Model predictive heuristic control

Richalet, J.; Rault, A.; Testud, J. L.; Papon, J.

doi:10.1016/0005-1098(78)90001-8

Cited by 1,689 publications

(175 citation statements)

References 7 publications

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“…As in the unconstrained predictive control formulation, we consider the alternative deterministic problem (8), where xL (k) satis"es the predictor equation (9). We then have the following Proposition.…”

Section: Constrained Predictive Controlmentioning

confidence: 99%

Disturbance sensitivity issues in predictive control

Doná

Goodwin

1999

Int. J. Adapt. Control Signal Process.

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SUMMARYThe idea of predictive control has been one of the core paradigms in control engineering for over two decades. The current paper brie#y reviews the idea of predictive control and then examines a particular sensitivity issue that arises in its application. Speci"cally, we look at a robustness problem related to di!erent sources of the disturbance, e.g. input disturbance, output disturbance, etc.

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Section: Constrained Predictive Controlmentioning

confidence: 99%

Disturbance sensitivity issues in predictive control

Doná

Goodwin

1999

Int. J. Adapt. Control Signal Process.

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“…g(y, z, u) = 0 [ 8 ] where, y is the differential variable, z is the algebraic variable and u is the manipulated variable. …”

Section: Generic Model Controlmentioning

confidence: 99%

“…1,[2][3][4][5] These models are also highly non-linear in nature, which adds significant challenges to implement control strategies along with the abovementioned issues. Past researchers have shown multivariable control techniques like Dynamic Matrix Control (DMC), 6 Internal Model Control (IMC), 7 and Model Predictive Heuristic Control (MPHC) 8 for a variety of systems. However, these models suffer from their reliability on the linear approximations of the experimentally obtained step-response data and do not directly consider the full nonlinear model explicitly.…”

mentioning

confidence: 99%

Generic Model Control for Lithium-Ion Batteries

Pathak

Kolluri

Subramanian

2017

J. Electrochem. Soc.

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Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained nonlinear optimization. In this work, we present an alternative approach to control batteries called as Generic Model Control, or Reference System Synthesis. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. As compared to the generic model control approaches implemented by previous researchers, we implement the same concept using direct DAE numerical solvers. The results are presented for single input single objective problems, and for constrained problems for various battery models. Lithium-ion batteries are used in a wide range of applications ranging from cell phones to electric vehicles (EVs) to electric grids. With the expected decline in the price of batteries over the next few years, the market for EVs and grids is growing rapidly. Long recharging times, capacity fade and thermal runaway are some of the main issues that need to be addressed in order to use batteries safely and efficiently for a longer time. This necessitates the use of smarter battery management systems that can derive optimal use strategies by exploiting the dynamics of the battery.Battery models based on transport, physical, electrochemical and thermodynamics principles can be used to monitor the internal states of the battery and to obtain optimal control strategies. Such models are computationally expensive which limits their use in control applications in real-time. Various issues, such as the onset of dynamics of some internal states only during use of batteries at high charge/discharge rates, and several other states which are active only while charging or discharging, adds to the challenges of the observability of these physics-based electrochemical models. Hence, various approximated and reduced order models have been proposed in the past, to make the models strongly observable in local intervals. 1,[2][3][4][5] These models are also highly non-linear in nature, which adds significant challenges to implement control strategies along with the abovementioned issues. Past researchers have shown multivariable control techniques like Dynamic Matrix Control (DMC), 6 Internal Model Control (IMC), 7 and Model Predictive Heuristic Control (MPHC) 8 for a variety of systems. However, these models suffer from their reliability on the linear approximations of the experimentally obtained step-response data and do not directly consider the full nonlinear model explicitly. Several researchers have been working to design optimal charging profiles ...

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“…MPC in applications dates to the late 1970's, where it first found use in the process industry and was termed Model Predictive Heuristic Control (MPHC) (Richalet et al, 1976(Richalet et al, , 1978. In this setting, relatively long time scales (e.g.…”

Section: Introductionmentioning

confidence: 99%

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

Bouffard¹

2012

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE DEC 20122. REPORT TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 Copyright © 2012, by the author(s). TITLE AND SUBTITLE On-board Model Predictive Control of a QuadrotorAll rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. AcknowledgementThe This report describes work in applying model predictive control (MPC) techniques to the control of quadrotor helicopters, a type of micro aerial vehicle (MAV) platform that has gained great popularity in recent years both in research and commercial/military settings. MPC is a form of optimal control which is attractive in part because it allows engineering requirements to be addressed directly in the design of the controller in terms of costs to be minimized and constraints to be satisfied in an optimization problem. Furthermore, for many engineering problems of interest, the optimization to be performed is convex, meaning that a global optimum can be efficiently computed. MPC first found broad early application in the process industry, where the typically longer time scales were compatible with the time necessary to solve the optimization problem. More recently with both the exponential increase in available computing power and the development of more efficient solution techniques, MPC has become an option for control of systems with faster dynamics, such as quadrotors.We bring together results from our application of two distinct variants of MPC. The common thread is that we seek advanced control algorithms that can be applied to an autonomous MAV like the quadrotor, ideally without requiring any external resources, i.e. we aim to perform all computations required for real-time closed-loop control on-board the vehicle.The first variant is known as explicit MPC, where in a sense the heavy numerical work of solving optimization problems is done a priori and off-line, such that the on-line implementat...

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Model predictive heuristic control

Cited by 1,689 publications

References 7 publications

Disturbance sensitivity issues in predictive control

Disturbance sensitivity issues in predictive control

Generic Model Control for Lithium-Ion Batteries

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

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