On real-time robust model predictive control

Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.

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“…The area has been explored especially when these solvers are part of embedded systems [28,31]. For an overview on the matter see [59].…”

Section: Formal Assessmentmentioning

confidence: 99%

Automated control of multiple software goals using multiple actuators

Maggio

Papadopoulos

Filieri

et al. 2017

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

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“…Proof: The first part of the proof assumes w 2 = 0 and follows similar steps as in Section IV to show that V rs * N (x, z r ) is an ISS Lyapunov function with respect to w 1 . A more detailed version of this part of the proof can also be found in [26]. The second part of the proof then shows that V rs * N (x, z r ) is also an ISS Lyapunov function with respect to w 2 .…”

Section: Theorem V4 (Iss Under κmentioning

confidence: 98%

“…Proof: Using Lemma IV.1 and IV.2, it can be shown that V s * N (x, z r ) is a Lyapunov function by following the same arguments as in the hard constrained case presented in [26]. The proof is included in the appendix.…”

Section: Theorem Iv3 (Asymptotic Stability Under κmentioning

confidence: 99%

“…both 1) and 2) hold for max{α 1 , α 2 } ≤ α < 1. Note that 2) is independent of the soft constrained formulation and can be shown as in the hard constrained case in [26]. The proof is included in the appendix for completeness.…”

Section: Lemma Iv2 Letmentioning

confidence: 99%

“…The tracking approach described in Section II was extended to a robust tube-based MPC method for tracking in [23], which will be combined in the following with the soft constrained scheme proposed in Section III. [28], we propose to augment the cost with the term V f (x −x 0 ), which offers the advantage of directly providing an ISS Lyapunov function (Theorem V.4, see also [26], [30] for more details).…”

Section: B Combination Of Robust and Soft Constrained Mpcmentioning

confidence: 99%

See 2 more Smart Citations

Soft Constrained Model Predictive Control With Robust Stability Guarantees

Zeilinger

Morari

Jones

2014

IEEE Trans. Automat. Contr.

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108

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Abstract-Soft constrained MPC is frequently applied in practice in order to ensure feasibility of the optimization during online operation. Standard techniques offer global feasibility by relaxing state or output constraints, but cannot ensure closedloop stability. This paper presents a new soft constrained MPC approach for tracking that provides stability guarantees even for unstable systems. Two types of soft constraints and slack variables are proposed to enlarge the terminal constraint and relax the state constraints. The approach ensures feasibility of the MPC problem in a large region of the state space, depending on the imposed hard constraints, and stability is guaranteed by design. The optimal performance of the MPC control law is preserved whenever all state constraints can be enforced. Asymptotic stability of all feasible reference steadystates under the proposed control law is shown, as well as inputto-state stability for the system under additive disturbances. The soft constrained method can be combined with a robust MPC approach, in order to exploit the benefits of both techniques. The properties of the proposed methods are illustrated by numerical examples.

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A novel electric vehicles fleet control strategy for micro grids with fuzzy based vehicle prioritization considering battery longevity

Ramalingeswar

Subramanian

2021

Int Trans Electr Energ Syst

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Transportation has become one of the major contributors to emissions. Global concerns on emissions reduction have led to the drastic adoption of renewable sources and Electric Vehicles (EV). Stochastic nature is the predominant aspect of renewables for which energy storage is indispensable. The coordination of renewables and EVs storage will bring new opportunities in handling the stochastic nature of heterogeneous entities of Micro Grids (MG). This article proposes a novel EV fleet control strategy for MG Energy Management System (EMS). Here, reducing grid dependency and operating energy cost of the MG are the key objectives. A smart FUZZY based EV prioritization is developed. A probabilistic approach is developed to estimate the chance of EV usage for the smart decision on choosing EVs while supporting EMS. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. Optimal Energy Distribution (OED) is accomplished using Water Filling Algorithm (WFA). The proposed EMS is implemented in a real time on-grid MG scenario hosting solar Photovoltaic (Solar PV), EV fleet, and

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On real-time robust model predictive control

Cited by 109 publications

References 27 publications

Automated control of multiple software goals using multiple actuators

Automated control of multiple software goals using multiple actuators

Soft Constrained Model Predictive Control With Robust Stability Guarantees

A novel electric vehicles fleet control strategy for micro grids with fuzzy based vehicle prioritization considering battery longevity

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