This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].

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Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

Laboratory experimentation plays an essential role in engineering and scientific education. Virtual and remote labs reduce the costs associated with conventional hands-on labs due to their required equipment, space, and maintenance staff. Furthermore, they provide additional benefits such as supporting distance learning, improving lab accessibility to handicapped people, and increasing safety for dangerous experimentation. This paper analyzes the literature on virtual and remote labs from its beginnings to 2015, identifying the most influential publications, the most researched topics, and how the interest in those topics has evolved along the way. To do so, bibliographical data gathered from ISI Web of Science, Scopus and GRC2014 have been examined using two prominent bibliometric approaches: science mapping and performance analysis. Display Omitted Laboratory experimentation plays an essential role in engineering and sci-entific education.Virtual and remote labs are emerging as a valuable alternative to conven-tional hands-on labs.This paper analyzes the literature on virtual and remote labs from 1993 to 2015.4405 records retrieved from ISI Web of Science, Scopus and GRC2014 are processed.Two bibliometric approaches are applied: performance analysis and science mapping.

Virtual and remote labs in education

Stochastic linear Model Predictive Control with chance constraints – A review

In this paper, five major shifts in engineering education are identified. During the engineering science revo- lution, curricula moved from hands-on practice to mathemat- ical modeling and scientific analyses. The first shift was initiated by engineering faculty members from Europe; accel- erated during World War II, when physicists contributed mul- tiple engineering breakthroughs; codified in the Grinter report; and kick-started by Sputnik. Did accreditation hinder curricular innovations? Were engineering graduates ready for practice? Spurred by these questions, the Accreditation Board for Engineering and Technology (ABET) required engineering programs to formulate outcomes, systematically assess achievement, and continuously improve student learning. The last three shifts are in progress. Since the engineering science revolution may have marginalized design, a distinctive feature of engineering, faculty members refocused attention on cap- stone and first-year engineering design courses. However, this third shift has not affected the two years in between. Fourth, research on learning and education continues to influence engineering education. Examples include learning outcomes and teaching approaches, such as cooperative learning and inquiry that increase student engagement. In shift five, tech- nologies (e.g., the Internet, intelligent tutors, personal compu- ters, and simulations) have been predicted to transform education for over 50 years; however, broad transformation has not yet been observed. Together, these five shifts characterize changes in engineering education over the past 100 years.

Five Major Shifts in 100 Years of Engineering Education The authors discuss what has reshaped, or is currently reshaping, engineering education over the past 100 years up until the current emphasis on design, learning, and social-behavioral sciences research and the role of technology.

This paper argues the case for emulation-based virtual laboratories in control engineering education. It demonstrates that such emulation experiments can give students an industrially relevant educational experience at relatively low cost. The paper also describes a particular emulation-based system that has been developed with the aim of giving students an introduction to real-world control engineering design.

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Emulation-Based Virtual Laboratories: A Low-Cost Alternative to Physical Experiments in Control Engineering Education

A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment

Model predictive control has been a major success story in process control. More recently, the methodology has been used in other contexts, including automotive engine control, power electronics and telecommunications. Most applications focus on set-point tracking and use single-sequence optimisation. Here we consider an alternative class of problems motivated by the scheduling of emergency vehicles. Here disturbances are the dominant feature. We develop a novel closed-loop model predictive control strategy aimed at this class of problems. We motivate, and illustrate, the ideas via the problem of fluid deployment of ambulance resources.

Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling | NOVA. The University of Newcastle's Digital Repository

Adrian M. Medioli

Papers

Emulation-Based Virtual Laboratories: A Low-Cost Alternative to Physical Experiments in Control Engineering Education

A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment

Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling | NOVA. The University of Newcastle's Digital Repository

A fundamental control performance limit for a class of positive nonlinear systems