Data-driven parallel linear controllers for reference tracking in nonlinear systems
Yao Shi1, José M Maestre2, Lei Xie1
1State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027 Hangzhou, China.
ISA Transactions
|June 25, 2025
Summary
This study introduces parallel linear controllers (PLIC), a novel data-driven method for nonlinear system control without models. PLIC effectively achieves reference tracking by combining inverse control and error compensation strategies.
Area of Science:
- Control Theory
- Nonlinear Systems
- Data-Driven Methods
Background:
- Nonlinear system control is challenging, especially without models and with real-time computation needs.
- Existing methods often require accurate system models, limiting their applicability.
Purpose of the Study:
- To develop a purely data-driven approach for reference tracking control in nonlinear systems.
- To address challenges of model unavailability and real-time computation requirements.
Main Methods:
- Proposed a parallel linear controllers (PLIC) architecture with two concurrent linear controllers.
- Employed Koopman operator for system lifting and quadratic programming for constraint handling in one controller.
- Utilized a modified direct data-driven virtual reference tuning for error compensation in the second controller.
Main Results:
- Analyzed the closed-loop properties of the proposed PLIC method.
- Demonstrated the efficacy of PLIC through benchmark simulations.
- Achieved effective reference tracking control using only data, without explicit system models.
Conclusions:
- The parallel linear controllers (PLIC) offer a viable data-driven solution for reference tracking in nonlinear systems.
- The method successfully handles model unavailability and real-time constraints.
- PLIC shows promise for practical applications in complex control scenarios.
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