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Item type:Publication, Access status: without files , Decentralized real-time iterations for distributed NMPCThis article presents a Real-Time Iteration (RTI) scheme for distributed Nonlinear Model Predictive Control (NMPC). The scheme transfers the well-known RTI approach, a key enabler for many industrial real-time NMPC implementations, to the setting of cooperative distributed control. At each sampling instant, one outer iteration of a bi-level decentralized Sequential Quadratic Programming (dSQP) method is applied to a centralized optimal control problem. This ensures that real-time requirements are met and it facilitates cooperation between subsystems. Combining novel dSQP convergence results with RTI stability guarantees, we prove local exponential stability under standard assumptions on the MPC design with and without terminal constraints. The proposed scheme only requires neighbor-to-neighbor communication and avoids a central coordinator. A numerical example with coupled inverted pendulums demonstrates the efficacy of the approach.Publicationtype: Journal Article42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Real-time optimization for distributed nonlinear model predictive controPublicationtype: Doctoral Thesis13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Real-time optimization for distributed nonlinear model predictive controlThe widespread availability of computing and communication technology promotes the development of Cyber Physical Systems of Systems (CPSoS) which couple subsystems in the physical and cyber domains. CPSoS enable cooperation between subsystems and facilitate novel applications in diverse domains such as energy systems, robotic teams, and beyond. Distributed Model Predictive Control (DMPC) is a framework for control of CPSoS as it builds upon centralized Model Predictive Control (MPC) while also addressing the needs of CPSoS. One promising avenue relies on the use of decentralized optimization to solve a cooperative Optimal Control Problem (OCP) online without a coordinator. However, the iterative nature of decentralized optimization induces the need for repeated communication between coupled subsystems, which may jeopardize real-time feasibility. For centralized nonlinear MPC, real-time feasibility is achieved through Real-Time Iterations (RTIs) that enable fast control sampling. This thesis presents a novel decentralized RTI (dRTI) scheme for DMPC. The framework builds upon a novel decentralized Sequential Quadratic Programming (dSQP) method which parallelizes expensive computations among subsystems via the alternating direction method of multipliers. We first prove the local convergence of dSQP to regular OCP solutions in the presence of non-convex constraints. Subsequently, we show the local exponential stability of the closed-loop system-optimizer dynamics and we provide quantifiable bounds on the number of optimizer iterations that guarantee stability. We further demonstrate the efficacy of dRTI in hardware experiments for the formation control of mobile robots and holonomic hovercraft, studying scenarios of increasing difficulty ranging up to collision avoidance in dynamic environments. In addition to the above theoretical and practical contributions, we showcase the computational efficacy of dRTI through simulation case studies.Publicationtype: Doctoral Thesis107 63 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Large problems are not necessarily hard: a case study on distributed NMPC paying offA key motivation in the development of Distributed Model Predictive Control (DMPC) is to accelerate centralized Model Predictive Control (MPC) for large-scale systems. DMPC has the prospect of scaling well by parallelizing computations among subsystems. However, communication delays may deteriorate the performance of decentralized optimization, if excessively many iterations are required per control step. Moreover, centralized solvers often exhibit faster asymptotic convergence rates and, by parallelizing costly linear algebra operations, they can also benefit from modern multicore computing architectures. On this canvas, we study the computational performance of cooperative DMPC for linear and nonlinear systems. To this end, we apply a tailored decentralized real-time iteration scheme to frequency control for power systems. DMPC scales well for the considered linear and nonlinear benchmarks, as the iteration number does not depend on the number of subsystems. Comparisons with multi-threaded centralized solvers demonstrate competitive performance of the proposed decentralized optimization algorithms.Publicationtype: Conference Paper18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Cooperative distributed model predictive control for embedded systems: experiments with hovercraft formationsThis paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles.Publicationtype: Conference Paper23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formationsDistributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.Publicationtype: Journal Article5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A compendium of optimization algorithms for distributed linear-quadratic MPCModel Predictive Control (MPC) for {networked, cyber-physical, multi-agent} systems requires numerical methods to solve optimal control problems while meeting communication and real-time requirements. This paper presents an introduction on six distributed optimization algorithms and compares their properties in the context of distributed MPC for linear systems with convex quadratic objectives and polytopic constraints. In particular, dual decomposition, the alternating direction method of multipliers, a distributed active set method, an essentially decentralized interior point method, and Jacobi iterations are discussed. Numerical examples illustrate the challenges, the prospect, and the limits of distributed MPC with inexact solutions.Publicationtype: Journal ArticleScopus© Citations 6 7 39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Software-defined networking driven time-sensitive networking for mixed-criticality control applications(IEEE, 2022); 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Decentralized non-convex optimization via bi-level SQP and ADMMDecentralized non-convex optimization is important in many problems of practical relevance. Existing decentralized methods, however, typically either lack convergence guarantees for general non-convex problems, or they suffer from a high subproblem complexity. We present a novel bilevel SQP method, where the inner quadratic problems are solved via ADMM. A decentralized stopping criterion from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method has local convergence guarantees for non-convex problems. Moreover, it only solves sequences of Quadratic Programs, whereas many existing algorithms solve sequences of Nonlinear Programs. The method shows competitive numerical performance for an optimal power flow problem.Publicationtype: Conference Paper17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , First results on turnpike bounds for stabilizing horizons in NMPCThe stability analysis of NMPC schemes has seen significant progress in recent years, which includes schemes with and without terminal ingredients such as penalties and constraints. In the context of economic MPC, turnpike properties, which are closely related to dissipativity properties of the underlying optimal control problem, have enabled novel insights on stability conditions. In the present note, we show that turnpike properties naturally enable to bound the required stabilizing horizon length in MPC. The main idea is to define the turnpike with respect to a level-set of the terminal penalty. This way we derive a bound on the stabilizing horizon which guarantees that a terminal constraint is satisfied without being explicitly stated in the underlying optimal control problem. A numerical example indicates that if the terminal set is not too small, the horizon bound is not overly conservative.Publicationtype: Conference Paper8