State-Space Interpretation of Model Predictive Control

Lee, Jay H.; Morari, Manfred; García, Camino López

doi:10.1007/978-94-011-0135-6_12

Cited by 76 publications

(98 citation statements)

References 14 publications

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“…However, as the name suggests, offline methods lead to overly conservative designs, and they are unsuitable to be employed in real-time as well as in conjunction with the MPC design [13,19].These are usually performed using computer simulations for all time stamps based on the employed scheduling algorithm. As far as Model predictive control is concerned, these offline computer simulations will prove too expensive.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Ever since MPC became popular with the chemical plants and process industries [13], significant research has gone into this area of control theory. However, there is still very little work which studies MPC operating over CAN with online delay analysis.…”

Section: Review Of Literaturementioning

confidence: 99%

“…In particular, the random nature of timing delay of CAN messages will be impossible to capture in such a controller design. Therefore, this work considers Model Predictive Control (MPC), an advanced method that is becoming popular in chemical plants, process industries, and more recently, robotics and automotive [13]. This algorithm predicts optimal control efforts for a small window (prediction horizon) into the future, while implementing the current control effort.…”

Section: Introductionmentioning

confidence: 99%

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Online message delay prediction for model predictive control over controller area network

Rao

Zeng

2017

2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive Control (MPC) algorithm in designing the controller. By simulating an automotive Cruise Control system and a DC Motor plant in a CAN environment, it goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed method on an 8-bit 16MHz work goes on to demonstrate that the delay prediction is accurate, and that the MPC design which takes the message delay into consideration, performs considerably better. It also implements the proposed approach on a low end microcontroller (8bit, 16MHz ATmega328P) and measures the time taken for predicting the delay for each message (execution overhead).The obtained results clearly indicate that the method is computationally feasible for use in a real-time scenario.To my parents, my sister, and my grandparents for the unconditional love and support.iv I would like to extend my special thanks to Dr. Anton Cervin (Lund University) for clarifying queries about TrueTime and Dr. Liuping Wang (RMIT University) for the discussions on MPC which proved invaluable in my research.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online message delay prediction for model predictive control over controller area network

Rao

Zeng

2017

2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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“…Additionally, several control performance indexes are computed for different process configurations and compared in this section. Finally, the paper ends with some conclusions on the analyzed structures [23].…”

Section: Introductionmentioning

confidence: 96%

“…The MPC feedback and feedforward co-design procedure results in a tradeoff, where the controller tuning procedure is carried out taking into account the most important aspects. There exist several MPC formulations that can be used for disturbance compensation analysis providing the specific methodology in accordance with the analyzed features [17,22,23]. In [3,22,24], it was shown that the optimal MPC controller setup for disturbance attenuation results in high bandwidths in the feedback loop, and it is necessary to compensate this issue.…”

Section: Introductionmentioning

confidence: 99%

Measurable Disturbances Compensation: Analysis and Tuning of Feedforward Techniques for Dead-Time Processes

et al. 2016

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Abstract:In this paper, measurable disturbance compensation techniques are analyzed, focusing the problem on the input-output and disturbance-output time delays. The feedforward compensation method is evaluated for the common structures that appear between the disturbance and process dynamics. Due to the presence of time delays, the study includes causality and instability phenomena that can arise when a classical approach for disturbance compensation is used. Different feedforward configurations are analyzed for two feedback control techniques, PID (Proportional-Integral-Derivative) and MPC (Model Predictive Control) that are widely used for industrial process-control applications. The specific tuning methodology for the analyzed process structure is used to obtain improved disturbance rejection performance regarding classical approaches. The evaluation of the introduced disturbance rejection schemes is performed through simulation, considering process constraints in order to highlight the advantages and drawbacks in common scenarios. The performance of the analyzed structure is expressed with different indexes that allow us direct comparisons. The obtained results show that the proper design and tuning of the feedforward action helps to significantly improve the overall control performance in process control tasks.

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Achieving state estimation equivalence for misassigned disturbances in offset‐free model predictive control

2009

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in Wiley InterScience (www.interscience.wiley.com).Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed-loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. Any correlated disturbance that enters the plant and is not included in the model results in biased predicted states. It is customary to assume that the disturbance enters through the inputs or the outputs. The bias can be removed by using the predicted and measured outputs to estimate the disturbance. In the target calculator formulation, the state is augmented with integrating disturbances. The target calculator then ensures offset free control in presence of unmodeled disturbances 4 by shifting the steady state target for the regulator depending on the estimate of the bias (under the assumption that the bias remains constant in the future). The integrating disturbance can be added either to the input, 5,6 the output, 7,8 or a combination of both. 9-11 Most industrial MPC implementations add the bias term to the output. 1,12 In Refs. 10 and 11, rank conditions that should be satisfied for the disturbance models to ensure offset-free control were independently derived, and the lack of consensus in the literature on the choice of the disturbance model was also pointed out. The examples in these references show significant difference in the closed-loop behavior of the controller depending on the choice of the disturbance model. Recently, 13 a method for a combined design of a disturbance model and its observer by solving an appropriate H 1 control problem was presented. The results presented in Ref. 13 differ considerably from the autocorrelation-based technique to be discussed in this article. The autocorrelation-based technique uses steady state operating data to specify the estimator gain as opposed to designing the disturbance models for a specified disturbance.The main contribution of this article is to show that for linear models, the choice of the disturbance model does not affect the performance of the closed-loop when the estimator gain is found from appropriately identified noise covariances. The equivalence in the estimator gains is shown by appealing to realiz...

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State-Space Interpretation of Model Predictive Control

Cited by 76 publications

References 14 publications

Online message delay prediction for model predictive control over controller area network

Online message delay prediction for model predictive control over controller area network

Measurable Disturbances Compensation: Analysis and Tuning of Feedforward Techniques for Dead-Time Processes

Achieving state estimation equivalence for misassigned disturbances in offset‐free model predictive control

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