Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System

Fan, Yuqi; Shao, Junpeng; Sun, Guitao

doi:10.3390/s19122727

Cited by 63 publications

(29 citation statements)

References 49 publications

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“…The beetle antennae search (BAS) algorithm [18][19][20] is an efficient, intelligent optimization algorithm proposed in 2017. Beetles search for food according to the intensity of food odor.…”

Section: Improved Gwo Algorithm Based On the Beetle Antennae Search Amentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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Section: Improved Gwo Algorithm Based On the Beetle Antennae Search Amentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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“…The stability could also be analyzed on the basis of the Jury's stability criterion discussed in 2.1.2. The coefficients c 0 and c n were calculated using (21) and (22). The value of the polynomial P(z) when z = 1 and z = −1 was calculated according to (18).…”

Section: Example 1: Closed Loop Stability Analysismentioning

confidence: 99%

“…As mentioned in Section 5.1.1, the Jury's stability test was performed on the pressure control process. The value of P(z) for z = 1 and z = −1, θ ref , coefficients c 0 and c n were calculated using (18), (21), (22) and (25). The values are tabulated in Table 14.…”

Section: Example 2: Closed Loop Stability Analysismentioning

confidence: 99%

“…There are bio-inspired optimization algorithms that can also be used to tune the classical and RTD-A controller parameters [21]. Yugi Fan proposed beetle antennae search algorithm to tune PID controller for controlling the position of electro-hydraulic servo system [22]. The hybrid of the PSO and GA algorithm is used to self-tune PID for the hydraulic cylinder pressure control [23].…”

Section: Introductionmentioning

confidence: 99%

“…Computing the control action. electro-hydraulic servo system [22]. The hybrid of the PSO and GA algorithm is used to self-tune PID for the hydraulic cylinder pressure control [23].…”

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confidence: 99%

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Simulation Tool for Tuning and Performance Analysis of Robust, Tracking, Disturbance Rejection and Aggressiveness Controller

2019

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The RTD-A (robust, tracking, disturbance rejection and aggressiveness) controller is a novel control scheme that substitutes the classical proportional integral derivative (PID) controller. This novel controller’s performance depends on the four controller tuning parameters (θR, θT, θD and θA). The tuning of RTD-A controller is more transparent than classic PID controllers. The RTD-A tuning parameters values lies between ZERO and ONE. Availability of a tool to design optimal parameters for this controller and evaluating the performance on a given system is necessary for the researchers. In this paper, the new simulation tool is presented to deal with the RTD-A control scheme. There are four graphical user interface tools included in the proposed tool and working of each tool is explained in detail. To demonstrate the proposed tool, two examples, which involve a liquid level control application and an air pressure control application, are presented in this work. The performance of the RTD-A controller is compared with PID controller. RTD-A controllers are tuned using optimization algorithms and their performances are observed and analyzed in both cases under deterministic and uncertain conditions.

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Neural network‐based output synchronization control for multi‐actuator system

Wang

Ding

Zhao

et al. 2022

Adaptive Control & Signal

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This article proposes a novel output synchronization control strategy for a class of multi-actuator system with strict-feedback form. High-order sliding mode observer is utilized to estimate the system states with the only available output signal. Moreover, radio basis function neural network combined estimated states is applied to handle the system uncertainties, which helps to realize the combination of state observation and disturbance observation and reduce the dependence on the system model. Furthermore, a new synchronization control method is employed to improve the synchronization accuracy of multiple actuators through backstepping technology. Based on the above control strategies, the control performance of the multi-actuator system is greatly enhanced while the design difficulty of the controller is significantly reduced. In the end, simulations and experiments examples are used to illustrate the superiority of the designed technique.

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Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System

Cited by 63 publications

References 49 publications

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Simulation Tool for Tuning and Performance Analysis of Robust, Tracking, Disturbance Rejection and Aggressiveness Controller

Neural network‐based output synchronization control for multi‐actuator system

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