This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes.

Trajectory tracking control for wheeled mobile robots (WMRs) faces significant challenges in real-world applications due to actuator faults, longitudinal and lateral slippage. This study proposes an innovative dual-loop control structure combining adaptive sliding mode control (ASMC) and backstepping control (BC), supported by robust fault observers, to address these challenges. The dynamic loop employs ASMC to handle model uncertainties and disturbances, while the kinematic loop integrates BC with fault information provided by the observers, enabling real-time error compensation. Simulation results show that the proposed method significantly reduces tracking errors and improves stabilization time compared to traditional SMC and ASMC controllers. The system exhibits enhanced fault tolerance and disturbance rejection, maintaining stability under both normal and faulty conditions. The effectiveness of this approach is demonstrated through simulations and theoretical analysis, ensuring system stability using Lyapunov stability theory. The proposed method enhances robustness, adaptability, and stability of WMRs, contributing significantly to the field of mobile robotics under adverse conditions.

Ensuring the stability and accuracy of the output voltage in DC/DC buck converters (DBCs) is critical for reliable operation. This paper investigates an observer-based adaptive command-filtered controller designed for DBC systems subject to lumped disturbances. First, a mathematical model of the system is developed on the basis of switching modes. Then, a simplified extended state observer (SESO) is elaborated to mitigate the effects of lumped disturbances. A command filter technique with an integrated adaptive law is subsequently synthesized to enhance output voltage regulation. The stability of the observer and DBC control system is rigorously certified using the Lyapunov principle. Finally, simulation and experimental approaches are exploited to confirm the validity of the proposed method. Compared to state-of-the-art approaches, the proposed observer-based adaptive command-filtered controller improves tracking performance by 96.1% and 77.8% in simulations and 84.4% and 49.1% in experiments under a sinusoidal reference trajectory.

This paper addresses the problem of unexpected errors, such as model uncertainties and actuator faults, that degrade the performance of wheeled mobile robots (WMR). To overcome these challenges, a fault-tolerant control (FTC) approach is developed in which model-free control (MFC) is merged with an intelligent-proportional integral derivative (i-PID) controller and complemented by a fault observer (FO). Unlike existing approaches, the proposed controller does not rely on accurate system modeling; instead, MFC ensures robustness to time-varying parameters, and while i-PID enhances trajectory tracking through adaptive gain adjustment. The FO estimates actuator faults in real time and compensates for their effects, ensuring reliable operation even under severe conditions. The closed-loop stability is rigorously analyzed via Lyapunov theory. MATLAB/Simulink results show reduced tracking errors, improved stability, and strong robustness under both model uncertainties and actuator faults, including time-varying mass and inertia, validating the effectiveness and practical potential of the proposed FTC.

A direct current (DC) motor is used for automotive electronic throttle valves (AETV) to adjust incoming air into the engine’s combustion system, which has many advantages such as smooth, fast response, and simplicity. However, high-accuracy tracking control for AETV faces various obstacles because of the nonlinear features, hard identification, and noise. In this paper, a model of the AETV with four states in the form of a state space is developed. Then, a Kalman filter is formulated to eliminate the impact of measurement noise. The Kalman filter gain is obtained via the solve the linear quadratic gaussian (LQG) equation. Next, the optimal control based linear quadratic regulation (LQR) and Kalman filter are presented in which the control gain is constructed by the Riccati equation with the assistance of MATLAB/Simulink software. Finally, simulation studies are conducted to demonstrate the efficiency of the suggested method for the AETV system with other control strategies.