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Adaptive spatial-model-based predictive control for complex distributed parameter systems
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DOI：10.1016/j.aei.2023.102331.png)
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Abstract
En 中文
Many industrial thermal processes can be classified as complex distributed parameter systems (DPS). The traditional control method has become ill-suited for complex DPS since it ignores the time -varying dynamics and spatially distributed behavior. Moreover, the controller incorporating a nonlinear model imposes substantial computational demands, hindering the timely acquisition of control strategies in practical scenarios. In this study, an adaptive spatial -model -based predictive controller with real-time linearization for complex DPS is proposed. Initially, an adaptive spatiotemporal modeling method is developed to characterize the timevarying dynamics of DPS more accurately. Based on this developed spatiotemporal model, a spatial model predictive control strategy with real-time linearization is further designed to reduce the computational burden of the controller. In addition, a weighted terminal cost is embedded to replace terminal constraints in the controller, ensuring stability while improving calculation efficiency. Theoretical analysis confirms the stability of the proposed controller. Finally, furnace simulation and oven experiments are performed to comprehensively evaluate the performance of the proposed controller. It shows that the proposed method significantly improves tracking performance and robustness while reducing computational load.
Keywords:
Model predictive control
Process control
Data-driven model
Thermal processes
Distributed parameter system
Neural network
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9.9
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4.5K
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1.7W
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