Robust model predictive control for nonlinear parameter varying systems without computational delay

Abstract

This article proposes a one-step ahead robust model predictive control (MPC) for discrete-time Lipschitz nonlinear parameter varying (NLPV) systems subject to disturbances. Within the proposed design framework, the optimization that generates the MPC policy to be implemented at next time instant is executed in advance during the current sampling period based on future state prediction.This new feature allows avoidance of the online computational delay existing in the traditional MPC settings and improves the control performance. The proposed MPC is proved to be recursively feasible with the guarantee for robust closed-loop system stability and satisfaction of input and output constraints. A tractable linear matrix inequality (LMI) optimization problem is formulated to compute the control gains at each time instant. The computational complexity of the obtained LMI problem is also analyzed. The one-step ahead robust MPC is further developed to cover discrete-time Lipschitz NLPV systems with disturbance compensation. Efficacy and performance improvement of the design are demonstrated through a numerical example and an application to adaptive cooperative cruise control for automated vehicles under variable road geometry.

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

“…As for fuel consumption, studies on MPC-based CACC technologies demonstrate how extending control architectures to platooning gives opportunities to increase comfort [143]. More specifically, in [119], the simulation results of the proposed robust MPC (RMPC) are compared to those of a nominal MPC in a highway driving scenario in CACC.…”

“…If parts and components are in a mature state, HiL tests can be performed with the actual components integrated into specifically designed test benches (see Figure 3). As for fuel consumption, studies on MPC-based CACC technologies demonstra how extending control architectures to platooning gives opportunities to increase comfo [143]. More specifically, in [119], the simulation results of the proposed robust MP (RMPC) are compared to those of a nominal MPC in a highway driving scenario in CAC Two vehicles are platooning, and the leading one sends information to the following on which exploits this to improve safety and comfort.…”

Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network

“…Recently, due to complex mechanical structures, physical and chemical reactions, and so forth, in actual industrial production processes where nonlinearities are inevitable, some scholars have started to work on dual‐rate sampled nonlinear systems 16 . Wang et al utilized a Hammerstein model structure to describe the system nonlinearities, then transformed the system into a bilinear identification model based on the polynomial transformation technique, and further presented a maximum likelihood identification algorithm, which can achieve a lower computation load than the over‐parameterization method 17 .…”

Proportional Predictive Control of Networked Linear Switched Reluctance Machine System With Time-Varying Delay

“…It is better than the traditional method of letting the system control value remain unchanged or equal to zero to deal with the time delay problem. However, it also has a drawback, due to the calculation delay caused by a large number of calculations in forward prediction and rolling optimization (Findeisen and Allgöwer, 2004;Lan and Zhao, 2020). A time-varying networked predictive control method was proposed in the study by (Yang et al, 2014) for a network control system with communication delay.…”