This special issue consists of eleven articles with contributions from internationally renowned experts in the field of model predictive control. Among them are four papers for the development of computationally effective algorithms, two papers for nonlinear model predictive control, three papers for general areas of model predictive control and two papers for their applications.
The special issue begins with the article by Professor Boyd and his colleagues from Stanford University, USA, that proposes a simple and effective heuristic approach to embedded mixed integer quadratic programming. This approach will find wide applications in solving the optimisation problems among engineering systems. The second paper by Professor Morari and his colleagues from ETH Zurich, Switzerland, addresses the challenges of real-time implementation of optimisation-based control and trajectory planning for nonlinear systems, in which efficient implementation of interior-point methods is proposed for multistage nonlinear convex programs. Professor Cantoni and his colleagues from the University of Melbourne, Australia investigate structured computation of optimisation for constrained cascade systems. Professor Weller and his colleagues from the University of Newcastle, Australia, propose a non-cooperative, price-based hierarchical distributed optimisation approach that probably recovers the centralised or cooperative optimal performance, relating the algorithm to the application for price-based model predictive control of a small-scale electricity network.
The next two papers present the research topics in nonlinear model predictive control. In a tutorial style, Professors Zanon and Bemporad and their colleagues from IMT Institute for Advanced Studies, Lucca, Italy, discuss how to bridge the gap between linear and nonlinear model predictive control via real-time iterations. Professor Guay and his colleagues from Queen's University, Canada, present a design technique for fast sampled-data nonlinear model predictive control.
The next three papers are in the general areas of model predictive control. Professor Rossiter and his colleagues from the University of Sheffield, UK, address the issue of embedding target information into MPC design using a systematic and effective approach. Professor Ossareh from University of Vermont, USA, investigates reference governors in the context of model predictive control for periodic systems. Professor Braatz and his colleagues at MIT, USA, present stochastic model predictive control with joint chance constraints.
The final two papers of this special issue are related to the applications of model predictive control. A model predictive control system is used in rollover prevention for steer-by-wire vehicles presented by Professor Khajepour and his colleagues from University of Waterloo, Canada, Professors Romero (National University of Rosario, Argentina) and Seron (University of Newcastle, Australia) and their colleague investigate geometric MPC for three-phase AC inverter with performance bounds.
I sincerely hope that the readers will enjoy reading this special issue on model predictive control.