Active exploration in adaptive model predictive control

Parsi, Anilkumar; Iannelli, Andrea; Smith, Roy S.

doi:10.1109/cdc42340.2020.9304303

Cited by 5 publications

(6 citation statements)

References 18 publications

(26 reference statements)

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“…In this paper, we use a similar approach to that followed in [28] and impose terminal constraints to make sure that X N |k lies inside an invariant set. As the controller in [28] is designed to perform regulation, the center z N |k was set as the origin and α N |k is bounded by a precomputed constant ᾱ. However, a different strategy must be employed for reference tracking to account for the uncertainty in the input setpoint.…”

Section: Terminal Setmentioning

confidence: 99%

See 1 more Smart Citation

An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

Parsi

Iannelli

Smith

2023

IEEE Trans. Automat. Contr.

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A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. A suboptimal solution is proposed using a model predictive control (MPC) based explicit dual control approach which enables active uncertainty learning. A novel algorithm for the design of robustly invariant online terminal sets and terminal controllers is presented. Set membership identification is used to update the parameter uncertainty online. A predicted worst-case cost is used in the MPC optimization problem to model the dual effect of the control input. The cost-to-go is estimated using contractivity of the proposed terminal set and the remaining time horizon, so that the optimizer can estimate future benefits of exploration. The proposed dual control algorithm ensures robust constraint satisfaction and recursive feasibility, and navigates the exploration-exploitation trade-off using a robust performance metric.

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Section: Terminal Setmentioning

confidence: 99%

“…In [28], we proposed an extension to [22] which introduces dual actions in AMPC by means of a predicted state tube while guaranteeing constraint satisfaction. The problem considered was again only a regulation one, and was implicitly framed in an infinite horizon setting.…”

Section: Introductionmentioning

confidence: 99%

An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

Parsi

Iannelli

Smith

2023

IEEE Trans. Automat. Contr.

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“…. , Λ j N −1|k } v1 j=1 needed for nominal tube containment (13), which is expressed by constraints (24h), (24i), and (24j). The cost function expectation (24a) is taken with respect to a predicted noise sequence…”

Section: Assumption 2 (Terminal Set For Nominal Tube) There Exists a ...mentioning

confidence: 99%

“…For instance, early results have considered a state and input dependent term [3], [4], or alternatively stochastic multiplicative and additive disturbances [5]- [7]. Under the same assumptions, recent efforts have been made to improve control performance by including model learning [8]- [10], or parameter adaptation [11], [12], and dual actions [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Stochastic MPC with robustness to bounded parametric uncertainty

Arcari¹,

Iannelli²,

Carron³

et al. 2022

Preprint

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The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robustly treat all possible sources of uncertainty, such as model inaccuracies or external disturbances. This, however, can result in overly conservative control strategies. In this paper, we present a stochastic model predictive control approach for discrete-time LTI systems subject to bounded parametric uncertainty and potentially unbounded stochastic additive noise. The proposed scheme makes use of homothetic tubes along the prediction horizon for a robust treatment of parametric uncertainty. Stochastic noise is handled by non-conservatively tightening constraints using the concept of probabilistic reachable sets (PRS). In order to accommodate all possible parametric uncertainties, we provide a strategy for generating "robustified" PRS based only on first and second moments of the noise sequence. In the case of quadratic cost functions, and under a further i.i.d. assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state. Finally, we demonstrate our scheme on both an illustrative example, and in a building temperature control problem.

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“…Adaptive MPC (AMPC) is a technique which ensures robustness while updating the uncertainty using online identification (e.g. see [2], [3], [4]). In AMPC, uncertainty is captured by a parameteric state space model.…”

Section: Introductionmentioning

confidence: 99%

A distributed framework for linear adaptive MPC

Parsi

Aboudonia

Iannelli

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain 2 stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.

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Active exploration in adaptive model predictive control

Cited by 5 publications

References 18 publications

An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

Stochastic MPC with robustness to bounded parametric uncertainty

A distributed framework for linear adaptive MPC

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