{"type":"ScholarlyArticle","title":"Model Predictive Control with Reach-avoid Analysis","authors":[{"name":"Dejin Ren","slug":"dejin-ren","given":"Dejin","family":"Ren","affiliation":"State Key Lab. of Computer Science , Institute of Software , CAS , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Wanli Lu","slug":"wanli-lu","given":"Wanli","family":"Lu","affiliation":"National Engineering Research Center of Rail Transportation Operation and Control System , Beijing Jiaotong University , Beijing , China"},{"name":"Jidong Lv","slug":"jidong-lv","given":"Jidong","family":"Lv","affiliation":"National Engineering Research Center of Rail Transportation Operation and Control System , Beijing Jiaotong University , Beijing , China"},{"name":"Lijun Zhang","slug":"lijun-zhang","given":"Lijun","family":"Zhang","affiliation":"State Key Lab. of Computer Science , Institute of Software , CAS , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Bai Xue","slug":"bai-xue","given":"Bai","family":"Xue","affiliation":"State Key Lab. of Computer Science , Institute of Software , CAS , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"}],"abstract":"In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples.","doi":"10.24963/ijcai.2023/604","url":"https://ijcai.org/proceedings/2023/604","pdf":"https://ijcai.org/proceedings/2023/0604.pdf","markdown":"https://ijcai.org/proceedings/2023/604.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/604","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Planning and Scheduling","track":"Main Track","number":604,"pages":{"first":5437,"last":5445},"keywords":["Machine Learning: ML: Optimization","Planning and Scheduling: PS: Learning in planning and scheduling"],"videos":[],"references":[{"position":1,"raw":"[Arimoto et al., 1984] Suguru Arimoto, Sadao Kawamura, and Fumio Miyazaki. 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