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This paper presents the design, implementation, and comparative analysis of two intelligent neural network based controllers employed for nonlinear dynamic compensation and adaptive trajectory tracking of a mobile robot system. The first control law is an integration of a backstepping controller with a neural network which is designed to learn the inverse dynamic model of the robot and to compensate...
A method of intelligent PID control was proved and it's based on RBF neural network and fuzzy theory, which constructs RBF neural network identifier online and identifies a controlled object online by means of adopting the receding horizon optimization methods, and adjusts parameters of PID controller online and realizes decoupling control of multivariable, nonlinear and time variation system. The...
This An approach to design dynamic inversion controller with adaptive compensator for airborne dispenser has been presented in this paper. The basic control law is designed using the dynamic inversion method, and the online learning neural networks are used to compensate the system errors and external disturbance. The online weights and thresholds tuning rules are deduced according to the tracking...
This paper presents a hybrid neuro-wavelet scheme for on-line tuning of a wavelet-based multiresolution PID (MRPID) controller in real-time for precise speed control of an interior permanent magnet synchronous motor (IPMSM) drive system under system uncertainties. In the wavelet-based MRPID controller, the discrete wavelet transform (DWT) is used to decompose the error between actual and command speeds...
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural...
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