Neural algorithm for optimization of multidimensional object controller parameters

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire rang...

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Vydáno v:	Neural computing & applications Ročník 36; číslo 25; s. 15907 - 15924
Hlavní autoři:	Bałazy, Patryk, Lalik, Krzysztof, Knap, Paweł
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.09.2024 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control systems Controllers Cost analysis Cost function Cranes Data Mining and Knowledge Discovery Image Processing and Computer Vision Linear quadratic regulator Multivariable control Nonlinear control Nonlinear systems Nonlinearity Optimal control Original Article Parameters Probability and Statistics in Computer Science System dynamics System effectiveness Model-free Neural network Adaptive control Reinforcement learning
ISSN:	0941-0643, 1433-3058
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Abstract	Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R , have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.
AbstractList	Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R , have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics. Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.
Author	Lalik, Krzysztof Knap, Paweł Bałazy, Patryk
Author_xml	– sequence: 1 givenname: Patryk surname: Bałazy fullname: Bałazy, Patryk organization: Faculty of Mechanical Engineering and Robotics, AGH University of Krakow – sequence: 2 givenname: Krzysztof orcidid: 0000-0003-4277-9801 surname: Lalik fullname: Lalik, Krzysztof email: klalik@agh.edu.pl organization: Faculty of Mechanical Engineering and Robotics, AGH University of Krakow – sequence: 3 givenname: Paweł surname: Knap fullname: Knap, Paweł organization: Faculty of Mechanical Engineering and Robotics, AGH University of Krakow
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Cites_doi	10.1016/j.ins.2021.05.018 10.1109/TCST.2007.903392 10.1109/ACCESS.2021.3090907 10.1109/TSMC.2016.2640950 10.1049/iet-cta.2020.0073 10.1080/10798587.2017.1329245 10.1146/annurev-control-053018-023825 10.1109/TCYB.2020.2998505 10.1002/rnc.4469 10.3390/s21186247 10.1002/rnc.4396 10.1016/j.jfranklin.2011.05.009 10.1109/TFUZZ.2020.2973950 10.4028/www.scientific.net/SSP.164.95 10.1049/iet-cta.2017.0154 10.1177/1045389X15610907 10.1109/ACCESS.2020.3009357 10.1137/S0363012902400713 10.5121/ijccms.2013.2301 10.1007/978-981-16-6448-9_2 10.1016/j.ymssp.2017.03.015 10.1016/j.sna.2016.01.047 10.1016/j.ifacol.2018.09.373 10.1109/TFUZZ.2019.2915192 10.3390/e23111487 10.1109/TFUZZ.2021.3119750 10.1002/rnc.3987 10.1049/iet-cta.2011.0348 10.1002/rnc.4352 10.1515/ecce-2015-0006 10.1016/j.measurement.2017.01.041 10.1016/j.neucom.2021.12.091 10.2514/1.G004226 10.1109/AIM43001.2020.9158838 10.1109/ICSTCC.2019.8886157 10.1007/978-81-322-2217-0_41 10.1609/aaai.v33i01.33013870 10.1109/EPEC52095.2021.9621395 10.1109/ICRA40945.2020.9196673
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Snippet	Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately,...
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SubjectTerms	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control systems Controllers Cost analysis Cost function Cranes Data Mining and Knowledge Discovery Image Processing and Computer Vision Linear quadratic regulator Multivariable control Nonlinear control Nonlinear systems Nonlinearity Optimal control Original Article Parameters Probability and Statistics in Computer Science System dynamics System effectiveness
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Title	Neural algorithm for optimization of multidimensional object controller parameters
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