An off-line approach for output feedback robust model predictive control |
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| Institution: | 1. School of Electro-Mechanical Engineering, Xidian University, China;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, China;3. College of Automation, Chongqing University of Posts and Telecommunications, China;4. Inria Lille-Nord Europe, France;1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China;2. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China;3. School of Information Science and Engineering, Chengdu University, Chengdu 610106, PR China;4. School of Mechatronic Engineering and Automation, Shanghai University, 200072, PR China |
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| Abstract: | For constrained linear parameter varying systems subject to bounded disturbances and noises, this article investigates an off-line output feedback robust model predictive control approach. The sub-observer gains with robust positively invariant sets, and sub-controller gains with robust control invariant sets are simultaneously off-line optimized and stored in a look-up table. According to real-time estimation error bounds and estimated states, the time-varying sub-observer gains and sub-controller gains are on-line searched. The proposed off-line output feedback robust model predictive control approach with the guarantee of nested robust positively invariant sets and robust control invariant sets in theory reduces the on-line computational burden. |
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