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With the emergence of large-scale and high-performance modern production system, how to effectively improve the safety performance of the control system has become an urgent problem to be solved.[1] Fault detection and diagnosis as the main means to improve the reliability of the control system have been widely valued. Fault diagnosis is a technique to use the information in the system to detect the...
This paper proposes adaptive control for vehicle active suspensions with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise damper dynamics). An adaptive control is designed to stabilize the altitude of vehicles and to improve the ride comfort, where an augmented neural network is developed to provide the online compensation for the unknown dynamics. A novel adaptive law is proposed...
This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation)...
Aiming at meeting the needs of high precision and high stability in large angle attitude maneuver control for flexible spacecraft, a self-adjusting sliding-mode control law based on RBF neural network was proposed. A delay factor in exponential form was introduced into the reaching-law of sliding-mode control to improve the stability of maneuver process. An online self-adjusting factor was designed...
This paper presents a predictive proportionalintegral-derivative (PID) controller based on wavelet type-2 fuzzy neural network (WT2FNN) for a class of nonlinear systems. The WT2FNN is employed to estimate the nonlinear function of the controlled system and the predictive PID controller is derived via a predictive performance criterion. The stability analysis of the closed-loop control system is presented...
This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer...
In order to raise the design efficiency and get the most excellent design effect, this paper combined ant colony optimization (ACO) algorithm and neural networks, which based on ACO algorithm and the implementing framework of ACO. It gives the basic theory and steps; The test results show that rapid global convergence and reached the lesser mean square error(MSE) when compared with genetic algorithm,...
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