HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems

Torrisi, Fabio Danilo; Bemporad, Alberto

doi:10.1109/tcst.2004.824309

Cited by 387 publications

(282 citation statements)

References 29 publications

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“…The reason is that the method offers a systematic design procedure for these systems. Modelling tools such as HYSDEL (hybrid system description language) (Torrisi & Bemporad, 2004) make it easy to generate MLD models suitable for implementation with an MPC control law. This is done by describing the system to be controlled as a discrete time hybrid automaton.…”

Section: Methods A: Finite Horizon Model Predictive Controlmentioning

confidence: 99%

Optimal switching control of burner setting for a compact marine boiler design

Solberg

Andersen

Maciejowski

et al. 2010

Control Engineering Practice

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a b s t r a c tThis paper discusses optimal control strategies for switching between different burner modes in a novel compact marine boiler design. The ideal behaviour is defined in a performance index the minimisation of which defines an ideal trade-off between deviations in boiler pressure and water level from their respective setpoints and the cost of burner switches and variation of continuous input flows. Direct minimisation was found computationally infeasible and two different suboptimal strategies have been considered. The first one is based on the mixed logical dynamical framework. The second approach is based on a generalisation of hysteresis control. The strategies are verified on a simulation model of the compact marine boiler for control of low/high burner load switches.

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Section: Methods A: Finite Horizon Model Predictive Controlmentioning

confidence: 99%

Optimal switching control of burner setting for a compact marine boiler design

Solberg

Andersen

Maciejowski

et al. 2010

Control Engineering Practice

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“…The optimization algorithm is warm-started: the optimal control sequence u * determined at the previous sampling instant is employed to provide an initial guess for the MILP that is currently being solved. The optimal control problem defined in (20) is formulated with the Hybrid Toolbox [31] by expressing the system dynamics in MLD form, using the description language HYSDEL [32]. The resulting MILP is solved using the solver of the Gurobi Optimizer on the control computer, which is a Toshiba Satellite mM840, with Processor Intel Core i5, 4GB RAM running Windows 7.…”

Section: Hybrid Model Predictive Control Designmentioning

confidence: 99%

Optimal energy management of a small-size building via hybrid model predictive control

Khakimova

Kusatayeva

Shamshimova

et al. 2017

Energy and Buildings

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This paper presents the design of a Model Predictive Control (MPC) scheme to optimally manage the thermal and electrical subsystems of a small-size building ("smart house"), with the objective of minimizing the expense for buying energy from the grid, while keeping the room temperature within given time-varying bounds. The system, for which an experimental prototype has been built, includes PV panels, solar collectors, a battery pack, an electrical heater in a thermal storage tank, and two pumps on the solar collector and radiator hydraulic circuits. The presence of binary control inputs together with continuous ones naturally leads to using a hybrid dynamical model, and the MPC controller solves a mixed-integer linear program at each sampling instant, relying on weather forecast data for ambient temperature and solar irradiance. The procedure for controller design is reported with focus on the specific application, and the proposed method is successfully tested on the experimental site.

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“…We consider a continuous-time version of the discrete hybrid automaton (DHA) proposed in [16], denoted as integral continuous (-time) hybrid automaton (icHA), where discrete-time affine dynamics are replaced by integral continuoustime dynamics. Similarly to DHA, the icHA consists of the four components reported in Figure 1: affine system (iSAS), the event generator (EG), the mode selector (MS) and the asynchronous finite state machine (aFSM).…”

Section: Integral Continuous-time Hybrid Automatonmentioning

confidence: 99%

“…q(k) , for instance using the tool Hysdel [16]. Differently from the standard discrete-time MLD system [8], in the eMLD (15) k is an event counter, while time t is an additional state variable.…”

Section: Event-driven Representation Of Ichamentioning

confidence: 99%

Event-driven optimization-based control of hybrid systems with integral continuous-time dynamics

2009

Self Cite

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In this paper we introduce a class of continuous-time hybrid dynamical systems called integral continuous-time hybrid automata (icHA) for which we propose an event-driven optimization-based control strategy. Events include both external actions applied to the system and changes of continuous dynamics (mode switches). The icHA formalism subsumes a number of hybrid dynamical systems with practical interest, e.g., linear hybrid automata. Different cost functions, including minimum-time and minimum-effort criteria, and constraints are examined in the event-driven optimal control formulation. This is translated into a finite-dimensional mixed-integer optimization problem, in which the event instants and the corresponding values of the control input are the optimization variables. As a consequence, the proposed approach has the advantage of automatically adjusting the attention of the controller to the frequency of event occurrence in the hybrid process. A receding horizon control scheme exploiting the event-based optimal control formulation is proposed as a feedback control strategy and proved to ensure either finite-time or asymptotic convergence of the closed-loop.

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HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems

Cited by 387 publications

References 29 publications

Optimal switching control of burner setting for a compact marine boiler design

Optimal switching control of burner setting for a compact marine boiler design

Optimal energy management of a small-size building via hybrid model predictive control

Event-driven optimization-based control of hybrid systems with integral continuous-time dynamics

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