Successive Convexification for Nonlinear Model Predictive Control with Continuous-Time Constraint Satisfaction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910429185310720 |
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| author | Uzun, Samet Elango, Purnanand Kamath, Abhinav G. Kim, Taewan Acikmese, Behcet |
| author_facet | Uzun, Samet Elango, Purnanand Kamath, Abhinav G. Kim, Taewan Acikmese, Behcet |
| contents | We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency. We leverage the recently proposed successive convexification framework for trajectory optimization, where: (1) the path constraints and running cost are equivalently reformulated by augmenting the system dynamics, (2) multiple shooting is used for exact discretization, and (3) a convergence-guaranteed sequential convex programming (SCP) algorithm, the prox-linear method, is used to solve the discretized receding-horizon optimal control problems. The resulting NMPC framework is computationally efficient, owing to its support for warm-starting and premature termination of SCP, and its reliance on first-order information only. We demonstrate the effectiveness of the proposed NMPC framework by means of a numerical example with reference-tracking and obstacle avoidance. The implementation is available at https://github.com/UW-ACL/nmpc-ctcs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Successive Convexification for Nonlinear Model Predictive Control with Continuous-Time Constraint Satisfaction Uzun, Samet Elango, Purnanand Kamath, Abhinav G. Kim, Taewan Acikmese, Behcet Optimization and Control We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency. We leverage the recently proposed successive convexification framework for trajectory optimization, where: (1) the path constraints and running cost are equivalently reformulated by augmenting the system dynamics, (2) multiple shooting is used for exact discretization, and (3) a convergence-guaranteed sequential convex programming (SCP) algorithm, the prox-linear method, is used to solve the discretized receding-horizon optimal control problems. The resulting NMPC framework is computationally efficient, owing to its support for warm-starting and premature termination of SCP, and its reliance on first-order information only. We demonstrate the effectiveness of the proposed NMPC framework by means of a numerical example with reference-tracking and obstacle avoidance. The implementation is available at https://github.com/UW-ACL/nmpc-ctcs |
| title | Successive Convexification for Nonlinear Model Predictive Control with Continuous-Time Constraint Satisfaction |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2405.00061 |