3,985 research outputs found

    Cooperative distributed MPC for tracking

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    This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

    Parsimonious cooperative distributed MPC algorithms for offset-free tracking

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    We propose in this paper novel cooperative distributed MPC algorithms for tracking of piecewise constant setpoints in linear discrete-time systems. The available literature for cooperative tracking requires that each local controller uses the centralized state dynamics while optimizing over its local input sequence. Furthermore, each local controller must consider a centralized target model. The proposed algorithms instead use a suitably augmented local system, which in general has lower dimension compared to the centralized system. The same parsimonious parameterization is exploited to define a target model in which only a subset of the overall steady-state input is the decision variable. Consequently the optimization problems to be solved by each local controller are made simpler. We also present a distributed offset-free MPC algorithm for tracking in the presence of modeling errors and disturbances, and we illustrate the main features and advantages of the proposed methods by means of a multiple evaporator process case study

    Cooperative Tracking using Model Predictive Control

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    Distributed Model Predictive Control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of input and output composing the overall system. These controllers may cooperate to find an optimal control sequence that minimize a global cost function, as in the case of Cooperative Distributed Model Predictive Control (CD-MPC). In this thesis several types of linear CD-MPC controller for tracking are studied. The aim of these controllers is to drive the overall system to an admissible set-point, satisfying hard input and state constraints. However, this result, in literature, is achieved by using a set of centralized variables that keep track of the global state of the system. In this context, I developed a novel CD-MPC approach for tracking that relies on local information instead of the plant-wide information flow. This new control strategy reduces communication overhead and is more scalable than classical CD-MPC presented in literature. Il controllo predittivo distribuito si riferisce ad una classe specifica di controllo predittivo in cui i controllori calcolano localmente gli ingressi sfruttando solo un sottoinsieme delle variabili del sistema globale. Tali controllori possono cooperare per trovare una sequenza ottima di controlli che minimizzano una funzione obiettivo globale, come nel caso del Cooperative Distributed Model Predictive Control (CD-MPC). In questa tesi sono implementati più tipi di controllori lineari CD-MPC per il tracking. Lo scopo di tali controllori è di far convergere il sistema globale su un set-point ammissibile, soddisfacendo eventuali vincoli di stato e ingresso. Comunque, in letteratura, tale risultato sul tracking è raggiunto usando un insieme di variabili centralizzate informative del sistema globale. In questo contesto è stato quindi proposto un nuovo approccio a CD-MPC per il tracking che si basa su informazioni locali piuttosto che su tutto il flusso di informazioni del sistema nel suo insieme. Questa nuova strategia di controllo permette di ridurre il livello di congestione di rete ed è più scalabile degli algoritmi presenti attualmente in letteratura per questo tipo di problema

    Distributed Model Predictive Control Using a Chain of Tubes

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    A new distributed MPC algorithm for the regulation of dynamically coupled subsystems is presented in this paper. The current control action is computed via two robust controllers working in a nested fashion. The inner controller builds a nominal reference trajectory from a decentralized perspective. The outer controller uses this information to take into account the effects of the coupling and generate a distributed control action. The tube-based approach to robustness is employed. A supplementary constraint is included in the outer optimization problem to provide recursive feasibility of the overall controllerComment: Accepted for presentation at the UKACC CONTROL 2016 conference (Belfast, UK

    Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

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    In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M

    Indoor wireless communications and applications

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    Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
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