PID based nonlinear processes control model uncertainty improvement by using Gaussian process model

Chan, Lester Lik Teck; Chen, Tao; Chen, Junghui

doi:10.1016/j.jprocont.2016.03.006

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…PID controllers are the basic component in modern multi-level hierarchy of control [12]. The multi-level hybrid mechanism can, however, pose a great challenge to the PID controller design.…”

Section: Self-adaptive Interaction Pid Controllermentioning

confidence: 99%

Control Algorithm of Six Cable-Driven Parallel Manipulator for FAST

Hu¹,

Xu²,

Cao³

et al. 2018

dtetr

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The feed cabin supporting system in a Five-hundred-meter Aperture Spherical Radio Telescope (FAST) project is a large-span cable-driven parallel manipulator (CPM). In this article, the kinematics model of the six CPM was established in the global coordinate O-XYZ. A corresponding difference of cable length between the current position and next position of the feed cabin was determined by controlling the servo motor. A nonlinear adaptive interaction PID (NAIPID) controller was proposed to improve the control performance of the control system. The simulations program of traditional PID and NAIPID were implemented in MATLAB software, respectively. A 3-meter model experiment was carried out to prove the control accuracy of the feed cabin. Both simulation and experimental results showed that the proposed control method had rapid response, and can effectively improve the control performance for the six CPM systems.

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Section: Self-adaptive Interaction Pid Controllermentioning

confidence: 99%

Control Algorithm of Six Cable-Driven Parallel Manipulator for FAST

Hu¹,

Xu²,

Cao³

et al. 2018

dtetr

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“…Gaussian process regression (GPR), another supervised machine learning method, [24] is commonly used for control in state space, due to its flexibility for model inference [25], [26] and the existence of the theoretical error bound [27]. The GPR method examined in [28] is employed for predicting the unknown system dynamics, followed by parameter adjustment for the controller, which is accomplished through optimization of an objective function associated with tracking error [29]. However, except for the lack of stability analysis, the optimization-based PI tuning is hard to apply for higher frequency control for PMSMs due to its high computational load.…”

Section: Introductionmentioning

confidence: 99%

A Learning-based Online Controller Tuning Method for Electric Motors using Gaussian Processes

Yin,

Dai,

Yang

et al. 2024

Preprint

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In industrial applications, Proportional-Integral (PI) controllers are frequently employed for controlling Permanent Magnet Synchronous Motors (PMSMs) due to their fast response rate and comprehensibility. However, their control performance may deteriorate with unforeseen environmental disturbances and uncertainties. To enhance the adaptivity of the controller, Gaussian Process Regression (GPR), a machine learning technique, is used to mitigate the impact of unknown components in system dynamics in this paper. In particular, GPR is adopted to autonomously tune the parameters of the PI controller, composing a novel GPR-based PI (GPR-PI) controller that maintains both interpretability and safety, because of its theoretical prediction error bound. Moreover, the stability of the system is guaranteed under the sufficiently small designed learning rate of the PI coefficients in the gradient descent rule, indicating the tradeoff between stability and adaptivity. Then, the GPR-based PI is compared with the NN-based PI and demonstrates the priority of enlarging the stabilized speed range. Ultimately, the experiments validate the efficacy of the GPR-PI approach, showcasing a significant reduction of over 60% in response time when compared to the standard PI controller.

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“…To control a liquid level system in most applications, classical linear proportional-integral (PI) controller is generally performed. 4,5 In this control technique, to obtain the parameters of controller, the system should be linearized using linearization methods with a nominal system operating point. At that point, the parameters of the controller are identified and tuned.…”

Section: Introductionmentioning

confidence: 99%

Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system

Ayten

Dumlu

Kaleli

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents the self-tuning regulator control technique for a coupled tank liquid level system that often used in industry. An autoregressive with exogenous model has been used as the liquid process model with the self-tuning control implementation in order to track the desired tank level trajectories with disturbances and uncertainties of the system dynamics. The designed self-tuning controller has been sensitive to parameter variations of the nonlinear coupled tank system. The parameters of the proposed controller are periodically updated themselves during the process by means of online recursive least square method with the forgetting factor algorithm. In this way, the parameter variations and unwanted disturbances of the system are eliminated in real-time application. In order to demonstrate the efficiency of the self-tuning regulator control technique, the real-time studies have been executed. The obtained experimental results demonstrated that the proposed controller gives the better trajectory tracking performance and smaller magnitude in overshot and undershot than the designed classical proportional–integral and sliding mode controllers.

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PID based nonlinear processes control model uncertainty improvement by using Gaussian process model

Cited by 14 publications

References 28 publications

Control Algorithm of Six Cable-Driven Parallel Manipulator for FAST

Control Algorithm of Six Cable-Driven Parallel Manipulator for FAST

A Learning-based Online Controller Tuning Method for Electric Motors using Gaussian Processes

Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system

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