Titlebook: Recent Advances in Intelligent Control Systems; Wen Yu Book 2009 Springer-Verlag London 2009 Adaptive Control.Control.Control Applications

形容词

泥瓦匠

ℋ∞ Fuzzy Control for Systems with Repeated Scalar Nonlinearities. A modified T-S fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. Attention is focused on the analysis

Expertise

Stable Adaptive Compensation with Fuzzy Cerebellar Model Articulation Controller for Overhead Cranes crane model is not exactly known, fuzzy cerebellar model articulation controller (CMAC) is used to compensate friction, and gravity, as well as the coupling between position and anti-swing control. Using a Lyapunov method and an input-to-state stability technique, the controller is proven to be rob

比目鱼

伪书

使饥饿

Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid SystemsConvergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the . strategy of the continuous subsystems could be used in the auto

FLORA

Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networksbe addressed in robot motion planning. In this chapter, the inverse kinematic control of redundant manipulators for obstacle avoidance task is formulated as a convex quadratic programming (QP) problem subject to equality and inequality constraints with time-varying parameters. Compared with our prev

affinity

动脉

任命

Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networksonal link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online l