Neural-Dynamic Optimization-Based Model Predictive Control for Tracking and Formation of Nonholonomic Multirobot Systems

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearing-orientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader–follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader–follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.

[1]  Ching-Hung Lee,et al.  Tracking control of unicycle-modeled mobile robots using a saturation feedback controller , 2001, IEEE Trans. Control. Syst. Technol..

[2]  Marilena Vendittelli,et al.  WMR control via dynamic feedback linearization: design, implementation, and experimental validation , 2002, IEEE Trans. Control. Syst. Technol..

[3]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Changyin Sun,et al.  Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer , 2017, IEEE Transactions on Cybernetics.

[5]  Xu Xin-he.  TRAJECTORY TRACKING CONTROL OF WHEELED MOBILE ROBOT BASED ON GLOBAL VISION , 2004 .

[6]  Jian Chen,et al.  Leader-Follower Formation Control of Multiple Non-holonomic Mobile Robots Incorporating a Receding-horizon Scheme , 2010, Int. J. Robotics Res..

[7]  Xiaohui Lu,et al.  Data-Driven Predictive Gearshift Control for Dual-Clutch Transmissions and FPGA Implementation , 2015, IEEE Transactions on Industrial Electronics.

[8]  Baocang Ding,et al.  Distributed RHC for Tracking and Formation of Nonholonomic Multi-Vehicle Systems , 2014, IEEE Transactions on Automatic Control.

[9]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[10]  Shuzhi Sam Ge,et al.  A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  François Chaumette,et al.  Potential problems of stability and convergence in image-based and position-based visual servoing , 1997 .

[12]  Zheng Yan,et al.  Model Predictive Control for Tracking of Underactuated Vessels Based on Recurrent Neural Networks , 2012, IEEE Journal of Oceanic Engineering.

[13]  Fuchun Sun,et al.  Robotic Room-Level Localization Using Multiple Sets of Sonar Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[14]  Di Guo,et al.  Weakly Paired Multimodal Fusion for Object Recognition , 2018, IEEE Transactions on Automation Science and Engineering.

[15]  Giuseppe Loianno,et al.  A Distributed Optimization Framework for Localization and Formation Control: Applications to Vision-Based Measurements , 2016, IEEE Control Systems.

[16]  Warren E. Dixon,et al.  Adaptive tracking control of a wheeled mobile robot via an uncalibrated camera system , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[18]  Peter J. Fleming,et al.  An overview of population-based algorithms for multi-objective optimisation , 2015, Int. J. Syst. Sci..

[19]  Shuzhi Sam Ge,et al.  Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[20]  S. Shankar Sastry,et al.  Formation control of nonholonomic mobile robots with omnidirectional visual servoing and motion segmentation , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[21]  Baris Fidan,et al.  Single-View Distance-Estimation-Based Formation Control of Robotic Swarms , 2013, IEEE Transactions on Industrial Electronics.

[22]  Qingshan Liu,et al.  A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[24]  Luca Ferrarini,et al.  Temperature Control of a Commercial Building With Model Predictive Control Techniques , 2015, IEEE Transactions on Industrial Electronics.

[25]  Mireille E. Broucke,et al.  Pursuit formations of unicycles , 2006, Autom..

[26]  Dongbing Gu,et al.  A model predictive controller for robots to follow a virtual leader , 2009, Robotica.

[27]  Nicholas R. Gans,et al.  Formation control of wheeled robots with vision-based position measurement , 2012, 2012 American Control Conference (ACC).

[28]  Derong Liu,et al.  Output Tracking Control Based on Adaptive Dynamic Programming With Multistep Policy Evaluation , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Liang Tang,et al.  A Recursive Receding Horizon Planning for Unmanned Vehicles , 2015, IEEE Transactions on Industrial Electronics.

[30]  Jun Wang,et al.  Robust Pole Assignment for Synthesizing Feedback Control Systems Using Recurrent Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.