Abstract
With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness.
Highlights
Data-Driven Residual GeneratorConsidering a general noisy linear time-invariant discrete system:. Among them, x (k) ∈ Rn , u(k) ∈ Rku and y(k) ∈ Rm are the state variable, input variable and output variable of the system, respectively. w(k) ∈ Rn is the process noise; v(k) ∈ Rm is the measurement noise
College of Intelligence Science and Technology, National University of Defense Technology, Abstract: With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is increasing
Model-based control performance monitoring is widely used in industrial automation control systems and has always been favored [6,7,8]
Summary
Considering a general noisy linear time-invariant discrete system:. Among them, x (k) ∈ Rn , u(k) ∈ Rku and y(k) ∈ Rm are the state variable, input variable and output variable of the system, respectively. w(k) ∈ Rn is the process noise; v(k) ∈ Rm is the measurement noise. W(k) ∈ Rn is the process noise; v(k) ∈ Rm is the measurement noise. X (k) ∈ Rn , u(k) ∈ Rku and y(k) ∈ Rm are the state variable, input variable and output variable of the system, respectively. Both obey the normal distribution and are independent of the input and state variables. Where s is the number of future data, s ≥ n. Where g is a parameter vector [29] and is the core of residual generator
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