Quantized Robust Model Predictive Control for Networked Time-Delay LPV Systems with Grey Wolf-Pattern Search Algorithm

In modern networked control systems, various network-induced constraints pose critical challenges to reliable system operation. This paper investigates the robust model predictive control (RMPC) problem for networked linear parameter varying (LPV) systems subject to time delays and dynamic quantization. The dynamic quantizer compresses sensor data for transmission to the controller, inevitably introducing quantization errors. In order to address these constraints, an RMPC framework integrated with the grey wolf-pattern search optimization (GWPS) algorithm is proposed. The GWPS algorithm is utilized to update the weight matrices of RMPC online, thereby improving the system performance under quantization errors and time delays. Moreover, a mixed $H_{2}/H_{\infty }$ performance index is embedded into the controller design to guarantee both robustness and baseline control performance against bounded disturbances and quantization errors. Simulation results on a numerical example and an islanded microgrid system example have demonstrated the effectiveness and feasibility of the proposed method.