Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

This paper presents a survey of the most significant results on robust control theory. In
particular, we study the modeling of uncertain systems, robust stability analysis for systems …

This paper proposes a new probabilistic solution framework for robust control analysis and
synthesis problems that can be expressed in the form of minimization of a linear objective …

The formulation and final control law of two of the more popular MPC algorithms, dynamic
matrix control (DMC) and generalised predictive controller (GPC), are specially analysed in …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

For dynamic distributed systems modeled by partial differential equations, existing methods
of sensor location in parameter estimation experiments are either limited to one-dimensional …

The scenario approach is a general methodology for data-driven optimization that has
attracted a great deal of attention in the past few years. It prescribes that one collects a …

The 'scenario approach'is an innovative technology that has been introduced to solve
convex optimization problems with an infinite number of constraints, a class of problems …

Linear Model Predictive Control (MPC) continues to be the technology of choice for
constrained, multivariable control applications in the process industry. Successful …

Robotic systems need to be able to plan control actions that are robust to the inherent
uncertainty in the real world. This uncertainty arises due to uncertain state estimation …