Practical model predictive control for a class of nonlinear systems using linear parameter-varying representations
| Autor: | Philipp Rostalski, Pablo S. G. Cisneros, Georg Männel, Hossam S. Abbas, Herbert Werner |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Optimization problem General Computer Science Computer science model predictive control Scheduling (production processes) robust stability 02 engineering and technology Informatik [004] Tracking error Constrained systems 020901 industrial engineering & automation Control theory 0202 electrical engineering electronic engineering information engineering General Materials Science Quadratic programming Technik [600] robotic manipulators Computer Science::Information Retrieval 600: Technik General Engineering linear parameter-varying systems TK1-9971 Nonlinear system Model predictive control Control system 004: Informatik 020201 artificial intelligence & image processing Electrical engineering. Electronics. Nuclear engineering ddc:004 ddc:600 |
| Zdroj: | IEEE Access 9 : 9410253 (2021) IEEE Access, Vol 9, Pp 62380-62393 (2021) |
| Popis: | In this paper, a practical model predictive control (MPC) for tracking desired reference trajectories is demonstrated for controlling a class of nonlinear systems subject to constraints, which comprises diverse mechanical applications. Owing to the linear parameter-varying (LPV) formulation of the associated nonlinear dynamics, the online MPC optimization problem is solvable as a single quadratic programming (QP) problem of complexity similar to that of LTI systems. For offset-free tracking, based on the notion of admissible reference, the controller ensures convergence to any admissible reference while its deviation from the desired reference is penalized in the stage cost of the optimization problem. This mechanism provides a safety feature under the physical limitations of the system. To guarantee stability and recursive feasibility, a terminal cost as a tracking error penalty term and a terminal constraint associated with both the terminal state and the admissible reference are included. We use tube-based concept to deal with the uncertainty of the scheduling parameter over the prediction horizon. Therefore, the online optimization problem is solved for only the nominal system corresponding to the current value of the scheduling parameter and subject to tightened constraint sets. The proposed approach has been implemented successfully in real-time onto a robotic manipulator, the experimental results illustrates its efficiency and practicality. |
| Databáze: | OpenAIRE |
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