillation, flow control accuracy degradation, and motor overload due to unstable fluid loads (e.g., nuclear reactions in power plants and complex marine climates). This paper proposes a dual-motor redundant compensation method to address these challenges. The core lies in a control strategy where a single main motor drives the valve under normal conditions, while a redundant motor intervenes when load torque exceeds a preset threshold—calculated via the valve core’s fluid load model. By introducing excess load torque as positive feedback to the current loop, the method coordinates torque output between the two motors. AMESim and Matlab/Simulink joint simulations compare single-motor non-compensation, single-motor compensation, and dual-motor schemes. Results show that under inlet pressure step changes, the dual-motor compensation scheme shortens the stabilization time of the valve’s controlled variable by 40%, reduces overshoot by 65%, and decreases motor torque fluctuation by 50%. This redundant design enhances fault tolerance, providing a novel approach for reliability enhancement of deep-sea oil/gas control valves. Full article

This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic conditions. The main novelty is the integration of neural learning into the passivity framework, enabling real-time compensation for un-modeled dynamics and parameter uncertainties with only one gain adjustment across a broad speed range. Lyapunov-based analysis guarantees the global stability of the closed-loop system. Experiments on a 1.1 kW induction motor confirm the approach’s effectiveness over conventional observer-based and fuzzy-enhanced methods. Under torque reversal and flux variation, the proposed controller achieves a torque mean absolute error (MAE) of 0.18 Nm and flux MAE of 0.21 Wb, compared to 1.58 Nm and 0.85 Wb with classical PBC. When peak torque deviation drops from 42.52% to 30.85% of the nominal, torque symmetric mean absolute percentage error (SMAPE) improves by 7.6%, and settling time is reduced to 985 ms versus 1120 ms. These results validate the controller’s precision, adaptability, and robustness in real-world sensorless motor control. Full article