scholarly journals Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjian Chen ◽  
Di Li ◽  
Pingxin Wang ◽  
Xibei Yang ◽  
Hongmei Li
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Sliding Mode ◽  
Robotic Exoskeleton ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Control Scheme ◽  
System Uncertainties ◽  
Adaptive Control Scheme

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

scholarly journals State Synchronization for a Class of n-Dimensional Nonlinear Systems with Sector Input Nonlinearity via Adaptive Two-Stage Sliding Mode Control

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chi-Hsin Yang ◽  
Kun-Chieh Wang ◽  
Long Wu ◽  
Ren Wen
Keyword(s):  
Dynamical System ◽  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
External Disturbances ◽  
Input Nonlinearity ◽  
Control Scheme ◽  
System Uncertainties ◽  
Stage 1 ◽  
Adaptive Control Scheme

This study addresses an adaptive two-stage sliding mode control (SMC) scheme for the state synchronization between two identical systems, which belong to a kind of n-dimensional chaotic system, by considering the appearance of lumped system uncertainties and external disturbances. The controlled system is assumed to be attached to sector nonlinearity for the control input. The proposed adaptive control scheme involves time-variable state-feedback gains, which are updated in accordance with the appropriate adapted rules without foreknown the certain information of nonlinear system dynamics, bounds of lumped system uncertainties and external disturbances, and sector input nonlinearity. The derivation of the control scheme is found based on the introduced sequence of two sliding functions. The stage 1 sliding function is defined by the states of the error dynamical system, where the asymptotical stability is inherent. Then, the stage 2 sliding function is formed by the stage 1 function, where the finite-time stabilization is guaranteed. The proposed adaptive control scheme can cope with the effect of sector nonlinearity for the control to meet the control goal. The sufficient conditions of the stability of the error dynamical system are proven mathematically by means of the Lyapunov theorem. Besides, the capacity of the present scheme is carried out by the numerical studies.

NEURAL ADAPTIVE CONTROL FOR VIBRATION SUPPRESSION IN COMPOSITE FIN-TIP OF AIRCRAFT

2008 ◽  
Vol 18 (03) ◽  
pp. 219-231 ◽  
Author(s):  
S. SURESH ◽  
N. KANNAN ◽  
N. SUNDARARAJAN ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Vibration Suppression ◽  
Mode Shapes ◽  
Neural Adaptive Control ◽  
Control Scheme ◽  
Adaptive Neural Network Control ◽  
Adaptive Control Scheme

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H∞ control scheme.

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

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