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This paper presents the design of a hybrid intelligent fuzzy sliding controller (HIFSC) for a purely electric quarter vehicle (QEV) using a brushless DC (BLDC) motor and a PACEJKA tire model. The proposed system processes control signals to manage the QEV's dynamic longitudinal behavior. The BLDC motor and tire are modeled together to form an in-wheel motor system, which is inherently non-linear and subject to uncertainties. To address these challenges, an intelligent controller integrating sliding mode control (SMC) with fuzzy logic tuning is proposed. While SMC is effective in managing non-linearities, it is prone to chattering. The incorporation of fuzzy logic aims to mitigate this issue, ensuring stability and maintaining the sliding mode. The system and controller were simulated using MATLAB/Simulink. Simulation results demonstrate that the fuzzy sliding mode controller outperforms the conventional PI controller by reducing chattering and enhancing the system's sensitivity to external noise, without overshooting across various road conditions. Notably, the slip rate achieves a maximum of around 2.1% on wet roads.

This study presents a two‐level Quasi T‐S fuzzy (QTS) model to represent nonlinear networked control systems (NCS), which takes the performance of network with time‐delay and data packet dropouts into the investigations. At the working points of nonlinear NCS, regard the subsystem as linear system modelling by QTS, then the global model of nonlinear is fuzzy fused by linear subsystem. This study constructs the corresponding system model, in which the systemʹs stability is proved with linear matrix inequality (LMI). As a bench test a nonlinear system is selected to demonstrate the feasibility and efficiency though numerical analysis. This study provides a new method of fault diagnosis and tolerance control of nonlinear NCS.

This paper presents a new scheme for variable structure (VS) fuzzy PD controller. The rule base of the fuzzy PD controller is tuned online. The purpose of the proposed controller is to track accurately a preselected position command for the servomechanism system. Therefore, this study establishes a model using a black-box modeling approach; simulations were performed based on real-time data collected by LabVIEW and processed using MATLAB. The input signal for the servomechanism driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval. The candidate models were obtained using linear least squares, nonlinear least squares, and deep neural network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors. Then, the DNN identified model was used to design the proposed control techniques. A comparison had been executed between the VS fuzzy PD control, the conventional PD control, and the fixed structure fuzzy PD control. The experimental results confirm the proposed VS fuzzy PD control can absorb the nonlinear behavior of the system. The speed regulation test, it reduces the rise time from 50% to 56%. While continuously changing in speed, it has the smallest tracking error (0.412 inches).

This thesis investigates the feasibility of a proposed hybrid linear/Fuzzy controller for nonlinear plants. The proposed controller concept is based on the use of multiple linearizations of a nonlinear plant, which describe the dynamics of perturbations about equilibrium points throughout the desired envelope of operation. A bank of linear compensators is developed, each corresponding to a linearized plant about a different equilibrium. The multiple control signals generated by the bank of compensators are then weighted and summed using Fuzzy Logic to produce a composite control perturbation signal, which is used to drive the nonlinear plant. Experiments were conducted to test and refine this control approach. Analysis shows that a linear/Fuzzy compensator based only on a bank of linear compensators is not feasible, largely due to the small regions for which the linearized models were valid and energy considerations within the plant/controller system. The analysis itself, however, suggests an alternate form for a hybrid Linear/Fuzzy approach, based on a bank of Fuzzy compensators smoothed by a linear controller. This concept is developed into the Model-Based Fuzzy Logic Controller (MBFLC). The concept of Fuzzy Logic Model Following Control is also addressed as a second hybrid approach.

This paper presents a study of fuzzy optimal variance control problem for dynamical systems subject to actuator amplitude and rate constraints. Using Takagi-Sugeno fuzzy modeling and dynamic Parallel Distributed Compensation technique, the stability and the constraints can be cast as a multi-objective optimization problem in the form of Linear Matrix Inequalities. By utilizing the formulations and solutions for the input and output variance constraint problems, we develop a fuzzy full-state feedback controller. The stability and performance of the proposed controller is demonstrated through its application to the airfoil flutter suppression.

This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.

The fuzzy inverse model of control of discharge energy conversion was developed. The model is based on experimental data with fuzzy approximation. The fuzzy inverse model provides adaptability of control system under variable technological parameters and external conditions.