Tube-Based Robust Economic Model Predictive Control on Dissipative Systems with Generalized Optimal Regimes of Operation

Dong, Zihang; Angeli, David

doi:10.1109/cdc.2018.8619325

Cited by 15 publications

(16 citation statements)

References 12 publications

(27 reference statements)

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“…As a result, it is in particular not possible to transmit and apply an updated version of the error feedback in each time step, as is done in classical tube-based MPC (cf. [13], [18], [19]).…”

Section: B Error Feedback and Bound For Control Errormentioning

confidence: 99%

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Uncertainties and output feedback in rollout event-triggered control

Wildhagen¹,

Allgöwer²

2021

Preprint

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The fact that event-triggered control (ETC) often exhibits an improved performance-communication tradeoff over time-triggered control renders it especially useful for Networked Control Systems (NCSs). However, it has proven difficult to characterize the traffic produced by ETC a priori, making it hard to adequately dimension the communication system. Rollout ETC addresses this issue by using a triggering and control law that is implicitly defined by the solution to a receding-horizon optimal control problem (OCP), instead of an explicit one as in classical ETC. This allows to directly incorporate predefined constraints on the transmission traffic as well as on states and inputs. In this article, we examine the practically relevant case when output instead of state measurements are available for feedback, and measurements as well as the plant are subject to uncertainties. To address these challenges, we adapt methods from robust tube-based model predictive control and propose three different strategies to implement an error feedback in an NCSs setup, the applicability of which depends on the capabilities of the actuator. We establish recursive feasibility of the OCP, satisfaction of state and input constraints despite the uncertainties, and convergence. Finally, we illustrate our results in a numerical example.

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Section: B Error Feedback and Bound For Control Errormentioning

confidence: 99%

“…Remark 11. The term λ is included in the objective function to establish convergence later, where the analysis relies on results from tube-based economic MPC [18], [19]. It is indeed essential for this guarantee, since the stage cost (22) is not positive definite w.r.t.…”

Section: B Robust Rollout Etc Algorithmmentioning

confidence: 99%

Uncertainties and output feedback in rollout event-triggered control

Wildhagen¹,

Allgöwer²

2021

Preprint

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“…where d t = [z 0:t+N |t , v t:t+N −1|t , γ t:t+N |t ] is the vector of decision variables, J N is the economic objective function with initial and terminal penalty costs λ and V f , and set X f is an appropriate time-varying terminal constraint set. Note that the initial weighting function is the storage function defined in Assumption 2, and it has been verified that this function is crucial in the proof of stability as shown in [16]. This optimization problem utilizes the full past information, which is not practically realizable as the number of decision variables increases linearly with time.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…A preliminary work on tube EMPC was presented in [16]. This, however, was based on exact availability of state measurements and a state feedback control policy was adopted to achieve closed-loop asymptotic stability and robust bounds on the long run average cost.…”

Section: Introductionmentioning

confidence: 99%

Homothetic tube-based robust offset-free economic Model Predictive Control

Dong

Angeli

2020

Automatica

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“…In [13], a min-max robust EMPC algorithm was proposed to address transmission delays in networked control systems. Tube-based formulations with and without stochastic information have also been proposed [14,15,16]. However, they either use a min-max optimization approach or use the nominal model with tightened invariant constraints.…”

Section: Introductionmentioning

confidence: 99%

Robust economic model predictive control with zone tracking

Decardi‐Nelson¹,

Liu²

2021

Preprint

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This paper presents a robust economic model predictive control (EMPC) formulation with zone tracking for discrete-time uncertain nonlinear systems. The proposed design ensures that the zone tracking objective is achieved in finite steps and at the same time optimizes the economic performance. In the proposed design, instead of tracking the original target zone, a robust control invariant set within the target zone is determined and is used as the actual zone tracked in the proposed EMPC. This approach ensures that the zone tracking objective is achieved within finite steps and once the zone tracking objective is achieved (the system state enters the robust control invariant set), the system state does not come out of the target zone anymore. To optimize the economic performance within the zone in the presence of disturbances, we introduce the notion of risk factor in the controller design. An algorithm to determine the economic zone to be tracked is provided. The risk factor determines the conservativeness of the controller and provides a way to tune the EMPC for better economic performance. A nonlinear chemical example is presented to demonstrate the performance of the proposed formulation.

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Tube-Based Robust Economic Model Predictive Control on Dissipative Systems with Generalized Optimal Regimes of Operation

Cited by 15 publications

References 12 publications

Uncertainties and output feedback in rollout event-triggered control

Uncertainties and output feedback in rollout event-triggered control

Homothetic tube-based robust offset-free economic Model Predictive Control

Robust economic model predictive control with zone tracking

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