Article
Automation & Control Systems
Alberto Bemporad
Summary: This article proposes an algorithm called PARC for solving multivariate regression and classification problems using piecewise linear predictors. The algorithm alternates between solving ridge regression or softmax regression problems and assigning training points to different clusters based on a criterion. We prove that PARC is a block-coordinate descent algorithm that minimizes an objective function and converges in a finite number of steps.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Xu Cai, Xin Zhang, Xuyang Lou, Wei Wu
Summary: This paper presents a model predictive control strategy for piecewise affine systems with dead zone constraints using the mixed logical dynamical modeling approach. The proposed strategy involves transforming the systems into mixed logic dynamical models and applying a predictive control scheme based on this model. The effectiveness of the approach is demonstrated through numerical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Johannes Koehler, Raffaele Soloperto, Matthias A. Mueller, Frank Allgoewer
Summary: This article introduces a nonlinear robust model predictive control framework for general disturbances, using an online constructed tube to tighten nominal constraints, implicitly including nonlinear functions for efficiency. An offline constructed function is used to estimate worst case disturbance, ensuring robust constraint satisfaction and practical asymptotic stability compared to nominal MPC approaches.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Fei Li, Huiping Li, Shaoyuan Li, Yuyao He
Summary: This article presents an online learning stochastic model predictive control method for linear uncertain systems. The proposed method utilizes probabilistic reachable sets as time-varying tubes to embody the chance constraints by forecasting the variance propagation of uncertainty via Gaussian process regression. The algorithm trains the Gaussian process model of uncertainty online by refining the active data dictionary and selects data points from the raw data around the predicted optimal nominal trajectories to reduce computational load and preserve control performance.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Bo Cheng
Summary: This article proposes an offline control algorithm called recurrent model predictive control to solve large-scale nonlinear optimal control problems. The algorithm approximates the optimal policy using a recurrent function and adapts the model prediction time based on current computing resources to improve policy performance.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Tong Liu, Martin Buss
Summary: This article investigates the output feedback-based direct model reference adaptive control of piecewise affine systems and its parameter convergence. It is shown that under the slow switching assumption, all the closed-loop signals are bounded and the output tracking error is small in the mean square sense. The estimation error of controller parameters is proven to converge to a residual set if the input signal is sufficiently rich.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Acoustics
Yahay Abbasi, Hamidreza Momeni, Amin Ramezani
Summary: This study introduces an algorithm based on robust tube-based model predictive control strategy to enlarge the region of attraction without increasing the prediction horizon by changing the terminal constraint set for piecewise affine systems with bounded additive disturbances. The proposed algorithm effectively expands the region of attraction without adding computational complexity of increasing receding horizon, as demonstrated through simulation examples including two different case studies.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Computer Science, Theory & Methods
Yuying Dong, Yan Song, Guoliang Wei
Summary: This paper discusses the membership-function-dependent model predictive control (MPC) problem for a class of Takagi-Sugeno (T-S) fuzzy systems with hard constraints. By converting the original T-S fuzzy systems into a piecewise-fuzzy model and using staircase membership functions, the conservatism of the controller design can be reduced, and errors between the staircase and continuous membership functions are taken into account for feasibility investigation and stability analysis.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Yuying Dong, Yan Song, Guoliang Wei
Summary: This paper focuses on membership-function-dependent model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy systems with hard constraints. By converting the original T-S fuzzy systems into piecewise-fuzzy models and using staircase membership functions, the conservatism of controller design can be reduced. An online optimization problem based on membership-function-dependent terminal constraint set is proposed, along with the consideration of errors between staircase membership functions and original continuous membership functions.
FUZZY SETS AND SYSTEMS
(2022)
Article
Automation & Control Systems
Nard Strijbosch, Geir E. Dullerud, Andrew R. Teel, W. P. M. H. Heemels
Summary: This study examines the stability and L-2-gain properties of a class of hybrid systems with time-varying linear flow dynamics, periodic time-triggered jumps, and arbitrary nonlinear jump maps. New concepts of stability and contractivity are introduced, with formal relationships being derived between them, showing that some are stronger than others. The results indicate that analyzing different discrete-time nonlinear systems than in existing works can lead to stronger conclusions on the L-2-gain.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Fei Li, Huiping Li, Yuyao He
Summary: This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The methods convert chance constraints into deterministic form and propose soft constraints to enhance feasibility. Numerical simulations demonstrate the effectiveness of these methods.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Shijia Fu, Haoyuan Sun, Honggui Han
Summary: This paper presents a data-driven model predictive control strategy for stabilizing unknown nonlinear systems with aperiodic sampling. The proposed method approximates the dynamics of the system using a linearized polytopic approximation dynamic and designs a data-driven controller to solve the optimal steady-state and control problems sequentially, enabling online tracking of the desired output.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Dongting Li, Rui-Qi Dong, Yanning Guo, Guangtao Ran, Dongyu Li
Summary: This article introduces a line-of-sight (LOS)-Euler rendezvous and docking (RVD) framework for docking with a tumbling target under various RVD constraints. The framework uses a double-loop control scheme to control the chaser's position and attitude to track the target's docking port and rotation. The proposed framework linearly describes the complex couplings between the position and attitude control and the RVD constraints.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Tong Liu, Yufeng Gao, Martin Buss
Summary: This article investigates the adaptive output tracking control for multi-input-multi-output piecewise affine systems with prescribed performance. Novel common Lyapunov functions are established to ensure the stability of the closed-loop system under arbitrary switching, and the effectiveness of both control methods is validated through numerical simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Construction & Building Technology
Qiong Chen, Nan Li
Summary: The research developed a physics-based model for radiant ceiling cooling systems and applied model predictive control to improve zone air temperature control, achieving better thermal comfort and energy efficiency. By simplifying the system model and utilizing advanced control strategies, the proposed approach demonstrated significant potential for enhanced performance compared to traditional control methods.
BUILDING AND ENVIRONMENT
(2021)
Article
Automation & Control Systems
Ugo Rosolia, Yingzhao Lian, Emilio T. T. Maddalena, Giancarlo Ferrari-Trecate, Colin N. N. Jones
Summary: In this technical article, the performance improvement and optimality properties of the learning model predictive control (LMPC) strategy for linear deterministic systems are analyzed. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. It is shown that, when a linear independence constraint qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Junyi Zhai, Yuning Jiang, Yuanming Shi, Colin N. Jones, Xiao-Ping Zhang
Summary: This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. It presents a novel distributionally robust joint chance-constrained (DRJCC) dispatch model for ITD systems via asynchronous decentralized optimization. The proposed model uses data-driven DRJCC models for transmission and distribution systems and adopts a combined Bonferroni and conditional value-at-risk approximation for the joint chance constraints. A tailored asynchronous alternating direction method of multipliers (ADMM) algorithm is proposed to better adapt to the star topological ITD systems.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Junyi Zhai, Yuning Jiang, Jianing Li, Colin N. Jones, Xiao-Ping Zhang
Summary: This paper investigates the distributed adjustable robust optimal power and gas flow (OPGF) model for integrated electricity and natural gas systems (IEGS). Linear decision rules (LDRs) are used to propose an improved adjustable robust model that can effectively deal with renewable energy uncertainty and reduce solution conservatism. Two tailored alternating direction method of multipliers (ADMM) based distributed optimization frameworks are presented to preserve information privacy and decision-making independence of subsystems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Junyi Zhai, Sheng Wang, Lei Guo, Yuning Jiang, Zhongjian Kang, Colin N. Jones
Summary: This paper focuses on the distributionally robust energy management problem for Multi-energy microgrid (MEMG). A data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed, utilizing various flexible resources in different energy sectors for uncertainty mitigation. An optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is used to make the model tractable, and an iterative sequential convex optimization algorithm is tailored to reduce solution conservatism by tuning OCA. Numerical results demonstrate the effectiveness of the proposed model.
Article
Engineering, Electrical & Electronic
Junyi Zhai, Xinliang Dai, Yuning Jiang, Ying Xue, Veit Hagenmeyer, Colin Jones, Xiao-Ping Zhang
Summary: The paper presents a nonconvex distributed optimization algorithm tailored for addressing the hybrid AC/DC OPF problem in power grids with VSC-HVDC technology, outperforming existing ADmm in convergence speed and numerical robustness.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Peng Yang, Yuning Jiang, Ting Wang, Yong Zhou, Yuanming Shi, Colin N. Jones
Summary: This paper proposes a novel over-the-air second-order federated optimization algorithm to reduce communication rounds and enable low-latency global model aggregation in wireless networks. It utilizes the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm. Numerical results demonstrate the system and communication efficiency of this algorithm compared to existing approaches.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Paul Scharnhorst, Emilio T. Maddalena, Yuning Jiang, Colin N. Jones
Summary: This study considers the problem of estimating out-of-sample bounds for an unknown ground-truth function. The main framework used is kernels and their associated Hilbert spaces, along with an observational model that includes bounded measurement noise. The noise can come from any compactly supported distribution, and no independent assumptions are made about the available data. The study shows how solving parametric quadratically constrained linear programs can compute tight, finite-sample uncertainty bounds. The properties of the approach are established and its relationship with another method is studied through numerical experiments.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Yingzhao Lian, Jicheng Shi, Manuel Koch, Colin Neil Jones
Summary: Data-driven control approaches have the potential to significantly reduce the energy consumption and cost of advanced control in the building sector. This article proposes a systematic method to handle measurement and process noise, and extends data-driven control schemes to adaptive building control. The proposed scheme has been validated through simulation and real-world experiments, where it achieved a significant improvement in energy efficiency while ensuring occupant comfort.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Yingzhao Lian, Jicheng Shi, Colin N. Jones
Summary: Data-driven control offers an alternative approach and facilitates the rapid development of controllers. Preprocessing of raw data is necessary to account for measurement noise and inconsistencies in order to maintain consistency with underlying physical laws. This letter presents a physics-based filter to achieve this and demonstrates its effectiveness through practical applications using real-world datasets collected on the EPFL campus.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Proceedings Paper
Automation & Control Systems
Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena, Jicheng Shi, Colin N. Jones
Summary: This manuscript offers the perspective of experimentalists on several modern data-driven techniques for building control, including model predictive control based on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. The techniques are compared in terms of data requirements, ease of use, computational burden, and robustness in real-world applications. The goal is to assist others in identifying the most suitable technique for their own problems.
2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC)
(2022)
Article
Automation & Control Systems
Jicheng Shi, Yingzhao Lian, Colin N. Jones
Summary: This letter proposes a data-driven method for input reconstruction that recursively estimates unknown inputs based on output measurements and asymptotically converges to the true inputs. An experimental study demonstrates the efficacy of this method for estimating building occupancy based on measured carbon dioxide levels.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Automation & Control Systems
Yingzhao Lian, Yuning Jiang, Colin N. Jones, Daniel F. Opila
Summary: This letter proposes a scheduling process to control the power demand of smart home appliances and solves the difficulty of scheduling deterministic load profiles. The efficacy of the proposed scheme is validated through numerical comparison and a case study.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Automation & Control Systems
Yingzhao Lian, Yuning Jiang, Naomi Stricker, Lothar Thiele, Colin N. Jones
Summary: This letter proposes a robust self-triggered model predictive control approach for optimizing control objectives under limited resources. It introduces a novel zero-order-hold aperiodic discrete-time feedback control law that ensures robust constraint satisfaction for continuous-time linear systems.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Automation & Control Systems
Yingzhao Lian, Yuning Jiang, Naomi Stricker, Lothar Thiele, Colin N. Jones
Summary: This letter discusses the control of uncertain systems under limited resource factors and proposes a resource-aware stochastic predictive control scheme to tackle challenges in such environments. The proposed scheme includes a novel zero-order hold feedback control to accommodate time-inhomogeneous predictive control updates.
IEEE CONTROL SYSTEMS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Emilio T. Maddalena, Martin W. F. Specq, Viviane L. Wisniewski, Colin N. Jones
Summary: The paper investigates piecewise-affine neural networks for optimizing the performance of power electronics devices, simplifying controllers and enabling low-cost implementation on commercial hardware. It focuses on enhancing the start-up transient response of step-down dc-dc converters while also satisfying inductor current constraints.
IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY
(2021)