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Online Reduced-Order Data-Enabled Predictive Control
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AI
DOI：10.1109/TASE.2025.3619434.png)
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Abstract
En 中文
Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit parametric models. Traditional DeePC methods use pre-collected input/output (I/O) data to construct a Hankel matrix offline and then formulate a predictive control framework online for linear, weakly nonlinear, and weakly stochastic systems. However, in systems with evolving dynamics, incorporating real-time data into the DeePC framework becomes crucial to enhance control performance. This paper proposes an online DeePC framework designed for strongly nonlinear and/or time-varying systems (i.e., systems with evolving dynamics), enabling the algorithm to update the Hankel matrix online by adding real-time informative signals. By exploiting the minimum non-zero singular value of the Hankel matrix, the developed online DeePC selectively integrates informative data and effectively captures evolving system dynamics. Additionally, a numerical singular value decomposition technique is introduced to reduce the computational complexity for updating a reduced-order Hankel matrix. Simulation results on three cases, linear time-varying system, vehicle anti-rollover control, and Li-ion battery fast charging, demonstrate the effectiveness of the proposed online reduced-order DeePC framework. Note to Practitioners-With the increasing complexity of modern control systems and the expanding availability of data, there is a growing preference for data-driven control schemes. Different from model-based control schemes that require precise system modeling, data-driven controllers generate control policies by using collected input/output (I/O) data. Data-driven control can be categorized into two paradigms: indirect data-driven control, which includes system identification and model-based control design, and direct data-driven control, which bypasses the system identification process and directly designs control strategies based on I/O data. The direct data-driven control offers greater flexibility and potential by avoiding a specific parametric model in control design. However, for time-varying systems, offline collected I/O data is not reliable, and incorporating real-time data into the control framework becomes crucial.
Keywords:
Trajectory
Linear systems
Real-time systems
Time-varying systems
Matrix decomposition
Control systems
Vectors
Predictive control
System dynamics
Stochastic systems
Data-enabled predictive control
reduced-order model
online optimization
time-varying systems
Journal
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