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 a mobile robots 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 (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach.Index Terms-Robust nonholonomic mobile robots, Scaling transformation, Model predictive control(MPC), Primal-dual neural network (PDNN).
Abstract-In this paper, a class of nonholonomic chained systems is first converted into two subsystems, and then an explicit exponential decaying term is introduced into the input of the first subsystem to guarantee its controllability. After a state-scaling transformation, a model predictive control (MPC) scheme is proposed for the nonholonomic chained systems. The proposed MPC scheme employs a general projection neural network (GPN) to iteratively solve a quadratic programming (QP) problem over a finite receding horizon. The GPN employed in this paper is proved to be stable in the sense of Lyapunov, and its global convergence to the optimal solution is guaranteed for the reformulated QP. A simulation study is performed to show stable and convergent control performance under the proposed method, irrespective of whether the control input u 1 vanishes or not.Index Terms-General projection neural networks (GPNs), model predictive control (MPC), nonholonomic chained systems, scaling transformation.
This paper investigates a nonlinear-model-predictive-control (NMPC)-strategy-based distributed leader-follower consensus multi-robot formation system. The control objective of this system is to design a group of nonholonomic robots to converge into the desired geometric pattern and to track a designed path. A directed graph that specifies communication topology for the formation is given. A leader-follower consensus formation problem based on the mobile robot kinematic model is obtained, which is further reformulated into a constrained nonlinear minimization problem through the NMPC strategy. A general projection neural network (GPNN) is implemented to efficiently derive the optimal control inputs for the robots. The simulation results verify the effectiveness of the proposed formation algorithm.INDEX TERMS Nonholonomic Multi-robot formation, leader-follower consensus system, nonlinear model predictive control (NMPC), graph theory, general projection neural network (GPNN).
I. INTRODUCTIONIn recent years, robot formation, which is one of the most important research areas in multi-robot coordination, has become more and more attractive. Many researchers are interested in its application prospects such as surveillance, transportation, mine sweeping, rescue operations, and geographical exploration. Compared to single robot, a team of robots can offer many superiorities on working. The consensus formation, whose objective is to control a group of robots to reach and maintain a designed geometric pattern during moving, is a typical formation scheme. Meanwhile, owing to Brockett's theorem [1], it is hard to directly implement the differentiable, or even continuous, pure state feedback algorithm on the nonholonomic-robot-based distributed consensus formation problem.Generally, there are two control paradigms for robot formation: centralized and distributed. In centralized formation,The associate editor coordinating the review of this manuscript and approving it for publication was Jinpeng Yu.
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