Journal ArticleDOI
Adaptive neural control of uncertain MIMO nonlinear systems
Shuzhi Sam Ge,Cong Wang +1 more
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TLDR
Adapt neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms that avoid the controller singularity problem completely without using projection algorithms.Abstract:
In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.read more
Citations
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Journal ArticleDOI
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints
Wei He,Yuhao Chen,Zhao Yin +2 more
TL;DR: Adaptive neural network control for the robotic system with full-state constraints is designed, and the adaptive NNs are adopted to handle system uncertainties and disturbances.
Journal ArticleDOI
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
TL;DR: The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design, and the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis.
Patent
Stable adaptive neural network controller
TL;DR: In this article, an adaptive control system uses a neural network to provide adaptive control when the plant is operating within a normal operating range, but shifts to other types of control as the plant operating conditions move outside of the normal operating ranges.
Journal ArticleDOI
Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
TL;DR: A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: for any initial compact set, how to determine a priori the compact superset on which NN approximation is valid; and how to ensure that the arguments of the unknown functions remain within the specified compact supersets.
Journal ArticleDOI
Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics
Hongwei Zhang,Frank L. Lewis +1 more
TL;DR: A practical design method is developed for cooperative tracking control of higher-order nonlinear systems with a dynamic leader using a robust adaptive neural network controller for each follower node such that all follower nodes ultimately synchronize to the leader node with bounded residual errors.
References
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Proceedings ArticleDOI
Robust adaptive control
Petros Ioannou,Jing Sun +1 more
TL;DR: In this article, the authors present a model for dynamic control systems based on Adaptive Control System Design Steps (ACDS) with Adaptive Observers and Parameter Identifiers.
Journal ArticleDOI
Universal approximation using radial-basis-function networks
Jooyoung Park,Irwin W. Sandberg +1 more
TL;DR: It is proved thatRBF networks having one hidden layer are capable of universal approximation, and a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.