Constrained predictive control synthesis for quantized systems with Markovian data loss

摘要: This paper investigates the predictive control synthesis problem for constrained feedback systems with both missing data and quantization. By introducing a compensation strategy an augmented Markov jump linear model polytopic uncertainties, effects of loss quantization on system performance are considered simultaneously. A robust approach involving recovering probabilities is developed by minimizing upper bound expected value infinite horizon quadratic objective at each sampling instant. Additional conditions to satisfy input constraint in presence multiple also incorporated into (MPC) synthesis. Furthermore, recursive feasibility proposed MPC algorithm closed-loop mean-square stability proved. Simulation results given illustrate effectiveness approach.