Search Results (194)

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

This study proposes a geometric procedure for robust controller tuning under parametric uncertainty, based on root-contour analysis of the closed-loop control system. For a fixed candidate controller tuning, the set of possible pole locations induced by the admissible variations of the control plant parameters is constructed. Robust admissibility is formulated as a geometric set-inclusion problem, requiring this set to remain inside a prescribed dynamic performance region in the complex s-plane. A distinction is introduced between nominal admissibility, robust stability, and robust admissibility, showing that stability over the entire uncertainty set is not sufficient to guarantee the desired dynamic performance. To quantify the root contours, several indices are defined, including the dispersion along the real and imaginary axes, the maximum pole displacement with respect to the nominal pole locations, and the geometric margin to the boundary of the performance region. The procedure is applied to the selection and verification of PI controller tunings for an uncertain single-input–single-output (SISO) control system and is further validated through examples with different structures of parametric uncertainty, including a system with a single uncertain parameter and a PID-controlled system with several uncertain control plant parameters. The results show that root-contour analysis can distinguish tunings that are only robustly stable from tunings that preserve the prescribed dynamic performance over the entire uncertainty set. Thus, the method can be used as a practical tool for the diagnosis, comparison, and selection of controller tunings under parametric uncertainty. Full article

Reliable load frequency control (LFC) in interconnected hybrid power systems remains challenging in the presence of nonlinear operating conditions, random demand variations, and parametric uncertainty. This study proposes a PSO-based LFC framework for a two-area hybrid power system and examines its performance through two successive stages. In the first stage, a Particle Swarm Optimization (PSO)-tuned Fuzzy PID controller is developed and benchmarked against reported Fuzzy-PIDF schemes optimized by MPA and COR. In the second stage, three PSO-tuned hybrid fuzzy structures, namely Fuzzy PI-PD + PID, Fuzzy (PI + PD) + PID, and Fuzzy PI + Fuzzy PD + PID, are formulated and comparatively assessed under identical operating conditions. The examined cases include nominal linear operation, Governor Dead Band (GDB) and Generation Rate Constraint (GRC) nonlinearities, random load disturbance, and seven parametric uncertainty scenarios. In the first stage, the PSO-tuned Fuzzy PID controller attains an ITAE of 0.00003433 under linear conditions and 0.00003822 under GDB/GRC nonlinearities, while yielding lower cumulative error than the benchmark controllers. In the second stage, the Fuzzy PI-PD + PID structure records the lowest ITAE and the shortest settling time, with ITAE = 0.000003655 and ST = 0.4234 s under nominal conditions, and ITAE = 0.000004063 and ST = 0.4519 s under nonlinear conditions. Under parametric uncertainty, its ITAE ranges from 2.482 × 10⁻⁶ to 4.833 × 10⁻⁶ with the nominal gains retained. Overall, the results indicate that the proposed PSO-based framework provides improved LFC performance within the examined linear, nonlinear, random-disturbance, and parametric-uncertainty scenarios for the studied two-area hybrid power system. Full article

Two-wheeled self-balancing robots exhibit nonlinear and inherently unstable dynamics due to their inverted-pendulum structure, making control design challenging under terrain variations and external disturbances. This paper proposes a hybrid master–slave fuzzy cascade controller with an additional wheel-synchronization loop to improve tracking performance and robustness. The architecture combines a master velocity PI loop with fuzzy-tuned integral action and a slave balance PD loop with fuzzy proportional control, while a differential synchronization mechanism compensates for motor mismatches without affecting the global balance dynamics. Local stability is analyzed through linearization and equivalent gain approximation within a sector-bounded framework. Experimental validation was conducted on an ESP32-based TWSBR under flat, uphill, and downhill conditions at reference velocities of $0.15$, $0.20$, and $0.30{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, including payload tests with additional masses of $0.279$ and $0.375\mathrm{kg}$. For each scenario, 30 independent trials were performed to compute the reported metrics. Compared with a conventional PID controller, the proposed strategy reduced the flat-terrain velocity RMSE from $0.0108$ to $0.0057{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, while also improving angular stabilization and robustness under slope and payload disturbances. Full article

Rotary direct-drive pressure valves (RDDPVs) have attracted increasing attention in aircraft braking systems because of their compact structure, reduced weight, and improved anti-contamination capability. However, the pressure regulation of RDDPVs is challenging due to the coupled dynamics among the limited-angle torque motor, eccentric spool motion, steady-state flow force, and load pressure. Existing pressure control methods for RDDPVs are still mainly based on linear controllers such as PI/PID controllers, which do not explicitly compensate for the nonlinear characteristics of the valve. To address this problem, this paper investigates the application of a model-based backstepping control strategy to RDDPV pressure regulation. First, a nonlinear dynamic model of the RDDPV is established, and a nonlinear state-space representation is derived for controller design. Based on this model, a backstepping pressure controller is developed, in which the nonlinear model information is used for feedforward compensation. Dynamic surface control is introduced to avoid direct differentiation of the virtual control signals. Lyapunov analysis shows that the closed-loop tracking errors are uniformly ultimately bounded under bounded disturbances. Comparative simulations are conducted under different reference pressure trajectories and randomized model parameter and disturbance conditions. The simulation results indicate that the proposed controller achieves better tracking performance than the controller without detailed model compensation and the conventional PI controller under the tested operating conditions. This study provides an initial simulation-based exploration of model-based nonlinear pressure control for RDDPVs. Full article

This paper presents an experimental comparison of proportional–integral (PI) and proportional–integral–derivative (PID) controllers for excitation regulation in a 1.5 kW synchronous condenser. The excitation current was controlled using a PWM-based converter driven by an ESP32 microcontroller, with reactive power feedback. Both controllers were tested across multiple reactive power setpoints to evaluate settling time and steady-state accuracy performance. The results show that both achieved steady-state errors within ±5% of the reference. The PI controller provided faster settling, while the PID controller offered smoother but slower responses due to feedback bandwidth limitations. The findings confirm that PI control is an effective and low-computational-cost solution for embedded excitation systems. Full article

Controller performance is strongly influenced by its parameters. Estimating these parameters requires an effective estimation approach for obtaining the best possible response. This study proposes a novel methodology for the estimation of controller parameters, utilizing the dream optimization algorithm (DOA) and a new objective function. The proposed method is employed to determine the optimal parameters of various PID controllers used in the automatic voltage regulator (AVR) system. Thus, the suggested objective function consists of transient response metrics and the stability index “integral of time-weighted absolute error ($ITAE$)”. Three different PID controllers are used, which are cascaded PIPD with filter (CPIPDF), cascaded fractional-order PI fractional-order PDF (CFOPIFOPDF), and PIDF. The DOA’s performance is compared with famous and recent optimizers and shows more reliable performance. For example, based on the statistical analysis, the DOA obtained a standard deviation of 0.0042, while the closest competitor obtained 0.0089. Furthermore, the CPIPDF, CFOPIFOPDF, and PIDF controllers are compared under a wide variety of operating conditions. Based on ITAE, the CPIPDF controller achieved lower values than the CFOPIFOPDF and PIDF controllers. Also, the results show that the CPIPDF controller achieves better performance than other published controllers. For instance, the CPIPDF controller improves AVR performance by approximately 45.3% compared to the fireworks whale optimization algorithm-based PIDD² controller in the case of varying load condition impact. Moreover, scenarios that remain insufficiently addressed in the literature, such as communication delays, restricted excitation voltages, and external disturbances, are considered. Full article

Although PI and PID controllers are mature control laws, their effect on energy-related variables is rarely isolated in a complete electric vehicle traction model when the plant, controller tuning basis and driving conditions are kept unchanged. A full-system MATLAB/Simulink model was developed, comprising a DC motor with PWM H-bridge, reduction gear, vehicle dynamics and a lithium-ion battery with SOC monitoring. Fixed-gain PI and PID configurations were compared under FTP75, with US06 added as a dynamic-cycle assessment. Speed tracking was evaluated using RMSE, MAE, IAE and ITAE, while energy behavior was assessed through SOC depletion, battery voltage, current and braking-command signals. Under FTP75, both controllers achieved nearly identical tracking accuracy, with an overall RMSE of 0.1525 km/h across the active intervals. Despite this kinematic equivalence, PID reduced SOC depletion by 0.980 percentage points over 4.963 km and produced a less intense but more distributed braking command. The additional 600 s US06 simulation did not confirm a general PID advantage: both controllers reached the same maximum speed and showed practically identical tracking accuracy, while PID did not reduce SOC depletion. The results show that the derivative channel changes the control-command pattern, but it does not automatically improve kinematic or energy performance under fixed-gain tuning. Full article

High penetration of converter-based wind generation reduces system inertia. It poses challenges to frequency stability in modern distribution networks, particularly in doubly fed induction generator (DFIG)-based wind-energy-conversion systems (WECSs), where frequency regulation is coupled with point-of-common-coupling (PCC) voltage and power factor (PF) dynamics. This study presents a multi-objective comparative evaluation of proportional–integral (PI), proportional–integral–derivative (PID), fractional-order PID (FOPID), and adaptive neuro-fuzzy inference system (ANFIS) controllers for a DFIG-based WECS connected to a radial distribution feeder. Controller parameters are tuned using multi-objective optimisation, considering frequency deviation, overshoot, settling time, disturbance robustness, control smoothness, and computational cost, while maintaining PCC voltage and PF within acceptable limits. MATLAB/Simulink simulations are conducted under turbulent wind conditions, load variations, voltage disturbances, and measurement noise. The results indicate that conventional PI and PID controllers exhibit limited performance under low-inertia conditions, whereas FOPID improves damping and voltage/PF behaviour. ANFIS achieves the best overall performance, providing reduced frequency deviation, faster settling time (below 3 s), improved disturbance rejection, and significantly lower integral absolute error (up to ~90%) compared to PI control. These findings offer practical guidance for selecting and tuning controllers to enhance frequency-centric stability in wind-integrated distribution networks. Full article

Fractional-order controllers have emerged as robust alternatives to conventional PID controllers. Existing tuning methods generally focus solely on robustness to process gain variations. This paper introduces a design method for fractional-order PI controllers, specifically resilient to time constant changes by shaping the loop frequency response. This work simplifies the design method by replacing the separate magnitude and phase derivative calculations used in prior techniques with a unified, single partial derivative approach. Instead of using cumbersome optimization routines and graphical analysis used in existing fractional-order controller tuning methods, the proposed approach uses a direct, simple, and efficient 1-step algorithm. Numerical simulations for lag- and delay-dominant processes are included to highlight the efficiency of the proposed approach. Traditional integer order controllers are designed for comparative purposes. The proposed approach achieves a constant overshoot despite time constant variations, an advantage compared to classical controllers. Full article

This study addresses pressure regulation in an induction-motor-driven centrifugal fan and introduces two complementary novelties: a Softsign-PI controller that shapes the tracking error via a Softsign nonlinearity before PI regulation and a hybrid starfish optimization with a differential evolution (hSFOA-DE) scheme for automatically tuning the controller parameters. The approach is evaluated on an experimentally validated nonlinear fan–motor model and benchmarked against modern metaheuristics—starfish optimization algorithm (SFOA), animated oat optimization (AOO), electric eel foraging optimization (EEFO), differential evolution (DE), particle swarm optimization (PSO)—as well as classical tunings—Murrill-based 2-DOF PID, Tyreus–Luyben PID and Ziegler–Nichols PI. Statistical summaries and boxplots indicate superior central tendency with reduced run-to-run variability; fitness–evolution curves show faster convergence; and time-domain performance metrics confirm improved transient and steady-state behaviour. Objective function comparisons further show the lowest values of both the Zwe-Lee Gaing (ZLG) and integral of absolute error (IAE), supporting advantages in robustness and tracking accuracy of the proposed approach. These gains reduce overshoot and cumulative error, which can lessen throttling losses and actuator duty in fan/pump service, suggesting potential energy and maintenance benefits. Full article

Accurate and robust control of robotic joints is essential for high-performance robotic systems. However, conventional proportional–integral–derivative (PID) controllers suffer from limited adaptability when applied to brushless direct current (BLDC) motor-driven joints operating under nonlinear and time-varying conditions. To address this issue, this paper proposes a Radial Basis Function (RBF) neural network-enhanced self-tuning PID control strategy. The RBF neural network serves as an online identifier to approximate the nonlinear dynamics of the BLDC motor and to estimate the system Jacobian online. Based on the estimated Jacobian, the PID gains (K_p, K_i, and K_d) are adaptively updated using a gradient descent mechanism, enabling continuous adjustment to varying operating conditions. Simulation and experimental results demonstrate that the proposed method achieves negligible overshoot, faster settling performance, and improved steady-state accuracy compared with conventional PID and PI controllers. In addition, the proposed controller exhibits enhanced disturbance rejection capability and robust performance under abrupt speed variations and start–stop conditions. The proposed approach effectively combines the simplicity of PID control with the adaptability of neural networks, providing a practical and efficient solution for high-precision robotic joint control. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article

Modern oil refining is faced with the need to maximize raw material processing in the face of fierce competition and environmental requirements. Therefore, the fluid catalytic cracking (FCC) process, key to the production of high-octane gasoline, requires special attention to automation efficiency. Maintaining optimal reactor temperature is a complex scientific and technical challenge, the solution to which directly impacts the yield of target products and the service life of the catalyst. Existing automatic control systems often fail to cope with process transients, nonlinearities, and time delays, making the search for new control approaches highly relevant. The scientific significance of this study lies in the system analysis and quantitative comparison of the effectiveness of classical control laws (P, PI, PID) applied to a plant with a delay. For the first time, a rigorous comparative analysis of tuning methods—analytical (based on phase margin specifications) and automated (using the PID Tuner tool in MATLAB Simulink R2024b)—is performed for a plant characterized as a second-order system with time delay, formed by the series connection of two first-order lag elements with transport delay. The results contribute to automatic control theory by clearly demonstrating the limitations of the proportional controller and the insufficient speed of the integral controller, as well as confirming the hypothesis that a PID law is necessary to achieve a balance between accuracy and response speed under inertia conditions. The practical significance of the work is confirmed by the development of an optimized automatic temperature control system. Using the PID Tuner tool, we achieved critical industrial performance indicators: zero static error, minimal control time (44 s), and acceptable overshoot (9.6%). The system’s robustness (maintaining stability with changes in plant parameters by 30–40%) and its invariance to the main disturbance (catalyst temperature fluctuations), confirmed during simulation, guarantee the viability of the proposed solution under real-world production conditions. Implementation of such a controller will minimize deviations from the process conditions, leading to increased yield of light petroleum products and an extended service life of the expensive catalyst, providing direct economic benefits. Full article

BLDC motors are utilized in electric cars, robotics, drones, home appliances and medical equipment due to their effectiveness, dependability, and accurate control. PI controllers have been put forward to enhance the dynamic performance of brushless direct current (BLDC) motors, and they have been tested in many papers with various algorithms (such as PSO, GA, GWO, ACO and ABC) and strategies (such as PI/PID control, FOC, FLC, SMC and MPC). Meanwhile, in this research, and for the first time, the PI controller was tuned by the proposed Sailfish Optimization algorithm (SFO) with a direct torque control (DTC) strategy to enhance the dynamic performance of BLDC motors. Although DTC provides a very fast torque response, it still suffers from high torque ripple and noticeable instability at low speeds. These issues persist even when using conventional PI tuning or common optimization algorithms. Hence, in this research, we proposed an improved control strategy that combines DTC with PI tuning optimized by the Sailfish Optimization algorithm (SFO), which delivers smoother torque, more stable low-speed operation, and stronger robustness during sudden changes in load. In this regard, the PI controller was tested under different levels of torque and compared with the traditional Gray Wolf Optimization (GWO-PI) algorithm controller, as well as PI and PID controllers, and the performance of each of them was evaluated for different torque levels at speeds of 600 rpm and 2000 rpm during physical experiments. The simulation results showed that the Sailfish-PI controller, compared to the others, recorded the fastest response with a rise time of 2.1 ms and settling time of 2.9 ms under 2.39 Nm nominal torque at 2000 rpm speed; in addition, it continuously showed the lowest values of overshoot and undershoot as torque increased. It also maintained the most accurate and consistent performance, keeping the peak rpm almost flat and extremely near to the target of 2001 rpm. Therefore, in systems that require variable speed and torque while operating, such as electric automobiles, the proposed method is suitable for application. Full article