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In this paper, an adaptive neural network (NN) sliding mode controller is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. The controller consists of two simple radial basis function (RBF) NNs which are used to approximate the desired sliding mode controller and the uncertain nonlinear part of the error dynamical...
A radial basis functions (RBF) neural network terminal sliding mode strategy is developed to control a uncertain coupled chaotic system with the two freedoms. Based on the designed adaptive update laws, the weights of RBF neural network are trained on-line so that the designed controllers can be updated adaptively. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled...
In this paper, an adaptive neural sliding mode controller (ANSMC) is proposed as an asymptotically stable robust controller for a class of Control Affine Nonlinear Systems (CANSs) with unknown dynamics. In the proposed method a Control Affine Radial Basis function Network (CARBFN) is developed for online identification of CANSs. A recursive algorithm based on Extended Kalman Filter (EKF) is used for...
In this paper, a robust intelligent tracking control (RITC) system employs a recurrent wavelet-based cerebellar model articulation controller (RWCMAC) is developed for uncertain nonlinear system to achieve H∞ tracking performance. The dynamic structure of RWCMAC has superior capability to the conventional static cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic...
The problem of output tracking control for a class of multi-input multi-output uncertain systems is considered. A novel adaptive robust controller is proposed, which incorporates a variable-structure radial basis function (RBF) network to approximate unknown system dynamics. The RBF network can determine its structure on-line dynamically, where radial basis functions are added or removed to ensure...
Aiming at a class of mismatched uncertain nonlinear system with a dead zone input, an adaptive neural controller design scheme is presented by combining backstepping with variable structure control (VSC). By applying online approaching uncertainties with fully turned radial basis function (RBF) neural networks (NNs), the adaptive tuning rules are derived from the Lyapunov stability theory. To deal...
In this paper, a hybrid control strategy, variable universe adaptive fuzzy sliding mode control, is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as...
A novel global sliding model control (GSMC) control law based on backstepping method was proposed for tracking control of robot manipulators with uncertainties. This control scheme consisted of two combined controllers. One was the integral backstepping controller for nominal model method, the other was the compensatory sliding mode controller for practical plant. This control scheme integrated the...
A novel robust Hinfin decentralized intelligent control (RHDIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory...
This paper develops a methodology for trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and external load changes. Based on backstepping technique, the proposed control law consists of two levels: kinematics and dynamic levels. First, the auxiliary kinematic velocity control laws for the mobile platform and the onboard arm are separately proposed...
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