Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

In this paper, a robust model predictive control (MPC) scheme using neural network-based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state-scaling transformation, the intrinsic controllability of the mobile robot can be regained by incorporation into the control input u1 an additional exponential decaying term. An MPC-based control method is then designed for the robot in the presence of external disturbances. The MPC optimization can be formulated as a convex nonlinear minimization problem and a primal- dual neural network is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been improved by the proposed neurodynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach.

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