High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

Chen, Ye; Tao, Guoliang; Líu, Hao

doi:10.3390/app9173472

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…When the amplitude A varies from 0.01 to 0.1 m, the peak error can be reduced from 1.8% to 10% and the RMS error can be reduced from 0.8% to 6.5%. The minimum peak error of 2.2 mm can be obtained at the amplitude A=0.01 m. This result of the new controller indicates better control performances than other nonlinear and robust control methods [9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Resultsmentioning

confidence: 70%

“…The full-state feedback was used in the control law for simultaneous parameter identification and tracking control. A combination between adaptive control strategy and neural network was proposed by Chen et al [11] for an electro-pneumatic servo system; the neural network was used to compensate for constructing a linearized model of the nonlinear system and the robust adaptive controller was used to perform the modelmatching for the uncertain linearized model of the system. In [12], Gross and Rattan applied multilayer neural networks to compensate for the nonlinear nature of the dynamic system in conjunction with a PID feedback controller.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Control of a Pneumatic Actuator Based on a Dynamic Friction Model

Tran

2021

SV-JME

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This paper presents a new controller for the position of a pneumatic actuator. The controller is designed based on the multiple-surface sliding control method in combination with a frictional compensator. The multiple-surface sliding control method is applied to deal with the nonlinear characteristics of the pneumatic system, and the frictional compensator is applied to compensate for the friction force in the pneumatic actuator. The friction force is estimated based on a dynamic friction model (the LuGre model). Both simulation and experimental studies are done to evaluate the new controller. The evaluation results indicate significant improvement in the tracking position error of the new controller comparing to the multiple-surface sliding controller without friction compensation and other nonlinear controllers.

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Section: Resultsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Nonlinear Control of a Pneumatic Actuator Based on a Dynamic Friction Model

Tran

2021

SV-JME

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“…We may also extend the proposed methods to probabilistic linguistic uncertain environments in our future studies, such as score function based on concentration degree for probabilistic linguistic term sets, evaluating the Internet of ings platforms using integrated probabilistic linguistic multicriteria decisionmaking method [20][21][22][23]. We can also introduce a nonlinear control algorithm in the controller design, such as fuzzy control, neural network control [24], and observerbased fuzzy adaptive control [25].…”

Section: Discussionmentioning

confidence: 99%

Man-Machine Synergy Control for Pneumatically Powered Exoskeleton Based on Surface Electromyogram Signal

Jiang

Liu

et al. 2022

Mathematical Problems in Engineering

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The biceps and triceps alternatively act as agonists and antagonists to realize upper limb movement. Pneumatic artificial muscle (PAM), which is inflated and deflated with compressed air instead of water, has similar characteristics to those of human muscle. The challenge is whether an exoskeleton actuated by PAM can help biceps lift the upper limb. Accordingly, the principal aim of this research is to guarantee precise signal collection and control process and adopt the synergy control of PAM and upper limb. In this system, the biceps and triceps provide the main signals in synergy control, electrodes are pasted outside of biceps and triceps to sample their electromyogram signal (EMGs), and the mechanical structure and control system of the pneumatic exoskeleton are proposed. The relationship between duty-ratio-controlled variables and PAM contraction speed is given by experimental analysis, and the maximum duty ratio of controlled variables of input is set to 80. The feature analysis of EMGs can be various including envelope, moving average, and moving root mean square (RMS). The envelope is taken to extract muscle contraction information through upper limb muscles in a static contraction experiment. Then, the processes of biceps and triceps EMGs feature changes including rapid swing, slow swing, and discontinuous swing under various loads are analyzed during upper limb muscle dynamic contraction. The duty-ratio-controlled variables can be divided into five levels, which correspond to exertion rating from powerless to very strong in two EMG characters. These can be reflected in a scatter diagram of duty-ratio-controlled variables and average EMG characters. A nonlinear relationship can be transferred into the continuous system by the polynomial interpolation method, solving the problem of saturation. The net duty-ratio-controlled variables are adopted to control the on-off state and pulse-width modulation (PWM) duty ratio of the high-speed on-off valve. The forearm lifting up movement is unpowered and powered with various load EMGs, and elbow discontinuous swing angle overshoot is performed to analyze the coordination effect in a synergy control experiment.

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“…To precisely control the position of the pneumatic actuators, pneumatic proportional valves are often used [3][4][5][6]. These valves allow controlling continuously the flow to the actuator and, therefore, it is easy to obtain the desired position of the actuators.…”

Section: Introduction *mentioning

confidence: 99%

A Method for Improving Position Control Performances of a Pneumatic Cylinder Using On-Off Solenoid Valves

2022

JST: SSAD

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Technology using on-off solenoid valves is currently being applied in controlling pneumatic actuators to replace the use of the pneumatic proportional/servo valves. However, controlling the pneumatic actuators using on-off solenoid valves is often difficult due to the low switching frequency of these valves and the high nonlinearity of the pneumatic system. In this paper, we first re-evaluate a controller proposed by Truong in 2020, in which four pneumatic on-off valves were used. We then modify the Truong's controller by dividing the desired position input of the cylinder into two controlled position intervals and each interval will use a separate control law. Seven operating modes of the four on-off solenoid valves combined with the Pulse Width Modulation method (PWM) are used. The experimental research method is carried out. The modified controller is evaluated by comparing it with the Truong’s controller and a controller for a pneumatic system using pneumatic proportional valves at the same conditions of the desired position inputs. Comparison results verify the usefulness of the modified controller.

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High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

Cited by 9 publications

References 35 publications

Nonlinear Control of a Pneumatic Actuator Based on a Dynamic Friction Model

Nonlinear Control of a Pneumatic Actuator Based on a Dynamic Friction Model

Man-Machine Synergy Control for Pneumatically Powered Exoskeleton Based on Surface Electromyogram Signal

A Method for Improving Position Control Performances of a Pneumatic Cylinder Using On-Off Solenoid Valves

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