Anil Parsi


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Parsi

First Name

Anil

ORCID

0000-0002-1806-8332

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2020 - 202514
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Publications 1 - 10 of 14
  • A distributed framework for linear adaptive MPC
    Item type: Conference Paper
    Parsi, Anil; Aboudonia, Ahmed; Iannelli, Andrea; et al. (2021)
    2021 60th IEEE Conference on Decision and Control (CDC)
    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 L2 stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.
  • Regularized System Identification: A Hierarchical Bayesian Approach
    Item type: Conference Paper
    Khosravi, Mohammad; Iannelli, Andrea; Yin, Mingzhou; et al. (2020)
    IFAC-PapersOnLine ~ 21st IFAC World Congress
    In this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the estimation problem to the cone of upper triangular matrices with positive diagonal entries. Following this, the hyperprior is introduced on a designed sub-cone of this set. The method differs from the current trend in regularized system identification from various aspect, e.g., the estimation is performed by solving a single stage problem. The MAP estimation problem reduces to a multi-convex optimization problem and a sequential convex programming algorithm is introduced for solving this problem. Consequently, the proposed method is a computationally efficient strategy specially when the regularization matrix has a large size. The method is numerically verified on benchmark examples. Owing to the employed full Bayesian approach, the estimation method shows a satisfactory bias-variance trade-off.
  • Closed-loop design strategies for robust and adaptive model predictive controllers
    Item type: Doctoral Thesis
    Parsi, Anil (2023)
  • Low-Complexity Identification by Sparse Hyperparameter Estimation
    Item type: Conference Paper
    Khosravi, Mohammad; Yin, Mingzhou; Iannelli, Andrea; et al. (2020)
    IFAC-PapersOnLine ~ 21st IFAC World Congress
    This paper presents a novel kernel-based system identification method, which promotes low complexity of the model in terms of the McMillan degree of the system. The regularization matrix is characterized as a linear combination of pre-selected rank-one matrices with unknown hyperparameter coefficients, and the hyperparameters are derived using a maximum a posteriori estimation approach. Each basis matrix is the optimal regularization matrix for a first-order system. With this basis matrix selection, the McMillan degree of the identified model is upper-bounded by the rank of the regularization matrix, which in turn is equal to the cardinality of the hyperparameters. For this reason, a sparsity-promoting prior is chosen for hyperparameter tuning. The resulting optimization problem has a difference of convex program form which can be efficiently solved. The advantages of the proposed method are that the identified model has a low-complexity structure and that an improved bias-variance trade-off is achieved. Numerical results confirm that the proposed method achieves a better bias-variance trade-off as well as a better fit to the model compared to both the empirical Bayes method and the atomic-norm regularization.
  • Active exploration in adaptive model predictive control
    Item type: Conference Paper
    Parsi, Anil; Iannelli, Andrea; Smith, Roy (2020)
    2020 59th IEEE Conference on Decision and Control (CDC)
    A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-space matrices. Online set-membership identification is performed to reduce the uncertainty and thus control affects both the informativity of identification and the system’s performance. The main contribution of the paper is to include this dual effect in the MPC optimization problem using a predicted worst-case cost in the objective function. This allows the controller to perform active exploration, that is, the control input reduces the uncertainty in the regions of the parameter space that have most influence on the performance. Additionally, the MPC algorithm ensures robust constraint satisfaction of state and input constraints. Advantages of the proposed algorithm are shown by comparing it to a passive adaptive MPC algorithm from the literature.
  • Once upon a time step: A closed-loop approach to robust MPC design
    Item type: Journal Article
    Parsi, Anil; Bartos, Marcell; Srivastava, Amber; et al. (2025)
    IEEE Transactions on Automatic Control
    A novel robust model predictive control (MPC) algorithm is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization. The proposed strategy considers the effects of model perturbations and disturbances occurring at only one time step. This is in contrast to existing formulations which compute control policies that are feasible under the worst-case realizations of all model perturbations and exogenous disturbances in the MPC prediction horizon. The proposed method has an online computational complexity similar to nominal MPC methods while guaranteeing constraint satisfaction, recursive feasibility and stability. Numerical simulations demonstrate the efficacy of our proposed approach.
  • Dual adaptive MPC using an exact set-membership reformulation
    Item type: Conference Paper
    Parsi, Anil; Liu, Diyou; Iannelli, Andrea; et al. (2023)
    IFAC-PapersOnLine ~ 22nd IFAC World Congress
    Adaptive model predictive control (MPC) methods using set-membership Identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guarantees robust constraint satisfaction and recursive feasibility. It is shown that method can be implemented using homothetic tube and flexible tube parameterizations of state tubes, and a simulation study demonstrates performance improvement over state-of-the-art controllers.
  • Robust Adaptive Model Predictive Control with Worst-Case Cost
    Item type: Conference Paper
    Parsi, Anil; Iannelli, Andrea; Yin, Mingzhou; et al. (2020)
    IFAC-PapersOnLine ~ 21st IFAC World Congress
    A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identi cation is used to reduce uncertainty in the impulse response. In the MPC scheme, robust constraints are enforced to ensure constraint satisfaction for all the models in the feasible set. The performance objective is formulated as a worst-case cost with respect to the modeling uncertainties. That is, at each time step an optimization problem is solved in which the control input is optimized for the worst-case plant in the uncertainty set. The performance of the proposed algorithm is compared to an adaptive MPC algorithm from the literature using Monte-Carlo simulations.
  • Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
    Item type: Conference Paper
    Yin, Mingzhou; Iannelli, Andrea; Khosravi, Mohammad; et al. (2020)
    IFAC-PapersOnLine ~ 21st IFAC World Congress
    This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
  • Robust Adaptive Model Predictive Control of Quadrotors
    Item type: Conference Paper
    Didier, Alexandre; Parsi, Anil; Coulson, Jeremy; et al. (2021)
    2021 European Control Conference (ECC)
    Robust adaptive model predictive control (RAMPC) is a novel control method that combines robustness guarantees with respect to unknown parameters and bounded disturbances into a model predictive control scheme. However, RAMPC has so far only been developed in theory. The goal of this paper is to apply RAMPC to a physical quadrotor experiment. To the best of our knowledge this is the first time that RAMPC has been applied in practice using a state space formulation. In doing so, we highlight important practical challenges such as computation of λ-contractive polytopes and dealing with measurement noise, and propose modifications to RAMPC so that it can be applied on a quadrotor. We first simulate quadrotor flight with a direct and a decoupled control architecture in different scenarios. The scenarios include: (i) an uncertain quadrotor mass and additive wind disturbance as part of a package delivery problem; and (ii) all rotor efficiencies drop as a power delivery problem. We then implement these scenarios on a physical quadrotor and present the experimental results.
Publications 1 - 10 of 14