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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...
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...
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...
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...
An adaptive backstepping neural controller design is presented for a class of nonaffine nonlinear system with mismatched uncertainties. By applying backstepping design strategy and online approaching uncertainties with fully tuned radial basis function (RBF) neural networks (NNs), the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced...
Antilock braking system (ABS) controls the slip of each wheel of a vehicle to prevent it from locking such that a high friction is achieved and steerability is maintained. It is designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this...
In this paper, a novel model reference adaptive control (MRAC) scheme based on neural network (NN) is proposed for servo system tracking control to achieve high-precision position control. This scheme consists of an MRAC controller and an online NN controller in velocity-loop and a traditional PID controller in position-loop. For reducing influence which arose from modeling error, unknown model dynamics,...
The problems of robust Hinfin tolerant control for a class of discrete systems with state and time delays and actuator faults are studied. The T-S fuzzy model with time delays is adopted to repress a class of discrete systems. By established actuator fault model, the state feedback controller is designed. A sufficient condition for the asymptotic stability of closed-loop system is given based on Lyapunov...
The problem of guaranteed cost fault-tolerant control for networked control systems (NCSs) is discussed in this paper. Based on Lyapunov stability theory and linear matrix inequality (LMI), the sufficient conditions which can meet a cost function for closed-loop networked control systems possessing robust integrity against actuator failures are given by adopting memory state feedback control law,...
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...
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