Implementation of Model Predictive Controllers in Programmable Logic Controllers using IEC 61131-3 standard

Krupa, Pablo; Limón, Daniel; Álamo, T.

doi:10.23919/ecc.2018.8550126

Cited by 17 publications

(19 citation statements)

References 16 publications

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“…The system is simulated by numerically integrating (21). The MPCT controller uses (22) as its prediction model. The initial state of the non-linear model is x = (0, 0, 20) (for the prediction model this corresponds to x = (0, 0, 1)) and the reference is given by xr = 0, ur = 0.…”

Section: Closed-loop Simulation Resultsmentioning

confidence: 99%

“…Some noteworthy examples of this approach being used to implement MPC controllers in embedded systems are [14,15,16] for implementations in FPGAs, [17] for microcontrollers and [18,19,20,21] for PLCs. Specifically, the authors proposed in [18] and [22] an implementation approach for MPC controllers in PLCs which takes advantage of the structure of the matrices of the QP problem. This led to the development of a sparse optimization algorithm whose memory requirements grow linearly with the prediction horizon of the MPC controller.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

Krupa,

Alvarado,

Limon

et al. 2020

Preprint

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This article presents an implementation of a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control for tracking formulation in embedded systems. The algorithm is based on an extension of the alternating direction method of multipliers to problems with three separable functions in the objective function. One of the main advantages of the proposed algorithm is that its memory requirements grow linearly with the prediction horizon of the controller. Its sparse implementation is attained by identification of the particular structure of the optimization problem, and not by employing the common sparse algebra techniques, leading to a very computationally efficient implementation. We describe the controller formulation and provide a detailed description of the proposed algorithm, including its pseudocode. We also provide a simple (and sparse) warmstarting procedure that can significantly reduce the number of iterations. Finally, we show some preliminary numerical results of the performance of the algorithm.

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Section: Closed-loop Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

Krupa,

Alvarado,

Limon

et al. 2020

Preprint

Self Cite

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“…The restart schemes shown are E f c (6) and E g c (7) from [15], restart condition E * c (8) [19], and the restart condition 9) proposed in this paper (using Algorithm 3). Additionally, we show the results of applying FISTA algorithm without using a restart scheme.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The resulting algorithm is commonly known as FISTA algorithm [4]. Because of its complexity certification, it is often used in the context of embedded model predictive control [7], [8], [9]. Another possibility to address composite convex optimization problems is to use splitting methods like ADMM [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Restart FISTA with Global Linear Convergence

Álamo

Limón

Krupa

2019

2019 18th European Control Conference (ECC)

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Fast Iterative Shrinking-Threshold Algorithm (FISTA) is a popular fast gradient descent method (FGM) in the field of large scale convex optimization problems. However, it can exhibit undesirable periodic oscillatory behaviour in some applications that slows its convergence. Restart schemes seek to improve the convergence of FGM algorithms by suppressing the oscillatory behaviour. Recently, a restart scheme for FGM has been proposed that provides linear convergence for non strongly convex optimization problems that satisfy a quadratic functional growth condition. However, the proposed algorithm requires prior knowledge of the optimal value of the objective function or of the quadratic functional growth parameter. In this paper we present a restart scheme for FISTA algorithm, with global linear convergence, for non strongly convex optimization problems that satisfy the quadratic growth condition without requiring the aforementioned values. We present some numerical simulations that suggest that the proposed approach outperforms other restart FISTA schemes.

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“…Apesar de sua operação robusta e confiável, este hardware de computação geralmente possui baixo poder de processamento e baixa capacidade de memória, fatores que podem limitar a implementação das estratégias de controle avançado. O número de artigos que discutem implementações de controle preditivo em CLPé ainda limitado, por isso quando se aborda este tipo de aplicação os autores, em geral, propõe técnicas para solucionar o problema de otimização do MPC de forma eficiente, a fim de viabilizar a aplicação do controlador no referido hardware (Valencia-Palomo and Rossiter, 2009;Huyck et al, 2014;Kufoalor et al, 2014;Levenson et al, 2017;Krupa et al, 2018).…”

Section: Introductionunclassified

Controle Preditivo Com Garantia De Estabilidade E Factibilidade Embarcado Em Um CLP Industrial

Santana

Martins

Chagas

2020

Anais Do Congresso Brasileiro De Automática 2020

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Este trabalho apresenta a implementação de uma estratégia de controle preditivo baseado em modelo estabilizante com garantia de factibilidade em um controlador logico programavel (CLP) industrial, tratando-se de uma aplicação ainda não descrita na literatura. A formulação deste controlador resulta em um problema de programação quadrática que ée sempre viável, por isso, torna-se uma opção atrativa para aplicações industriais. Para calcular a lei de controle estabilizante ée proposta a utilização da téecnica de otimização ADMM (Alternating Direction Method of Multipliers), viabilizando o controle de um sistema eletromecânico de dinâmica rápida em um CLP com baixa capacidade de memória e desempenho computacional limitado. Resultados de simulação e experimentais ilustram o bom desempenho e a robustez do controlador MPC implementado, o qual é capaz de tolerar certo grau de desassociação no modelo da planta, apontando-o como uma estratégia de controle adequada para aplicações práticas.

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Implementation of Model Predictive Controllers in Programmable Logic Controllers using IEC 61131-3 standard

Cited by 17 publications

References 16 publications

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm

Restart FISTA with Global Linear Convergence

Controle Preditivo Com Garantia De Estabilidade E Factibilidade Embarcado Em Um CLP Industrial

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