Indirect adaptive fuzzy sliding mode control for uncertain multi-link robots

This paper investigates the position regulation problem of permanent magnet synchronous motor (PMSM) servo system based on adaptive fuzzy sliding mode control (AFSMC) method. Adaptive fuzzy sliding mode control is designed by using sliding surface as input of fuzzy controller, and adaptive fuzzy controller to approximate the equivalent control law, thus overcome the impact of model inaccuracy and external disturbances. Then, sliding model controller is designed to compensate the error between the equivalent control law and the fuzzy controller by using adaptive method to estimate the upper bound of the approximation error, meanwhile, adaptive fuzzy sliding mode control can weaken the chattering and improve the robustness of the system, make the motor position reach the reference value in finite time, obtaining a better tracking precision. Simulation results illustrate that the proposed control scheme has much better performance than that of conventional PI.

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.

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.

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.

Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller

An indirect adaptive fuzzy sliding mode control scheme for a class of discrete-time systems with model uncertainty and external disturbance is developed. In this approach, a discrete-time exponential reaching law is used to obtain the ideal sliding mode control law. Then, two fuzzy systems and a third one are used to approximate unknown functions of the system and the switch-type control in the ideal control law, respectively, wherein the consequent parameters are adjusted by an on-line adaptation law with parameter projection. Towards to Tank pressurization system, experiment results have shown the effectiveness of the algorithm.

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.

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

In this paper a new method for Adaptive Fuzzy Sliding Mode Controller (AFSMC) is proposed for a Quadrotor helicopter and then this controller is developed by Parallel Fuzzy System (AFSMCP). Because oscillations in input signal and external disturbances, weights vector of fuzzy system may have unwanted changes and even it can be drifted to very large amounts. This event can eventuate chattering phenomenon in controlled signal and if there are un-modeled dynamics, it will even result instability in output of system. To eliminate of undesirable increases of weight vector while the adaptation law is being proceed, a parallel fuzzy system is used in addition of main fuzzy system. This fuzzy system controlled the speed and variation rate of main fuzzy system. In this technique, weights vector of main fuzzy system will be forced to follow the consequent weights vector of the parallel fuzzy system. The simulation results show effectiveness and robustness of proposed method.

In this paper, an adaptive fuzzy sliding-mode control (AFSMC) system is investigated for an islanded inverter to achieve a high-performance power supply. A sliding mode control (SMC) law is designed initially to obtain both the voltage tracking error and the current tracking error of the inverter involved, to realize both the output-voltage regulation and the current protection with global stability. Moreover, to deal with uncertainties in the practical inverter system without the chattering phenomenon, an adaptive fuzzy system embedded with a self-adjustive translation width is developed to replace the switch term of the SMC. In addition, the adaptation laws, derived from the Lyapunov stability theorem, adjust the AFSMC parameters online to guarantee optimal and robust performance. Furthermore, the superior control performance of the proposed AFSMC is verified by a numerical simulation in MATLAB, producing experimental results on the prototype in comparison with the conventional SMC.

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

Underactuated mechanical systems are frequently encountered in several industrial and real-world applications such as robotic manipulators with elastic components, aerospace vehicles, marine vessels, and overhead container cranes. The design of accurate controllers for this kind of mechanical system can become very challenging, especially if a high level of uncertainty is involved. In this paper, an adaptive fuzzy inference system is combined with a sliding mode controller in order to enhance the control performance of uncertain underactuated mechanical systems. The proposed scheme can deal with a large class of multiple-input–multiple-output underactuated systems subject to parameter uncertainties, unmodeled dynamics, and external disturbances. The convergence properties of the resulting intelligent controller are proved by means of a Lyapunov-like stability analysis. Experimental results obtained with an overhead container crane demonstrate not only the feasibility of the proposed scheme, but also its improved efficacy for both stabilization and trajectory tracking problems.

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson’s disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances. A class of fast and robust MIMO adaptive fuzzy terminal sliding mode control (AFTSMC) has been presented for control of membrane potential of thalamic neuron populations through continuous adaptive DBS current applied to the thalamus. A fuzzy logic system (FLS) is used to estimate the unknown nonlinear dynamics of the model, and the weights of FLS are adjusted online to guarantee the convergence of FLS parameters to optimal values. The simulation results show that the proposed AFTSMC not only significantly produces lower tracking errors in comparison with the classical adaptive fuzzy sliding mode control (AFSMC), but also makes more robust and reliable outputs. The results suggest that the proposed AFTSMC provides a more robust and smooth control input which is highly desirable for hardware design and implementation.

PNEUMATIC ISOLATORS ARE PROMISING CANDIDATES FOR INCREASING THE QUALITY OF THE PRECISION INSTRUMENTS. FOR THIS PURPOSE, HOWEVER, HIGHER PERFORMANCE OF SUCH ISOLATORS IS A PREREQUISITE. IN THIS PAPER AN ADAPTIVE FUZZY SLIDING MODE CONTROLLER IS PROPOSED TO IMPROVE THE PERFORMANCE OF A PNEUMATIC ISOLATOR IN THE LOW FREQUENCY RANGE, I.E., WHERE THE PASSIVE TECHNIQUES HAVE OBVIOUS SHORTCOMINGS. THE MAIN IDEA IS TO COMBINE THE ADAPTIVE FUZZY CONTROLLER WITH ADAPTIVE PREDICTOR AS A NEW TIME DELAY CONTROL TECHNIQUE. THE ADAPTIVE FUZZY SLIDING MODE CONTROL AND THE ADAPTIVE FUZZY PREDICTOR HELP TO CIRCUMVENT THE INPUT DELAY AND NONLINEARITIES IN SUCH ISOLATORS. THE MAIN ADVANTAGE OF THE PROPOSED METHOD IS THAT THE CLOSED-LOOP SYS-TEM STABILITY IS GUARANTEED UNDER CERTAIN CONDITIONS. SIMULATION RESULTS ARE COMPARED TO THE RESULTS OF TDC TECHNIQUE TO VERIFY THE EFFECTIVENESS OF THE PROPOSED METHOD.

An adaptive fuzzy sliding mode controller (AFSMC) is adopted to reduce the 2D flow-induced vibration of an elastically supported square-section cylinder, free to oscillate in stream-wise and<br />transverse directions in both lock-in and galloping regions. The AFSMC strategy consists of a fuzzy logic inference system intended to follow a sliding-mode controller (SMC), and a robust control system designed to retrieve the variance between the sliding mode and fuzzy controllers. The sprung square cylinder first experiences vortex-induced vibrations with increasing Reynolds number, and then, after passing the critical flow velocity, it confronts high-amplitude and low-frequency<br />vibrations of galloping owning to its sharp corners. A co-simulation platform is considered by linking the AFSMC system modeled in Matlab/Simulink to the plant model implemented in Fluent, aiming at the calculation of opposite control force needed for comprehensive annihilation of the cylinder motions. Based on the performed numerical simulations, it becomes clear that the utilized active control system has successfully mitigated the two-degree-of-freedom vibrations of a square cylinder in both the lock-in region and galloping zone. Here, the vibration amplitudes in the transverse and<br />streamwise directions have decreased by 93% and 94%, for the lock-in region and 93% and 99%, for the galloping zone, respectively.