This paper introduces a robust ${H}_{{\infty }}$ adaptive fuzzy controller for a class of unknown nonlinear systems over network. There are two main problems in the networked control systems, the time-varying networked-induced delay and the data packet dropouts. The time-varying networked-induced delays cause degradation for the system performance that controlled over network and also the system can be unstable. Moreover, the delay problem is aggravated when packet losses occur during the transmission of data. The proposed controller has a filtered error to handle the networked-induced delays. Furthermore, it is robust to overcome some packet losses. The unknown nonlinear functions of the system are approximated using fuzzy logic systems. The robustness of the proposed controller is achieved by combining the adaptive fuzzy controller with ${H}_{{\infty }}$ control technique. The Lyapunov stability analysis is used to prove that the proposed controller is asymptotically stable. Stability of the whole closed loop system is guaranteed via the proposed controller in the presence of bounded external disturbance, data packet dropouts and time-varying networked-induced delays. An extensive example of an inverted pendulum system is studied in detail to verify the effectiveness of the proposed controller based on TrueTime toolbox with comparative results.

In this paper, an adaptive fuzzy predictive controller (AFPC) is designed and analyzed for a class of networked nonlinear systems with time-varying communication delay. The integration of the communication network in the control loops makes the nonlinear system has forward and feedback time varying delays. The proposed controller compensates the network-induced delays in the forward and feedback channels. The structure of the AFPC consists of adaptive fuzzy logic control (AFLC) with state predictor located at the controlled plant and a remote adaptive smith predictive controller at the master node. The AFLC and the state predictor are utilized to identify the dynamic of the time-varying delay-free nonlinear plant in order to cancel the nonlinear term for the nonlinear control system in a canonical form. In the remote controller, adaptive smith predictor is employed to compensate the time-varying delay effect to achieve the desired tracking performance. Based on Lyapunov theory, the stability of the closed loop system is guaranteed in the presence of bounded external disturbance and time varying delays. Simulated application of Van der Pol oscillator is provided to demonstrate the feasibility and effectiveness of the proposed scheme based on TrueTime toolbox.

The aim of this paper is to design a speed controller of a DC motor by selection of a PID parameters using genetic algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). DC motor could be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The model of a DC motor is considered as a third order system. And this paper compares three kinds of tuning methods of parameter for PID controller. One is the controller design by the Ziegler and Nichols, second is the controller design by the Genetic Algorithm method and third is the controller design by Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed methods could be applied to the higher order systems.

This paper presents an improved adaptive fuzzy logic speed controller for a DC motor, based on field programmable gate array (FPGA) hardware implementation. The developed controller includes an adaptive fuzzy logic control (AFLC) algorithm, which is designed and verified with a nonlinear model of DC motor. Then, it has been synthesised, functionally verified and implemented using Xilinx Integrated Software Environment (ISE) and Spartan-3E FPGA. The performance of this controller has been successfully validated with good tracking results under different operating conditions

Control systems for Ground Data Receiving Stations (GDRS) play a crucial role in accurately pointing antennas to receive data from satellites. This study focuses on designing and simulating a closed-loop control system using PID, LQR, and Fuzzy-PI controllers for precise antenna positioning and establishing reliable communication links during data receiving sessions. The proposed model incorporates a DC servo motor-based controller and antenna positioning system, implemented through MATLAB/SIMULINK software. Comparative analysis of the controllers is performed based on various performance metrics. The findings indicate that the Fuzzy-PI controller exhibits the most responsive and robust operation, outperforming the PID and LQR controllers. It effectively manages time delays in antenna control within the GDRS and supersedes the current controller with enhanced bypass, rise time, and tracking antenna calibration values. The study demonstrates the significance of optimizing control systems for ground stations, particularly when dealing with large motors, offering potential advancements in motor control and system stability. The proposed Fuzzy-PI controller presents a valuable solution for improving the overall efficiency of GDRS and enhancing its data reception capabilities.

This paper presents an adaptive fuzzy controller for a class of unknown nonlinear systems over network. The network-induced delays can degrade the performance of the networked control systems (NCSs) and also can destabilize the system. Moreover, the seriousness of the delay problem is aggravated when packet losses occur during a transmission of data. The proposed controller uses a filtered tracking error to cope the time-varying network-induced delays. It is also robust enough to cope some packet losses in the system. Fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions that appear in the tracking controller. Based on Lyapunov stability theory, the constructed controller is proved to be asymptotically stable. Stability of the adaptive fuzzy controller is guaranteed in the presence of bounded external disturbance, time-varying delays, and data packet dropouts. Simulated application of the inverted pendulum tracking illustrates the effectiveness of the proposed technique with comparative results.