Abstract
The control of nonlinear dynamics is gaining increasing attention since many practical systems are with such kind of characteristics. To deal with the system uncertainty, in this paper, the efficient learning control using neural network is proposed for the nonlinear strict-feedback system. The whole scheme is with the back-stepping design, while the novel learning is proposed for the neural network weights update. To deal with the approximation error, the robust item is added. The stability of the closed-loop dynamics is analysed and the effectiveness of the design is verified through flight simulation.
Highlights
Nonlinear dynamics exists in many practical systems such as robots,[1] manipulators,[2] flight vehicle,[3] quadrotors,[4] and MEMS gyroscope.[5]
As the nth equation shown in dynamics (1) and using neural network (NN) to approximate fn(jn), it is known that v^_ n = gn(vn + gznzn)un(jn) where gn and gzn are the positive design constants
The efficient learning-based control is designed for the strict-feedback systems
Summary
Nonlinear dynamics exists in many practical systems such as robots,[1] manipulators,[2] flight vehicle,[3] quadrotors,[4] and MEMS (microelectromechanical system) gyroscope.[5]. Keywords Neural network, nonlinear dynamics, learning control, strict-feedback system For the dynamics in controllability canonical form, the main design can be with the error surface and the robust design can be used. The control of the strict-feedback system is well studied using back-stepping and dynamic surface design.
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