Nonlinear Model Predictive Control using a bilinear Carleman linearization-based formulation for chemical processes
Abstract
Model Predictive Control (MPC) has gained widespread acceptance in industry due to its capability of coping with constraints, handling multiple-input-multiple-output systems and evolving control policy. One significant barrier to the development of MPC is its complexity in computation when encountering nonlinear systems, the resulting feedback delays, and the consequent loss of controller performance as well as stability issues. In this manuscript, we propose a new formulation of MPC for nonlinear systems based on Carleman linearization. The nonlinear dynamic constraints are modeled with bilinear representations. This formulation enables analytical computation of NMPC. Optimization is accelerated by providing sensitivity of the cost function to the control signals. A case study example using a nonlinear isothermal CSTR is presented, demonstrating that the proposed formulation reduces computational efforts.
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Different from conventional linearization methods such as Taylor series and Carleman approaches, the linear system (13) is obtained without a concrete mathematical model and is effective in the whole set D_x, not only locally around equilibrium points.
“Different from conventional linearization methods, such as Taylor series [29], [47] and Carleman approaches [48], the linear system (13) is obtained without concrete mathematical model. Besides, it is effective in the whole set D x , not only locally around equilibrium points.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [48]
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- Authors
- Fang, Y., & Armaou, A.
- Affiliations
- Pennsylvania State University (US)
- Venue
- 2015 American Control Conference (ACC)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 5629-5634
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W1510038060
- Cited by
- 14
- References
- 28
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 1 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 3
- FWCI
- 3.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ... American Control Conference/Proceedings of the American Control Conference (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Process Optimization and Integration
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Keywords
- nonlinear model predictive control, Carleman linearization, bilinear representation, computational efficiency, chemical process control, feedback delay
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