Simultaneous tuning of cascade controllers based on nonlinear optimization

Torga, Diego S; Silva, Moisés T. da; Reis, Lucas; Euzébio, Thiago A. M.

doi:10.1177/01423312221099376

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…There are many classical and intelligent tuning methods for PID parameters, such as trial and error, the Ziegler–Nichols method, the Cohen-Coon method, neural network, genetic algorithms and some useful optimization algorithms. 29–32 In this study, we first obtained an initial set of parameter values using a neural network, and then optimized them using the trial-and-error method. A suitable set of PI controller parameters was determined using the neural network and trial-and-error method, with K P 1 = 6.1 and K I 1 = 0.00084 for main controller.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

Lan,

Chen,

Xue

et al. 2024

Measurement and Control

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Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

Lan,

Chen,

Xue

et al. 2024

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Euzébio et al [ 21 ] proposed a nonlinear optimization algorithm to minimize the disturbance effects in coupled loops in decentralized PID controllers. Torga et al [ 22 ] proposed a nonlinear optimization for the simultaneous tuning of control loops in a cascade configuration under load disturbance.…”

Section: Introductionmentioning

confidence: 99%

Optimal PID Control of a Brushed DC Motor with an Embedded Low-Cost Magnetic Quadrature Encoder for Improved Step Overshoot and Undershoot Responses in a Mobile Robot Application

Bitriá

Palacín

2022

Sensors

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The development of a proportional–integral–derivative (PID) control system is a simple, practical, highly effective method used to control the angular rotational velocity of electric motors. This paper describes the optimization of the PID control of a brushed DC motor (BDCM) with an embedded low-cost magnetic quadrature encoder. This paper demonstrates empirically that the feedback provided by low-cost magnetic encoders produces some inaccuracies and control artifacts that are not usually considered in simulations, proposing a practical optimization approach in order to improve the step overshoot and undershoot controller response. This optimization approach is responsible for the motion performances of a human-sized omnidirectional mobile robot using three motorized omnidirectional wheels.

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Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank

Urrea

Garcia-Garcia

2023

Processes

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This work seeks to contribute to the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied to classical control techniques and their results are analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive Control (MPC) and Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers are designed for the level control of a spherical tank. Subsequently, several tests and scenarios similar to those present in industrial processes are established, while the transient response of the controllers, their performance indices for monitoring the reference value, the rejection of disturbances, the presence of parameter uncertainties and the effects of noise are analyzed. The results show good reference tracking, with a settling time of approximately 5 s for 5 cm and a rise time of less than 4 s. No evidence for steady-state error or overshoot was found and controllers behave positively in the diverse scenarios assessed. The FLC and ANN controllers showed the greatest limitations, while ANFIS, MPC and NARMA-L2 exhibited competitive results considering their transient response and the performance indices calculated.

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Simultaneous tuning of cascade controllers based on nonlinear optimization

Cited by 4 publications

References 29 publications

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

Optimal PID Control of a Brushed DC Motor with an Embedded Low-Cost Magnetic Quadrature Encoder for Improved Step Overshoot and Undershoot Responses in a Mobile Robot Application

Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank

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