This paper proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of cons...

This paper proposes a novel robust predictive control synthesis technique for constrained nonlinear systems based on linear matrix inequalities (LMIs) formalism. Local discrete-time polytopic models have been exploited for prediction of the system behavior. This design strategy can be applied to a wide class of nonlinear systems provided a suitable embedding is available. The devised procedure ...

High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in mostMPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a realtime setting. In this paper we develop a real-time MPC approach for linear systems th...

This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear sys...

This paper gives an overview of robustness in Model Predictive Control (MPC). After reviewing the basic concepts of MPC, we survey the uncertainty descriptions considered in the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of “closedloop prediction” is discussed at length. The paper concludes with some comments on future...

The paper attempts to show that using the robust model-based predictive control (RMPC) strategy for control of thermal processes can lead to energy savings in comparison with classical control approaches. RMPC is applied for control of a tubular heat exchanger that is used for pre-heating petroleum by hot water. The heat exchanger is a nonlinear system with time delay and uncertainty. The contr...

This paper employs nonlinear disturbance observer (NDO) for robust trajectory-free Nonlinear Model Predictive Control (NMPC) of biped robots. The NDO is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. In contrary to the slow-varying disturbances, handling sudden pushing ...

laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. therefore, it is widely adopted for complex industrial process control. in this work, laguerre function based adaptive model predictive control algorithm (ampc) was implemented to control continuous stirred tank rea...