The path following of intelligent unmanned vehicle scheme based on adaptive sliding mode-model predictive control |
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| Institution: | 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;2. School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei 430205, PR China;1. Key Laboratory of Intelligent Analysis and Decision on Complex Systems, School of Science, Chongqing University of Posts and Telecommunications, Chongqing, PR China;2. Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, PR China;3. Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany;4. Institute of Physics, Humboldt University of Berlin, Berlin, Germany;1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;2. Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, China;3. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;1. LAJ Laboratory, University of Jijel, Algeria;2. Université Paris-Saclay, Univ Evry, IBISC, Evry 91020, France;1. Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, South Korea;2. Department of Mathematics and Algorithms Research, Nokia Bell Labs, Murray Hill NJ 07974, USA;1. Department of State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China;2. School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 101408, China;3. Qiqihar Heavy CNC Equipment Corp., Ltd., Qiqihar, 161000, China |
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| Abstract: | An adaptive sliding mode-model predictive control for the path following of intelligent unmanned vehicle is given in this paper. On account of excellent performances of the sliding mode structure, this algorithm can not only effectively estimate the uncertainty of the vehicle system to further improve the following accuracy, but minish the amount of calculation in comparision with model predictive control. Then, the following accuracy between the real system and the theoretical model can be compensated by the fractional order coefficient of controller. Therefore, an adaptive fractional order sliding mode-fractional order model predictive control is designed to follow the path of the intelligent unmanned vehicle. Meanwhile, the robust stability and control accuracy of the associated control algorithm are proved. Finally, different paths are designed to verify the theoretical analysis of the control performance in the controllers. |
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