Due to computational burden and dynamic uncertainty, the model based decoupling control approach is hard to be implemented in a multivariable robotic system. Here, a model-free model reference adaptive fuzzy sliding mode controller (MRAFSC) is proposed to control a five degree-of-freedom (DOF) robot. MRAFSC drives the system state variables to hit a user-defined sliding surface and then slide along it to approach a reference model. Consequent parameters of the fuzzy control can be initialized at zero, and then adjusted by using a novel online parameters tuning algorithm derived from the Lyapunov stability theory. A boundary layer function is introduced into the updating law to eliminate the chattering phenomenon which is inherent to the sliding mode control. The state errors converge into a specified error bound which depends on the boundary layer, parameter errors and modeling errors. The experimental results show that this novel controller has good control performance, stability and robustness. © 2004 Elsevier Ltd. All rights reserved.

A kind of recurrent fuzzy wavelet neural network (RFWNN) is constructed by using recurrent wavelet neural network (RWNN) to realize fuzzy inference. In the network, temporal relations are embedded in the network by adding feedback connections on the first layer of the network, and wavelet basis function is used as fuzzy membership function. An adaptive control scheme based on RFWNN is proposed, in which, two RFWNN are used to identify and control plant respectively. The proposed adaptive control scheme is applied on AC servo control problem, and simulation results are given.

A sliding mode control strategy compensated by neural network is proposed, and that is applied to robotic trajectory control. First, a three-layer neural network is used to compensate the uncertainties in the robotic system. Then the structure of sliding mode controller with neural network compensation and the learning algorithm of the neural network are designed based on Lyapunov theorem to guarantee the stability of the system and improve the dynamic performance of the system. The simulation results show that the proposed control strategy can not only reduce the phenomenon of chattering in effect, but also has good robustness and dynamic performance.

Wavelet networks are a class of neural networks consisting of wavelets. In this paper, wavelet network is used for estimating model of robot manipulator. An algorithm is introduced for constructing wavelet network, and BP algorithm is used for training wavelet network An example on two-joint robot manipulator identification is presented for illustration

An adaptive fuzzy controller is developed for AC servo system to guarantee both global stability and performance. AC motor dynamics under consideration may include large nonlinear uncertainties, such as nonlinearload variations. The Controller Output Method (COEM) isintroduced and applied to the design of adaptive fuzzycontrol system. The method employs a gradient descentalgorithm to minimize a cost function that is based on theerror of the controller output. The cost function can beminimized through adapting some or all of the parameters of fuzzy controller. The proposed adaptive fuzzy controlleris applied to a AC servo control problem and comparedwith a standard fuzzy controller. The simulation resultsshow that the proposed control is effective and yieldssuperior performance.