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Robust MPC with recursive model update

AUTOMATICA 2019年 103卷 461-471页

作者： Lorenzen, Matthias Cannon, Mark Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Univ Oxford Dept Engn Sci Oxford England

Robust constrained control of linear systems with parametric uncertainty and additive disturbance is addressed. The main contribution is the introduction of a mathematically rigorous and computationally tractable framework for stabilizing model predictive control with online parameter estimation to improve performance and reduce conservatism. Requirements for closed-loop stability and provable constraint satisfaction are considered separately, resulting in the use of online set-membership system identification combined with homothetic prediction tubes for robust constraint satisfaction, and an i optimal point estimate of the unknown parameters to achieve a finite closed-loop gain from the disturbance to the state. Extensions to time-varying parameters and persistently exciting inputs to guarantee parameter convergence are presented. A numerical example illustrates the proven properties and efficacy of the approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： model predictive control adaptive model predictive control Receding horizon control control of constrained systems adaptive control Uncertain linear systems System identification

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Convergence of a Stochastic adaptive MPC

Convergence of a Stochastic Adaptive MPC

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第40届中国控制会议

作者： Hui Chen Lei Guo Academy of Mathematics and Systems Science Chinese Academy of Sciences

This paper presents an adaptive model predictive control（MPC） for discrete-time linear stochastic systems. The proposed adaptive MPC is designed by combining the weighted least squares（WLS） estimation algorithm with the receding horizon LQG control. For unconstrained systems, it is proven that the closed-loop system with the proposed adaptive MPC is stable under some natural conditions on the true systems. Moreover, by adding a diminishing excitation to the adaptive MPC, the parameters converge to the true ones and the performance of the system can be optimized asymptotically.

关键词： adaptive model predictive control Weighted Least-Squares Stochastic Linear Systems

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adaptive personalized multivariable artificial pancreas using plasma insulin estimates

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JOURNAL OF PROCESS control 2019年 80卷 26-40页

作者： Hajizadeh, Iman Rashid, Mudassir Samadi, Sediqeh Sevil, Mert Hobbs, Nicole Brandt, Rachel Cinar, Ali IIT Dept Chem & Biol Engn Chicago IL 60616 USA IIT Dept Biomed Engn Chicago IL 60616 USA

An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma insulin estimates is proposed to efficiently accommodate major disturbances to the blood glucose concentration, such as meal and physical activity. Accurate adaptive glycemic models are developed through a recursive subspace identification technique with wearable physiological measurements and estimates of unannounced meal effect and plasma insulin concentration (PIC) along with continuous glucose concentration signals to characterize the glucose concentration dynamics under various conditions such as food consumption and physical activity. The identified models with time-varying parameters are employed in the design of an adaptive model predictive control (MPC) system that is cognizant of the PIC. The adaptive controller parameters, dynamic PIC constraint, addition of physiological measurements from wearable devices, feature variables generated from the glucose measurements, and estimation of uncertain model parameters, including the meal effect, enable the mAP system to effectively compute the optimal insulin infusion over diverse diurnal variations without meal and exercise announcements. Simulation case studies using a multivariable simulator demonstrate the efficacy of the proposed mAP system. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multivariable artificial pancreas adaptive model predictive control Subspace methods Recursive system identification

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Unbiased-estimation-based and computation-efficient adaptive MPC for four-wheel-independently-actuated electric vehicles

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MECHANISM AND MACHINE THEORY 2020年 154卷 104100-104100页

作者： Wu, Jianyang Wang, Zhenpo Zhang, Lei Beijing Inst Technol Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China Beijing Inst Technol Natl Engn Lab Elect Vehicles Sch Mech Engn Beijing 100081 Peoples R China

In this paper, an adaptive model predictive control (AMPC) scheme with high computational efficiency is developed to improve the yaw stability for four-wheel-independently-actuated electric vehicles (FWIA EVs). A novel vehicle model is first established based on an autoregressive with exogenous input (ARX) model, which is independent of vehicle parameters and road conditions. The time-varying model parameters are identified by an unbiased estimation system via an instrumental variable (IV) method. The AMPC scheme is proposed based on the ARX vehicle model for direct yaw moment control (DYC). Then, a multi-objective optimization method is proposed to optimize torque allocation for yaw stability enhancement. Finally, the performance of the proposed scheme is verified under the double lane change and slalom maneuvers in Carsim. Simulation results show that the ARX-model-based unbiased estimation can effectively follow the reference while filtering out measurement noises. The yaw rate signal is smoother and the computational time is reduced by half under the proposed AMPC scheme in comparison to that under conventional dynamics-model-based MPC. In the meantime, the vehicle slip angle and the steering wheel angle are reduced, which indicates improved vehicle stability. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： adaptive model predictive control Autoregressive with exogenous input (ARX) model Unbiased estimation Yaw stability control

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Integrating MPC with Learning-Based and adaptive Methods to Enhance Safety, Performance and Reliability in Automated Insulin Delivery 1

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IFAC-PapersOnLine 2020年第2期53卷 16149-16154页

作者： Iman Hajizadeh Mohammad Reza Askari Ranjeet Kumar Victor M. Zavala Ali Cinar Department of Chemical and Biological Engineering Illinois Institute of Technology Chicago IL USA Department of Chemical and Biological Engineering University of Wisconsin-Madison Madison WI USA

In this work, an adaptive-learning model predictive control (AL-MPC) framework that integrates disturbance forecasting, uncertainty quantification, learning, and recursive subspace identification is developed. The proposed technique can be used for continuous systems affected by repetitive disturbances with unknown periods. The AL-MPC integrates online learning from historical data to anticipate impending disturbances and proactively counteract their effects to an adaptive MPC. This is done by using machine learning to quantify the significant disturbances from historical data and forecast their future evolution time series. Behavior patterns of the system can be identified from historical data, and the set-point, objective function weights, and constraints of the controller can be modified in advance for the anticipated time periods of the disturbance effects. AL-MPC is used to regulate glucose concentration (GC) in people with diabetes by automated insulin delivery. Simulation results indicate that the AL-MPC can regulate the BGC 75.4% of the simulation time in the target range (70-180) mg/dL without causing any hypoglycemia and hyperglycemia events.

关键词： adaptive model predictive control Diabetes Automated insulin delivery Machine learning Recursive system identification

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adaptive control of Meniscus Velocity in Continuous Caster based on NARX Neural Network model 13th

Adaptive Control of Meniscus Velocity in Continuous Caster b...

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13th International-Federation-of-Automatic-control (IFAC) Workshop on adaptive and Learning control Systems (ALCOS)

作者： Abouelazayem, Shereen Glavinic, Ivan Wondrak, Thomas Hlava, Jaroslav Tech Univ Liberec Studentska 1402-2 Liberec Czech Republic Helmholtz Zentrum Dresden Rossendorf Bautzner Landstr 400 Dresden Germany

Meniscus velocity in continuous casting is critical in determining the quality of the steel. Due to the complex nature of the various interacting phenomena in the process, designing model-based controllers can prove to be a challenge. In this paper a NARX neural network model is trained to describe the complex relationship between the applied current to an Electromagnetic Brake (EMBr) and the measured meniscus velocity. The data for the model is obtained using a laboratory scale continuous casting plant. adaptive model predictive control (MPC) was used to deal with the non-linearity of the model by adapting the prediction model to the different operating conditions. The controller uses the EMBr as an actuator to keep the meniscus velocity within the optimum range, and reject disturbances that occur during the casting process such as changing the casting speed. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Continuous casting process control Meniscus Velocity Neural Network ARX model adaptive model predictive control

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adaptive control of Meniscus Velocity in Continuous Caster based on NARX Neural Network model

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IFAC-PapersOnLine 2019年第29期52卷 222-227页

作者： Shereen Abouelazayem Ivan Glavinić Thomas Wondrak Jaroslav Hlava Technical University of Liberec Studentská 1402/2 Liberec Czech Republic Helmholtz-Zentrum Dresden - Rossendorf Bautzner Landstraße 400 Dresden Germany

关键词： Continuous casting process control Meniscus Velocity Neural Network ARX model adaptive model predictive control

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adaptive MPC for Uncertain Discrete-Time LTI MIMO Systems with Incremental Input Constraints

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IFAC-PapersOnLine 2018年第1期51卷 329-334页

作者： Dhar, Abhishek Bhasin, Shubhendu Department of Electrical Engineering Indian Institute of Technology Hauz Khas New Delhi110016 India Department of Electrical Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India

In this paper, an adaptive model predictive control (MPC) strategy is proposed for controlling a discrete-time linear MIMO system with parametric uncertainties and subjected to actuator constraints. Compared to previous results in literature which either solve the constrained MPC problem for stable uncertain systems or the unconstrained MPC problem for unstable uncertain systems, this result, presents a solution approach for constrained MPC problems for fully uncertain and unstable systems. An adaptive law, designed to update the estimated parameters of the plant, is combined with a constrained MPC for an estimated system. A sufficient condition is imposed on the adaptation gain to account for feasibility of the MPC optimization problem in the presence of the actuator constraint. Stability analysis of the closed loop system with the proposed adaptive MPC strategy has been shown to guarantee the ultimate boundedness of the parameter estimation errors and boundedness as well as asymptotic convergence of the tracking errors to zero. © 2018

关键词： adaptive control systems Actuators Closed loop systems Discrete time control systems MIMO systems model predictive control Parameter estimation predictive control systems System stability Uncertain systems Actuator constraints adaptive control adaptive model predictive control Asymptotic convergence Input constraints Optimization problems Parameter estimation errors Parametric uncertainties

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adaptive predictive Energy Management Strategy for Integrating Intermittent Resources in Power Systems

Adaptive Predictive Energy Management Strategy for Integrati...

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53rd Annual Meeting of the IEEE-Industry-Applications-Society (IAS)

作者： Saad, Ahmed A. Youssef, Tarek Amin, Amr M. Abdallah, Omar H. Mohammed, Osama Florida Int Univ Energy Syst Res Lab Miami FL 33199 USA

ISBN: (纸本)9781538645369

In this paper, we develop a power system adaptive and predictive management strategy for integrating large-scale renewable energy penetration in the power systems. The variability and uncertainty of renewable energy increase the difficulty of the power system management extremely. Also, the problem becomes more challenging if the variable energy has large penetration. The proposed strategy depends on robust optimization under unforeseen contingencies and uncertain changes in intermittent resources output. adaptive model predictive control is used to manipulate the power system energy unit's set-points to maximize profit at maximum security. Simulation studies are conducted to validate the effectiveness of the introduced strategy. The results show that the proposed strategy is successfully able to maximize profit not only in normal operation case but also in the case of severe contingencies. In addition to, the management strategy is flexible for customized plans of the grid operator and it is scalable for system extensions. The developed strategy helps the management system to increase the free renewable energy sources share at the minimum cost with maximum security.

关键词： Energy Management Intermittent Resources adaptive model predictive control Energy Storage Systems

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Design and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system

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APPLIED ENERGY 2017年 203卷 549-559页

作者： Hernandez, Andres Desideri, Adriano Gusev, Sergei Ionescu, Clara M. Van Den Broek, Martijn Quoilin, Sylvain Lemort, Vincent De Keyser, Robin Univ Ghent Dept Elect Energy Met Mech Construct & Syst Dynam Syst & Control Lab Technol Pk 914 B-9000 Ghent Belgium Univ Liege Energy Syst Res Unit Thermodynam Lab Campus Sart Tilman B49 B-4000 Liege Belgium Univ Ghent Dept Flow Heat & Combust Mech Graaf Karel de Goedelaan 5 B-8500 Kortrijk Belgium Univ Ibague Fac Ingn Programa Ingn Elect Carrera 22 Calle 67 Ibague 730001 Colombia

Increasing the energy efficiency of industrial processes is a challenge that involves, not only improving the methodologies for design and manufacturing, but optimizing performance during part-load operation and transient conditions. A well-adopted solution consists of developing waste heat recovery (WHR) systems based on Organic Rankine Cycle (ORC) power units. The highest efficiency for such cycle is obtained at low superheating values, corresponding to the situation where the system exhibits time-varying nonlinear dynamics, triggered by the fluctuating nature of the waste heat source. In this paper, an adaptive control law using the model predictive control (MPC) framework is proposed. This work goes a step beyond most of the existing scientific works in the field of ORC power systems, since the MPC controller is implemented in a lab-scale prototype, and its performance compared against a gain-scheduled PID strategy. The experimental results show that the adaptive MPC outperforms the gain-scheduled PID based strategy, as it allows to accurately regulate the evaporating temperature, while keeping vapor condition at the inlet of the expander i.e., the superheating, in a safe operating range, thus increasing the net power generation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： adaptive model predictive control Organic Rankine Cycle Energy efficiency Waste heat recovery

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