Neuroadaptive deferred full-state constraints control without feasibility conditions for uncertain nonlinear EASSs |
| |
| Institution: | 1. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;2. State Key Laboratory of Synthetical Automation of Process Industries, Northeastern University, Shenyang, Liaoning 110189, China;1. School of Automation, Southeast University, Nanjing 210096, China;2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096, China;1. State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China;2. Intelligent Research Institute of Chang''an Automobile Co., Ltd, Chongqing, China;1. Department of Electronics and Communication, PDPM, IIITDM, Jabalpur, Madhya Pradesh, India;2. Department of Electronics and Communication, The LNM Institute of Information Technology, Jaipur, India;1. National Technical University of Athens, School of Electrical and Computer Engineering, Greece;2. USC Viterbi, Ming Hsieh Department of Electrical and Computer Engineering - Systems, USA;1. School of Automation, Nanjing University of Science and Technology, Nanjing, China;2. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China;3. School of Science, Huzhou University, Huzhou 313000, China |
| |
| Abstract: | This article studies the neuroadaptive full-state constraints control problem for a class of electromagnetic active suspension systems (EASSs). First, the original constraint system with arbitrary initial values is transformed into a new constraint system with zero initial values by using the shift function method. Then, a new kind of cotangent-type nonlinear state-dependent transition function is constructed to solve the asymmetric time-varying full-state constraints control problem, which eliminates the limitation that the virtual controller needs to satisfy the feasibility conditions in the previous full-state constraints control based on Barrier Lyapunov Function (BLF) and Integral BLF. Furthermore, the neural networks (NNs) are used as nonlinear function approximators to deal with the unknown nonlinear dynamics of EASSs, a neuroadaptive full-state constraints control design method is proposed under the Backstepping recursive design framework. Finally, the effectiveness of the proposed method is verified by a simulation of EASSs with road disturbances. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
