Summary An observer‐based output feedback predictive control approach is proposed for linear parameter varying systems with norm‐bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an ℋ∞‐type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input‐to‐state stable observer, the closed‐loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

In practical control systems, the plant states are not always measurable, so state estimation becomes essential before the state feedback control is applied. In this paper, we consider output feedback model predictive control (MPC) for linear parameter varying (LPV) systems with input constraints. We proposed two approaches to obtain the observer gain, that is to compute the gain in the dynamic optimization at each time instant (on-line), and to compute the gain in advance (off-line), respectively. By applying both approaches, the state estimation error goes to zero asymptotically, meanwhile, the state feedback gain is optimized. In fact, the on-line approach can help enlarge the feasibility region and improve the control performance. It has been shown that feasibility of both approaches can be maintained for the closed-loop control systems even in the presence of state estimation error. Finally, the proposed output-feedback MPC strategies are applied to an angular positioning control system and the control of a transcritical CO2 vapor compression refrigeration system.