Keywords: Optimization and Model Predictive Control, Dedicated Optimization Solvers for Model Predictive Control
Abstract: MPC has long been a source of challenges to optimization methodology, because of the need to tackle difficult, structured optimization problems in limited wall-clock time and in a way that meets the requirements of the controller (for example, stability). Recently, the interaction between optimization and MPC has taken on a new dimension, with new proposals being made for the use of machine learning (and thus optimization) in control strategies in which the models are learnt rather than specified. We review several of these developments from an optimization perspective, and speculate about how recent developments in nonconvex optimization and in MPC might impact each other in future.

Keywords: IoT/IoE, Real-Time Implementation of Model Predictive Control, Cyber-Physical Systems
Abstract: We present a novel cloud-based MPC scheme that is based on a recently proposed real-time proximal gradient method. The cloud-based implementation requires sensitive data (e.g., system states) to be transmitted via public networks and to be processed in the cloud. We guarantee privacy of the data throughout the control-loop by encrypting the control scheme using (partial) homomorphic encryption. The resulting encrypted MPC computes encrypted predictive control actions based on encrypted system states (without intermediate decryptions).

Keywords: Economic Predictive Control, Process Control, Stability and Recursive Feasibility
Abstract: Security of chemical process control systems against cyberattacks is critical due to the potential for injuries and loss of life when chemical process systems fail. A potential means by which process control systems may be attacked is through the manipulation of the measurements received by the controller. One approach for addressing this is to design controllers that make manipulating the measurements received by the controller in any meaningful fashion very difficult, making the controllers a less attractive target for a cyberattack of this type. In this work, we develop a model predictive control (MPC) implementation strategy that incorporates Lyapunov-based stability constraints and can allow several potential control laws to be available to apply to the process, one of which can be randomly selected at each sampling time, potentially making the response of the controller to a false state measurement more difficult to predict a priori. We investigate closed-loop stability and recursive feasibility of the resulting control design, and utilize a benchmark chemical process example to demonstrate the difference in the control actions computed by such a randomized MPC implementation strategy compared with those for the same process by the same MPC design utilized at every sampling time.

Keywords: Stability and Recursive Feasibility
Abstract: In this paper, we present a novel proximity formulation for moving horizon estimation (MHE) of constrained discrete-time linear systems. The cost function of the moving horizon optimization problem consists of a convex stage cost as well as a quadratic term centered around a pre-estimating observer. We show stability of the resulting estimation error and demonstrate the advantages offered by the proposed scheme by means of a numerical example.

Keywords: Stability and Recursive Feasibility
Abstract: We present a nonlinear model predictive control (MPC) scheme for tracking of periodic output trajectories. The scheme combines stabilization and output regulation in one layer, thus ensuring constraint satisfaction irrespective of changes in the reference signal. For periodic reference signals we ensure exponential stability of the optimal periodic trajectory. The main tool to enable this design is a novel reference generic offline computation that provides suitable terminal ingredients for tracking of reference trajectories. The practicality of this approach is demonstrated at a benchmark example.

Keywords: Formation control: mobile robots, UAVs, Motion Control, Robotics
Abstract: This paper presents a solution to the problem of multiple vehicle cooperative path following (CPF) that takes explicitly into account the constraints on the vehicles inputs and the topology of the inter-vehicle communications network. The solution involves decoupling the original constrained CPF problem into two sub-problems: i) single vehicle constrained path following and ii) multi-agent system (MAS) coordination. The first is solved by adopting a sampled-data model predictive control (MPC) scheme, whereas the latter is tackled by using a distributed control law with an event triggered communication (ETC) mechanism. We show that this design methodology yields a stable closed-loop CPF system: the path following error for each vehicle is globally asymptotically stable (GAS) and the coordination errors between the vehicles are bounded. A simulation example consisting of three autonomous vehicles following a given 2D- desired formation illustrates the efficacy of the CPF strategy proposed.

Keywords: Real-Time Implementation of Model Predictive Control, Power Electronics, Motion Control
Abstract: This paper proposes a tailored fixed-point iteration scheme for the model predictive torque control of permanent magnet synchronous machines. Both spherical voltage and current constraints are taken into account using a projection function and an augmented Lagrangian approach. The fixed-point scheme is a continuous control set approach and is based on the nonlinear dq-model. The inductivities and permanent magnet flux are modelled current-dependently and therefore account for saturation and parasitic effects. Experimental results on a standard dSpace hardware show the performance as well as the very small computational load of the proposed torque control scheme, which is therefore suitable for an embedded implementation.

Keywords: Cyber-Physical Systems, Modularisation and Predictive Control, Robust Model Predictive Control
Abstract: Technological frameworks such as the Internet of Things, Industry 4.0, and the Industrial Internet are promoting the development of cyberphysical systems with flexible structure where subsystems enter, leave, and get replaced over time. In absence of a reference model, flexibility must be mirrored in the control and monitoring layers, meaning that local regulators and fault detectors have to be designed in a scalable fashion by using information from a limited number of subsystems. In this talk we will present plug-and-play architectures integrating distributed MPC strategies with fault detection algorithms. The goal is to achieve fault tolerance through the automatic disconnection of malfunctioning subsystems and the reconfiguration of local controllers. Approaches for performing these operations safely and without compromising the stability of the overall system will be discussed. The final part of the talk will be devoted to research perspectives in the field.