BluSTL: Controller Synthesis from Signal Temporal Logic Specifications

Donzé, Alexandre; Raman, Vasumathi

doi:10.29007/g39q

Cited by 19 publications

(19 citation statements)

References 5 publications

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“…The simulations were run on a Dell Vostro 5471 computer with i7-8550U (4 cores, 1.8-4.0 GHz) processor and 8GB RAM under MAT-LAB R2019b using the BARON 19.3.24 solver [31] and YALMIP version R20200116 [36]. The code for the translation of specifications containing temporal logic expressions to optimization problems was based on the BluSTL toolbox [16].…”

Section: Control Scenariosmentioning

confidence: 99%

Nonlinear model predictive control with logic constraints for COVID-19 management

et al. 2020

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The management of COVID-19 appears to be a long-term challenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we propose a model predictive approach for the constrained control of a nonlinear compartmental model that captures the key dynamical properties of COVID-19. The control design uses the discrete-time version of the epidemic model, and it is able to handle complex, possibly time-dependent constraints, logical relations between model variables and multiple predefined discrete levels of interventions. A state observer is also constructed for the computation of non-measured variables from the number of hospitalized patients. Five control scenarios with different cost functions and constraints are studied through numerical simulations, including an output feedback configuration with uncertain parameters. It is visible from the results that, depending on the cost function associated with different policy aims, the obtained controls correspond to mitigation and suppression strategies, and the constructed control inputs are similar to real-life government responses. The results also clearly show the key importance of early intervention, the continuous tracking of the susceptible population and that of future work in determining the true costs of restrictive control measures and their quantitative effects.

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Section: Control Scenariosmentioning

confidence: 99%

Nonlinear model predictive control with logic constraints for COVID-19 management

et al. 2020

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“…In this study, the TLSs are encoded into a MILP using the toolbox BluSTL [26 ], and the overall problem is converted into a MILP and solved by the relevant solvers in Gurobi [27 ].…”

Section: Voltage Control With Tlssmentioning

confidence: 99%

Model predictive control for voltage restoration in microgrids using temporal logic specifications

Taousser¹,

Olama

Djouadi³

et al. 2020

IET Energy Systems Integration

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Power system operation will encounter numerous voltage variabilities as the proliferation of renewable energy continues. Real‐time monitoring and communication technologies can potentially improve voltage stability by enabling the rapid detection of voltage deviations and the implementation of corrective actions. These corrective actions will only be effective in restoring stability if they are chosen in a timely and scalable manner. This study considers the problem of power systems’ load voltage control in order to simultaneously address both magnitude and time voltage specifications. In order to comply with grid codes and avoid unnecessary relay protection actions, a model predictive control‐based control strategy employing temporal logic specifications (TLSs) is proposed. The TLSs strategy is introduced as a formalism to control the voltage variation at a critical load bus against operational bounds over time. The control objective is to schedule optimal control input signals from the available supportive energy storage systems, which provide reactive power injections, leading to satisfying the specified finite‐time restoration described by the TLSs with minimal control efforts. The simulation results display that the TLSs strategy for power systems’ voltage control synthesis is extremely significant.

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“…The performance measure J(u(·)) can be set as the control effort u(·) 2 or u(·) 1 . For linear systems, the above optimization problem can be converted to a linear program (LP) problem [20], [21] and it can be solved efficiently by LP solvers. As we only focus on the time horizon [0, T end ] (e.g.…”

Section: A Feedforward Controller Synthesismentioning

confidence: 99%

“…For the current mode q and every i q t , , obtain X i q t , [1] according to (20) 3: for each mode q in the order that the nominal trajectories pass do 4:…”

Section: B Feedback Controller Synthesismentioning

confidence: 99%

Coordinated Control of Wind Turbine Generator and Energy Storage System for Frequency Regulation under Temporal Logic Specifications

Julius

Chow

2018

2018 Annual American Control Conference (ACC)

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In this paper, we present a controller synthesis approach for wind turbine generators (WTG) and energy storage systems with metric temporal logic (MTL) specifications, with provable probabilistic guarantees in the stochastic environment of wind power generation. The MTL specifications are requirements for the grid frequency deviations, WTG rotor speed variations and the power flow constraints at different lines. We present the stochastic control bisimulation function, which bounds the divergence of the trajectories of a switched stochastic control system and the switched nominal control system in a probabilistic fashion. We first design a feedforward controller by solving an optimization problem for the nominal trajectory of the deterministic control system with robustness against initial state variations and stochastic uncertainties. Then we generate a feedback control law from the data of the simulated trajectories. We implement our control method on both a four-bus system and a nine-bus system, and test the effectiveness of the method with a generation loss disturbance. We also test the advantage of the feedback controller over the feedforward controller when unexpected disturbance occurs.Keywords-Controller synthesis, differential-algebraic equations, temporal logic, energy storage systems * 1 ∧ [2,Tend]p * 2 ∧ [2,Tend]p * 1 3 , p * 1 3 =(−0.25 + 0.0258e −0.01t ≤ P 28 ≤ 0.25 − 0.0258e −0.01t ).

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BluSTL: Controller Synthesis from Signal Temporal Logic Specifications

Cited by 19 publications

References 5 publications

Nonlinear model predictive control with logic constraints for COVID-19 management

Nonlinear model predictive control with logic constraints for COVID-19 management

Model predictive control for voltage restoration in microgrids using temporal logic specifications

Coordinated Control of Wind Turbine Generator and Energy Storage System for Frequency Regulation under Temporal Logic Specifications

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