IEEE Transactions on Automatic Control · 1999 · 27 citations · 10 references
EngineeringMechatronics EngineeringCyber-physical SystemsDynamical SystemsProcess ControlAdaptive ControlControl Of Dynamical SystemsControl System DesignControl SystemsControl TheoryController DesignAdaptive Neural ControlConvergence Rate AnalysisDirect Adaptive ControlFeedforward Neural NetworksHybrid ControlNeural Network ControlRecurrent Neural NetworksSteady-state Error
A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN controller. It results in a steady-state error and is sensitive to SPSA coefficients and termination condition. For trajectory control purpose, a hybrid control system scheme with a conventional PID controller is proposed.
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