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Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems
1Advanced Control Systems Laboratory of the School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. zhshhou@bjtu.edu.cn
IEEE Transactions on Neural Networks
|December 8, 2011
Summary
This study introduces a data-driven adaptive control method using dynamic linearization for nonlinear systems. The approach ensures system stability and accurate tracking using only measured input/output data.
Area of Science:
- Control Engineering
- Nonlinear System Dynamics
- Data-Driven Modeling
Background:
- Traditional control methods often require accurate system models, which are difficult to obtain for complex nonlinear systems.
- Model-free adaptive control (MFAC) offers an alternative by learning system dynamics from data.
- Existing MFAC techniques may have limitations in handling general multiple-input and multiple-output (MIMO) nonlinear discrete-time systems.
Purpose of the Study:
- To propose a novel model-free adaptive control (MFAC) approach for MIMO nonlinear discrete-time systems.
- To introduce a new dynamic linearization technique (DLT) incorporating pseudo-partial derivatives for controller design.
- To demonstrate the effectiveness of the proposed MFAC-DLT approach through analysis and simulations.
Main Methods:
- Development of a data-driven model-free adaptive control (MFAC) strategy.
- Introduction of a novel dynamic linearization technique (DLT) with pseudo-partial derivatives.
- Implementation of compact, partial, and full forms of DLT for controller design.
- Validation using extensive simulations on general MIMO nonlinear discrete-time systems.
Main Results:
- The proposed MFAC approach, utilizing DLT, successfully controls MIMO nonlinear discrete-time systems.
- The controller design relies solely on readily available input/output data, eliminating the need for explicit system models.
- Analysis and simulations confirm the achievement of bounded-input bounded-output (BIBO) stability.
- Demonstrated convergence of tracking errors, indicating effective system performance.
Conclusions:
- The proposed data-driven MFAC approach with DLT provides a robust and effective control solution for complex nonlinear systems.
- The method's reliance on measured data simplifies controller design and broadens applicability.
- Guaranteed stability and tracking performance highlight the practical utility of this novel control strategy.
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