Model reference fuzzy sliding mode control of strict-feedback uncertain systems with unknown dead-zone

A new adaptive fuzzy sliding-mode control (FSMC) scheme was proposed, which incorporated the fuzzy logic into sliding-mode control using fuzzy logic to approximate the equivalent control and the upper bound of uncertainty which involved the disturbance and approximation error, thus the influence of modeling error was reduced and the gain of sliding-mode control part was more fitting, such that the chattering effects could be alleviated . Besides, inspired by the thought of fully tuned RBF neural networks, a fully tuned fuzzy logic was proposed which enhanced the approximating ability of fuzzy logic system. Lyapunov stability theorem was used to prove the stability of the system and the adaptive laws were deduced. Finally, simulation results of some BTT missile were included to illustrate the effectiveness of the adaptive fuzzy sliding-mode control scheme.

An effective method, adaptive fuzzy sliding-mode (SM) PI Control to design full-bridge inverter is proposed in this paper. For practical applications, sliding mode control (SMC) with chattering phenomenon is a large hindrance. The modified model of the PI controller is built by the plant extension scheme, in order to relieve the chattering. Also, the fuzzy logic is applied to find the appropriate parameter values of the PI controller, and can adapt to plant parameter variations and external load disturbances. The proposed method has been realized for the actual full-bridge inverters controlled by DSP and CPLD. Simulation and experimental results of the closed-loop system show that the proposed method yields system performance improvement, such as low total harmonic distortion (THD), fast dynamic response, reduced steady-state error, and reduced chattering.

In this paper, an adaptive fuzzy sliding mode controller is employed for air supply on proton exchange membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given set point in order to prevent oxygen starvation and damage to the fuel-cell stack. The proposed control scheme consists of two parts: a sliding mode controller (SMC) and fuzzy logic controller (FLC) with an adjustable gain factor. The SMC is used to calculate the equivalent control law and the FLC is used to approximate the control hitting law. The performance of the proposed control strategy is analysed through simulations for different load variations. The results indicated that the adaptive fuzzy sliding mode controller (AFSMC) is excellent in terms of stability and several key performance indices such as the integral squared error (ISE), the integral absolute error (IAE) and the integral time-weighted absolute error (ITAE), as well as the settling and rise times for the closed-loop control system.

The increasing use of nonlinear loads entails serious electrical energy quality problems in power grids. Active power filter could lead to significant improvement in harmonic compensation performance. In this paper, an adaptive second-order sliding mode fuzzy control based on linear feedback strategy for a three-phase active power filter is proposed to eliminate harmonics. This scheme is constituted by adaptive controller, second-order sliding mode controller, fuzzy controller, and linear feedback controller. The linear feedback controller can compensate the system’s nonlinearities and make the new system have a linear behavior between the input and output. Moreover, the second-order sliding mode controller could transfer the discontinuities in the controller and reduce system chattering. Simulation results illustrate the proposed controller has better performance compared with fuzzy control and adaptive sliding mode fuzzy control.

This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models. Index Terms : Sliding Mode Control, Robot manipulators, Controller, Noise.

In this paper, perfect trajectory tracking control of a non-holonomic Wheeled Mobile Robot (WMR) is tackled. First, a Sliding Mode Controller (SMC) is proposed to convergence of WMR on desired position, velocity and orientation trajectories. Due to the weak performance of the SMC against exogenous inputs, a Mamdani-type fuzzy system is designed to improve the tracking performance of the controller. However, the Fuzzy SMC (FSMC) cannot remove the chattering phenomena completely. Therefore, a supervisory Adaptive FSMC (AFSMC) is proposed to suppress the chattering effects. The superiority of the adaptive approach to the non-adaptive methods has been revealed through simulations.

In this paper, a novel adaptive interval type-2 fuzzy sliding mode control (AIT2FSMC) methodology is proposed based on the integration of sliding mode control and adaptive interval type-2 fuzzy control for chaotic system. The AIT2FSMC system is comprised of a fuzzy control design and a hitting control design. In the fuzzy control design, an interval type-2 fuzzy controller is designed to mimic a feedback linearization (FL) control law. In the hitting control design, a hitting controller is designed to compensate the approximation error between the FL control law and the interval type-2 fuzzy controller. The parameters of the interval type-2 fuzzy controller, as well as the uncertainty bound of the approximation error, are tuned adaptively. The adaptive laws are derived in the sense of Lyapunov stability theorem, thus the stability of the system can be guaranteed. The proposed control system compared to adaptive fuzzy sliding mode control (AFSMC). Simulation results show that the proposed control systems can achieve favorable performance and robust with respect to system uncertainties and external disturbances.

Sliding mode control has been shown to be a robust and effective control approach for stabilization of nonlinear systems. However the dynamic performance of the controller is a complex function of the system parameters, which is often uncertain or partially known. This paper presents an adaptive fuzzy sliding mode control for a class of underactuated nonlinear mechanical systems. An adaptive fuzzy system is used to approximate the uncertain parts of the underactuated system. The adaptive law is designed based on the Lyapunov method. The proof for the stability and the convergence of the system is presented. Robust performance of the adaptive fuzzy sliding mode control is illustrated using a gantry crane system. Simulation results demonstrate that the system output can track the reference signal in the presence of modelling uncertainties, external disturbances and parameter variation.

This article introduces a human–robot interaction controller toward the lower extremity exoskeleton whose aim is to improve the tracking performance and drive the exoskeleton to shadow the wearer with less interaction force. To acquire the motion intention of the wearer, two subsystems are designed: the first is to infer the wearer is in which phase based on floor reaction force detected by a multi-sensor system installed in the sole, and the second is to infer the motion velocity based on the multi-axis force sensor and admittance model. An improved single-input fuzzy sliding mode controller is designed, and the adaptive switching controller is combined to promote the tracking performance considering system uncertainties. Adaptation laws are designed based on the Lyapunov stability theorem. Therefore, the stability of the single-input adaptive fuzzy sliding mode control can be guaranteed. Finally, the proposed methods are applied to the lower extremity exoskeleton, especially in the swing phase. Its effectiveness is validated by comparative experiments.

EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots

A new scheme of adaptive fuzzy sliding mode control for a class of uncertain nonlinear systems is presented, which is by incorporating adaptive model tracking control with fuzzy sliding mode control. It cannot only provide the instantaneous response characteristics of tracking error, make system have good control property and strong robustness against parameter variations and external disturbance, but also eliminate the chattering of conventional sliding mode control. The simulation results show the correctness of this scheme.

In this paper, the proposed observer-based robust adaptive fuzzy sliding mode control scheme can deal with the problem of robust stability for a class of uncertain multi-input multi-output (MIMO) nonlinear systems whose states are not available. The fuzzy basis function is used to approximate an unknown nonlinear function according to some adaptive laws, and then the state observer is designed for estimating the states of the plant, upon which a adaptive fuzzy sliding mode controller is investigated. Based on the Lyapunov stability theorem and the theory of variable structure control, the proposed robust adaptive fuzzy sliding mode control scheme can guarantee the robust stability of the whole closed-loop system and obtain good tracking performance. Finally, the simulation results are carried out on a two-link robot manipulator to illustrate the performance of the proposed approach.

A new adaptive fuzzy sliding mode control scheme is proposed, which combines the fuzzy logic with sliding-mode control together using fuzzy logic system to approximate the difference of the equivalent control between the nominal and the real system. Lyapunov stability theorem is used to prove the stability of the system and the adaptive laws are deduced which possess the characteristics of sliding-mode control. In addition, for better control performance, the upper bound of the uncertainty is estimated. Finally, the correctness and effectiveness of the method have been verified by simulation using nonlinear overload model of pitch channel of some BTT missile.

We present a novel control approach for trajectory tracking of an autonomous airship. First, the dynamics model and the trajectory control problem of an airship are formulated. Second, the sliding mode control law is designed to track a time-varying reference trajectory. To achieve better control performance, fuzzy adaptive sliding mode control is proposed in which the control gains are tuned according to fuzzy rules, and an adaptation law is used to guarantee that the control gains can compensate for model uncertainties of the airship. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control scheme.

Adaptive TSK fuzzy sliding mode control design for switched reluctance motor DTC drive systems with torque sensorless strategy