{"title":"CODEV:自动模型预测控制设计和形式化验证","authors":"Nicole Chan, S. Mitra","doi":"10.1145/3178126.3187003","DOIUrl":null,"url":null,"abstract":"We present CODEV, a Matlab-based tool for verifying systems employing Model Predictive Control (MPC). The MPC solution is computed offline and modeled together with the physical system as a hybrid automaton, whose continuous dynamics may be nonlinear with a control solution that remains affine. While MPC is a widely used synthesis technique for constrained and optimal control in industry, our tool provides the first automated approach of analyzing these systems for rigorous guarantees of safety. This is achieved by implementing a simulation-based verification algorithm for nonlinear hybrid models, with extensions tailored to the structure of the MPC solution. Given a physical model and parameters for desired system behavior (i.e. performance and constraints), CODEV generates a control law and verifies the resulting system will robustly maintain constraints. We have applied CODEV successfully to a set of benchmark examples, which illuminates its potential to tackle more complex problems for which MPC is used.","PeriodicalId":131076,"journal":{"name":"Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"CODEV: Automated Model Predictive Control Design and Formal Verification\",\"authors\":\"Nicole Chan, S. Mitra\",\"doi\":\"10.1145/3178126.3187003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present CODEV, a Matlab-based tool for verifying systems employing Model Predictive Control (MPC). The MPC solution is computed offline and modeled together with the physical system as a hybrid automaton, whose continuous dynamics may be nonlinear with a control solution that remains affine. While MPC is a widely used synthesis technique for constrained and optimal control in industry, our tool provides the first automated approach of analyzing these systems for rigorous guarantees of safety. This is achieved by implementing a simulation-based verification algorithm for nonlinear hybrid models, with extensions tailored to the structure of the MPC solution. Given a physical model and parameters for desired system behavior (i.e. performance and constraints), CODEV generates a control law and verifies the resulting system will robustly maintain constraints. We have applied CODEV successfully to a set of benchmark examples, which illuminates its potential to tackle more complex problems for which MPC is used.\",\"PeriodicalId\":131076,\"journal\":{\"name\":\"Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3178126.3187003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3178126.3187003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CODEV: Automated Model Predictive Control Design and Formal Verification
We present CODEV, a Matlab-based tool for verifying systems employing Model Predictive Control (MPC). The MPC solution is computed offline and modeled together with the physical system as a hybrid automaton, whose continuous dynamics may be nonlinear with a control solution that remains affine. While MPC is a widely used synthesis technique for constrained and optimal control in industry, our tool provides the first automated approach of analyzing these systems for rigorous guarantees of safety. This is achieved by implementing a simulation-based verification algorithm for nonlinear hybrid models, with extensions tailored to the structure of the MPC solution. Given a physical model and parameters for desired system behavior (i.e. performance and constraints), CODEV generates a control law and verifies the resulting system will robustly maintain constraints. We have applied CODEV successfully to a set of benchmark examples, which illuminates its potential to tackle more complex problems for which MPC is used.