Smith Predictor Type Control Architectures for Time Delayed Teleoperation
International Journal of Robotics Research
Fuzzy output regulator design of discrete affine systems with multiple time-varying delays
Fuzzy Sets and Systems
IEEE Transactions on Neural Networks
Adaptive neural network predictive control based on PSO algorithm
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A new iterative learning controller using variable structure Fourier neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Industrial Engineering
Trends in the control schemes for bilateral teleoperation with time delay
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Predictor-based control for an uncertain Euler-Lagrange system with input delay
Automatica (Journal of IFAC)
Predictive control method of improved double-controller scheme based on neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Expert Systems: The Journal of Knowledge Engineering
Repetitive control of servo systems with time delays
Robotics and Autonomous Systems
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A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.