An Efficient Approach for SIMO Systems using Adaptive Fuzzy Hierarchical Sliding Mode Control

<div>The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) underactuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness.</div>

An Efficient Approach for SIMO Systems using Adaptive Fuzzy Hierarchical Sliding Mode Control

<div>The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) underactuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness.</div>

A novel adaptive control design method for stochastic nonlinear systems using neural network

This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

Sliding mode control of continuous-time switched systems with signal quantization and actuator nonlinearity

This article investigates sliding mode control for a class of continuous-time switched systems with signal quantization, actuator nonlinearity and persistent dwell-time switching that can guarantee the globally uniformly asymptotical stability of the closed-loop system. First, a sliding surface is devised for the switched system and sufficient conditions are proposed to ensure the globally uniformly asymptotical stability of the sliding motion equation by utilizing multiple Lyapunov function technique. Second, the sliding mode control laws, based on the parameters of quantizer, actuator nonlinearity and disturbance, are devised to stabilize the closed-loop systems. Moreover, sufficient conditions are given to guarantee the devised sliding surface’s reachability. Finally, the superiority and effectiveness of developed results is illustrated via a numerical simulation.