Model Identification And Controller Design For An Electro-Pneumatic Actuator System With Dead Zone Compensation

Pneumatic actuator system is inexpensive, high power to weight ratio, cleanliness and ease of maintenance make it’s a choice compared to hydraulic actuator and electromagnetic actuator. Nonetheless, the steady state error of the system is high due to the dead zone of the valve. In this paper, an Auto-Regressive with External Input (ARX) model structure is chosen to represent the pneumatic actuator system. The recursive least square method is used to estimate the model parameters. The pole-assignment controller is then developed for position tracking. To cater the problem of high in steady state error, the dead zone compensation is added to the system. The dead zone controller was designed based on the inverse dead zone model and the dead zone compensation designed based on the desired error. The proposed method is then experimentally with varies load and compares with Nonlinear PID controller. The result shows that the proposed controller reduced the overshoot and steady state error of the pneumatic actuator system to no overshoot and 0.025mm respectively. Index terms: System identification, recursive least square, ARX, dead zone compensator, pneumatic actuato

controller. The result shows that the proposed controller reduced the overshoot and steady state error of the pneumatic actuator system to no overshoot and 0.025mm respectively. Index terms: System identification, recursive least square, ARX, dead zone compensator, pneumatic actuator

In this paper, a nonlinear mathematical modeling based on fundamental physical derivation is presented. The mass flow rate, pressure dynamic and equation of motion are derived referring to the previous research. Simulation work is done to confirm the model based on this derivation. Cascade control based on PID and P controller is designed through simulation in SIMULINK where the parameters of the controller are obtained through PID with optimization toolbox. The results reveal that both step and sinusoidal response test, the cascade controller consistently indicates outperform performance compared to classical PID method. In future, it is recommended to apply this technique to the real-time implementation

Practical robust control using Self-regulation Nonlinear PID controller for pneumatic positioning system

This paper investigates the robustness of the pneumatic positioning system controlled by Self-regulation Nonlinear PID (SNPID) controller. This controller is executed by utilizing the characteristic of rate variation of the nonlinear gain that are readily available in Nonlinear PID (NPID) controller. A Self-regulation Nonlinear Function (SNF) is used to reprocess the error signal with the purpose to generate the value of the rate variation, continuously. Simulation and experimental tests are conducted. The controller is implemented to a variably loads and pressures. The comparison with the other existing method i.e. NPID and conventional PID are performed and evaluated. The effectiveness of SNPID + Dead Zone Compensator (DZC) has been successfully demonstrated and proved through simulation and experimental studies

Robust control strategy for pneumatic drive system via enhanced nonlinear PID controller

This paper presents the pneumatic positioning system controlled by Enhanced Nonlinear PID (NPID) controller. The characteristic of rate variation of the nonlinear gain that are readily available in NPID controller is utilized to improve the performance of the controller. A Self-regulation Nonlinear Function (SNF) is used to reprocess the error signals with the purpose of continuously generating the values for the rate variation. Subsequently, the controller has successfully been implemented on dynamically changing loads and pressures. The comparison with the other available method such as. NPID and conventional PID are performed and evaluated. The effectiveness of this method with Dead Zone Compensator (DZC) has also been successfully demonstrated and proven through simulations and experimental studies

Enhancement in pneumatic positioning system using nonlinear gain constrained model predictive controller: experimental validation

The issues of inaccurate positioning control have made an industrial use of pneumatic actuator remains restricted to certain applications only. Non-compliance with system limits and properly control the operating system may also degrade the performance of pneumatic positioning systems. This study proposed a new approach to enhance pneumatic positioning system while considering the constraints of system. Firstly, a mathematical model that represented the pneumatic system was determined by system identification approach. Secondly, model predictive controller (MPC) was developed as a primary controller to control the pneumatic positioning system, which took into account the constraints of the system. Next, to enhance the performance of the overall system, nonlinear gain function was incorporated within the MPC algorithm. Finally, the performances were compared with other control methods such as constrained MPC (CMPC), proportional-integral (PI), and predictive functional control with observer (PFC-O). The validation based on real-time experimental results for 100 mm positioning control revealed that the incorporation of nonlinear gain within the MPC algorithm improved 21.03% and 2.69% of the speed response given by CMPC and PFC-O, and reduced 100% of the overshoot given by CMPC and PI controller; thus, providing fast and accurate pneumatic positioning control system

Position Control of Pneumatic Actuator Using Self-Regulation Nonlinear PID

The enhancement of nonlinear PID (N-PID) controller for a pneumatic positioning system is proposed to improve the performance of this controller. This is executed by utilizing the characteristic of rate variation of the nonlinear gain that is readily available in N-PID controller. The proposed equation, namely, self-regulation nonlinear function (SNF), is used to reprocess the error signal with the purpose of generating the value of the rate variation, continuously. With the addition of this function, a new self-regulation nonlinear PID (SN-PID) controller is proposed. The proposed controller is then implemented to a variably loaded pneumatic actuator. Simulation and experimental tests are conducted with different inputs, namely, step, multistep, and random waveforms, to evaluate the performance of the proposed technique. The results obtained have been proven as a novel initiative at examining and identifying the characteristic based on a new proposal controller resulting from N-PID controller. The transient response is improved by a factor of 2.2 times greater than previous N-PID technique. Moreover, the performance of pneumatic positioning system is remarkably good under various loads

Improved pole-placement control with feed-forward dead zone compensation for position tracking of electro-pneumatic actuator system

Dead-zone in the valve degraded the performances of the Electro-Pneumatic Actuator (EPA) system. It makes the system difficult to control, become unstable and leads to chattering effect nearest desired position. In order to cater this issue, the EPA system transfer function and the dead-zone model is identified by MATLAB SI toolbox and the Particle Swarm Optimization (PSO) algorithm respectively. Then a parametric control is designed based on pole-placement approach and combine with feed-forward inverse dead-zone compensation. To reduce chattering effect, a smooth parameter is added to the controller output. The advantages of using these techniques are the chattering effect and the dead-zone of the EPA system is reduced. Moreover, the feed-forward system improves the transient performance. The results are compared with the pole-placement control (1) without compensator and (2) with conventional dead-zone compensator. Based on the experimental results, the proposed controller reduced the chattering effect due to the controller output of conventional dead-zone compensation, 90% of the pole-placement controller steady-state error and 30% and 40% of the pole-placement controller with conventional dead-zone compensation settling time and rise time

Application of electromagnetic sensor in electro-pneumatic actuator displacement control under variable loads conditions: experimental analysis

Dead-zone is a major issue that degrades the performance of the positioning control system in the pneumatic proportional valve control system. In order to address the issue, a switching inverse dead-zone compensator was incorporated to the pole-placement control of the Electro-Pneumatic Actuator (EPA) systems driven by a proportional directional control valve. The focus of this study is to do an experimental analysis to evaluate the robustness of the system under varying loads and varying position distances. Electromagnetic sensor is used to measure the displacement of the pneumatic cylinder piston movement. In this paper, the EPA model was chosen as a Hammerstein model that contains an Autoregressive with exogenous term (ARX) model and a nonlinear dead-zone model. The ARX model is estimated using the Recursive Least Square (RLS) method and the nonlinear model is obtained by using the Particle Swarm Optimization (PSO) method. The position tracking of the EPA system adapts to the pole-placement control law and is combined with switching inverse dead-zone in a feedforward manner. Experimental investigations were carried out for varying loads from 3.1 kg to 23.5 kg and varying position distances from 25 mm to 200 mm. Experimental results show that the EPA system controlled by the proposed controller is able to perform no overshoots for loads weighing less than 23.5 kg for all tested position distances. In addition, the proposed method achieved a steady state position error of 0.46 mm, a rise time of 0.21 s and a settling time of 0.49 s. The results demonstrated that as the load weight and position distance increased, transient time increased. However, the proposed method has successfully controlled the positioning of the EPA systems for all tested load weight and position distance

Intelligent locking system using deep learning for autonomous vehicle in internet of things

Now-a-days, we are using modern locking system application to lock and unlock our vehicle. The most common method is by using key to unlock our car from outside, pressing unlock button inside our car to unlock the door and many vehicles are using keyless entry remote control for unlocking their vehicle. However, all of this locking system is not user friendly in impaired situation for example when the user hand is full, lost the key, did not bring the key or even conveniently suited for special case like disable driver. Hence, we are proposing a new way to unlock the vehicle by using face recognition. Face recognition is the one of the key components for future intelligent vehicle application in the Autonomous Vehicle (AV) and is very crucial for next generation of AV to promote user convenience. This paper proposes a locking system for AV by using face deep learning approach that adapt face recognition technique. This paper aims to design and implement face recognition procedural steps using image dataset that consist of training, validation and test dataset folder. The methodology used in this paper is Convolution Neural Network (CNN) and we were program it by using Python and Google Colab. We create two different folders to test either the methodology capable to recognize difference faces. Finally, after dataset training a testing was conducted and the works shows that the data trained was successful implemented. The models predict an accurate output result and give significant performance

Non-linear modeling and cascade control of an industrial pneumatic actuator system

In this paper, a nonlinear mathematical modeling based on fundamental physical derivation is presented. The mass flow rate, pressure dynamic and equation of motion are derived referring to the previous research. Simulation work is done to confirm the model based on this derivation. Cascade control based on PID and P controller is designed through simulation in SIMULINK where the parameters of the controller are obtained through PID with optimization toolbox. The results reveal that both step and sinusoidal response test, the cascade controller consistently indicates outperform performance compared to classical PID method. In future, it is recommended to apply this technique to the real-time implementation