Journal ArticleDOI
Neural network-based model reference adaptive control system
H.D. Patino,Derong Liu +1 more
- Vol. 30, Iss: 1, pp 198-204
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TLDR
An approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems and results showing the feasibility and performance are given.Abstract:
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a /spl sigma/-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.read more
Citations
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Journal ArticleDOI
Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer
Mou Chen,Shuzhi Sam Ge +1 more
TL;DR: Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.
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Bayesian Nonparametric Adaptive Control Using Gaussian Processes
TL;DR: This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions.
Journal ArticleDOI
Neural networks for advanced control of robot manipulators
TL;DR: A robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors.
Journal ArticleDOI
An adaptive neurocontroller using RBFN for robot manipulators
Min-Jung Lee,Young-Kiu Choi +1 more
TL;DR: An adaptive neurocontroller for robot manipulators based on the radial basis function network (RBFN) is proposed, which is a branch of neural networks and is mathematically tractable to approximate nonlinear robot dynamics.
Journal ArticleDOI
Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control
TL;DR: This paper investigates the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength.
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