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Tracking control of nonlinear uncertain Chua's chaotic systems is studied. Based on coordinate transform, the paper deduced the principle with which Chua's chaotic system can be translated into the so-called general strict-feedback form. Combining the back stepping method with robust control technology, an adaptive parameter control law is developed and thus the output tracking is successfully accomplished...
For a missile system with both input unmodeled dynamics and uncertainties, a robust adaptive controller based on RBF neural networks and backstepping technique is proposed. Two RBF neural networks are firstly designed to approximate the input unmodeled dynamics and its inversion. By introducing adaptive control technique, uncertainties are estimated by adaptive law. Then, adaptive tuning law of weight...
For a class of nonlinear MIMO system, a novel decoupling control method was proposed. Firstly, equivalent input of each subsystem was designed using backstepping method. Then, the total uncertainties including coupling effect, external disturbance and parameter time-varying of each subsystem was estimated via an ESO, and the estimated value was introduced into the backstepping controller to compensate...
This paper presents an adaptive neural network H∞ control for unidirectional synchronization of modified Morris-Lecar (ML) neurons in a master-slave configuration. The modified ML neurons exhibit different periods bursting and repetitive spiking in response to electrical stimulation. Based on the Lyapunov stability theory, we derive the update laws of neural network for approximating the nonlinear...
In this paper, we propose an adaptive control method for micro aerial vehicle(MAV) flight system with model uncertainties. The proposed control system is constructed by the combination of the adaptive dynamic surface control(ADSC) technique and the self recurrent wavelet neural network(SRWNN). The ADSC technique which make the virtual controller using the first order filter provides us with the ability...
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...
Combining backstepping with variable structure control (VSC) scheme, an adaptive neural controller design for a class of mismatched uncertain nonlinear system with inputs containing sector nonlinearity and dead zone is presented. By applying online approximating uncertainties with fully tuned radial basis function (RBF) neural networks (NNs), the adaptive tuning rules are derived from the Lyapunov...
Based on neural networks, an adaptive fast terminal sliding mode (FTSM) control strategy is presented for a class of high-order uncertain nonlinear system. The radial basis function (RBF) neural network is used to online approach uncertainties of system. The mathematical relationship between the neighborhood of each sliding mode surface and the system uncertainty is derived. It is strictly proved...
Aiming at a class of nonaffine nonlinear system with uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching nonlinearity with fully tuned radial basis function (RBF) neural networks, the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced to deal...
Aiming at a class of strict-feedback nonlinear systems with mismatched uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching uncertainties with fully tuned radial basis function (RBF) neural networks, the adaptive tuning rules are derived from the Lyapunov stability theory. To deal with the problem of extremely...
A novel T-S fuzzy model-based method is proposed for controlling a class of chaotic (hyperchaotic) systems with uncertain parameters. The interval matrix theory is applied to describe the parametric uncertainty. The T-S fuzzy model is employed for accurately modeling the chaotic (hyperchaotic) systems. Based on the T-S fuzzy model, the parallel distributed compensation (PDC) technique is applied to...
This paper considers a robust decentralized Hinfin control problem for uncertain multi-channel discrete-time systems with time-delay. The uncertainties are assumed to be time-invariant, norm-bounded, and exist in the system, time-delay and output matrices. Our interest is focused on dynamic output feedback. A sufficient condition for the multi-channel uncertain discrete time-delay system to be robustly...
Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system. Theoretical...
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