Adaptive critic designs: a case study for neurocontrol
Neural Networks
Ohio State University at the 2004 DARPA Grand Challenge: Developing a Completely Autonomous Vehicle
IEEE Intelligent Systems
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
IEEE Transactions on Neural Networks
Control of a nonholonomic mobile robot using neural networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Brief paper: Optimality and convergence of adaptive optimal control by reinforcement synthesis
Automatica (Journal of IFAC)
Adaptive dual heuristic programming based on delta-bar-delta learning rule
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
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Autonomous wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis or by domain expert to build control rules. This paper presents an adaptive critic motion control design, which enables WMR to autonomously generate the control ability by learning through trials. The design consists of an adaptive critic velocity control loop and a self-learning posture control loop. The neural networks in the velocity neuro-controller (VNC) are corrected with the dual heuristic programming (DHP) adaptive critic method. Designer simply expresses the control objective by specifying the primary utility function then VNC will attempt to fulfill it through incremental optimization. The posture neuro-controller (PNC) learns by approximating the specialized inverse velocity model of WMR so as to map planned positions to suitable velocity commands. Supervised drive supplies variant velocity commands for PNC and VNC to set up their neural weights. During autonomous drive, while PNC halts learning VNC keeps on correcting its neural weights to optimize the control performance. The proposed design is evaluated on an experimental WMR. The results show that the DHP adaptive critic design is a useful base of autonomous control.