Control Of Pneumatic Servo System Research Articles

Self-tuning hybrid fuzzy sliding surface control for pneumatic servo system positioning

This paper presents a new robust control strategy developed for the pneumatic servo system (PSS) by hybridizing two types of fuzzy logic control (FLC) rules as a self-tuner to the integral sliding mode control (ISMC), namely self-tuning hybrid fuzzy sliding surface control (SH-FSSC) controller. A sliding surface consisting of two switched fuzzification rules, relying on the tuning threshold value of the position error tracking, was designed to consider both the position and the force feedback of the pneumatic proportional valve with a double-acting cylinder (PPVDC) system. The approach is to acquire multiple features not only on tracking error but also faster transient response with finite-time convergence, chatter elimination, and robustness against uncertainty. The proposed control strategy was verified and validated by conducting experiments with the actual PPVDC unit linked to the tip of the robot’s tri-finger pneumatic grippers (TPG) platform. The experimental works were accomplished using two types of input trajectories: multi-steps and sinusoidal input trajectories. On the other hand, an additional external payload as a disturbance to the test rig has also been added at the end of the pneumatic gripper jaw, intended to evaluate the proposed controller’s robustness performance. The advantage of the proposed method was validated by significantly eliminating oscillation for each transient response, maintaining high tracking performance, and minimizing hysteresis effects. The oscillation was suppressed with minimal overshoot, and the proposed method was achieved without a significant loss of performance.

Adaptive neural network control of pneumatic servo system considering state constraints

At present, there are many efforts put into high performance tracking control of the pneumatic position servo system that have achieved considerable results, but the state constraints are rarely considered in the existing methods. The controller designed without considering the state constraints might lead to the damage of the device or the degradation of the expected performance. With the development of smart mechatronics, it is expected that the field engineers just connect the system together and put the controller into effect, which needs the controller have ability to deal with unknown model, unknown valve zero point, unknown control direction, etc, automatically. This paper aims at this complicated goal as well as considering the state constraints to improve the tracking accuracy. To fulfill the comprehensive goal, radial basis function neural network (RBFNN) is employed to treat the unknown model, unknown disturbance, unknown valve zero point, Nussbaum function is used to deal with unknown control direction, barrier Lyapunov function (BLF) is used to tackle the stability problem considering state constraints. The stability of the controlled system is proved by using Lyapunov theory and Young’s inequality. Experiment results prove the effectiveness of the method, and the comparative experiment results show the superiority of the proposed method.

Neural Network Identification and Direct Adaptive Fuzzy Neural Network (DAFNN) Controller for Unknown Nonlinear Non-affine Pneumatic Servo System

In this paper, a direct adaptive fuzzy neural network (DAFNN) controller for trajectory tracking control of the non-linear non-affine pneumatic servo system is presented. First, using a neural network identifier, the non-linear dynamics of a real pneumatic servo system is simulated. By comparing the output of the neural network and the output of the experimental setup, it is observed that the non-linear pneumatic actuator system is well-identified using neural networks. By incorporating the Lyapunov stability theorem, the adaptive laws for the parameters of the controller are obtained, parameter boundedness and stability of the closed-loop system are guaranteed. Finally, practical results are successfully implemented for trajectory tracking control of the pneumatic servo system, in which the effect of the simultaneous updating of the antecedent and consequent’s parameters of the fuzzy neural network controller has been investigated. The tracking error ±1.3mm and ±1 mm for proposed updating method compared to ±2.5mm and ±3.5mm, for a case that the weigh parameters are merely adjusted, are obtained. The results indicate the proposed adjustment method improves the performance of the controller in the presence of unknown nonlinearities and dynamics uncertainty.

Output feedback motion control of pneumatic servo systems with desired compensation approach

Output feedback motion control of pneumatic servo systems with desired compensation approach

Intelligent position control for pneumatic servo system based on predictive fuzzy control

In this research, a novel method of predictive fuzzy control combined with a neural network is proposed for a pneumatic servo system. With the proposed method, the future outputs of the plant are predicted by using an imaginary plant in the predictive fuzzy scheme. The error between the desired value and the output of the imaginary plant, and the change in this error is applied as the input signals of the predictive fuzzy controller to generate a direct control input. In the proposed method, the neural network is introduced to construct the imaginary plant model of the target pneumatic servo system. To obtain teaching signals for the neural network scheme, a pre-test based on Proportional-integral-derivative (PID) control is implemented, and the results are applied as the teaching signals. With the imaginary plant model, the effectiveness of the proposed method using predictive fuzzy control is experimentally verified, and satisfactory position control using the proposed method is confirmed.

Observer-based nonlinear precise control of pneumatic servo systems

Pneumatic systems are used in a wide range of industrial robotic and automation systems due to their interesting properties. However, air compressibility, friction, and the other nonlinear characteristics of a servo pneumatic system are difficulties, which contribute to use modern controllers. Conventional linear controllers face steady-state error and uncertainty. Nonlinear modeling with model-based control is a good choice to deal with this problem. In this paper, behavior equation of flow and pressure, friction, and other nonlinear factors are studied. Afterward to reach precise position tracking and low steady error, sliding mode control is proposed. In this way, measurement of pressures and other states of system is required. To reduce the cost of using pressure sensor, observation of pressure with nonlinear high gain observer is suggested. It was seen that the new proposed approach solved the observability problem of servo pneumatic systems. Pressure signal of each sides of cylinder are observed simultaneously by measurement of piston position. Finally, stability of designed controller is studied in the presence of observed states. Experimental results validate the advantage of using designed controller-observer instead of conventional proportional–integral–derivative controller with different input signals in the presence of high friction actuator.

PSO Based Weight Selection and Fixed Structure Robust Loop Shaping Control for Pneumatic Servo System with 2DOF Controller

PSO Based Weight Selection and Fixed Structure Robust Loop Shaping Control for Pneumatic Servo System with 2DOF Controller

Control of Pneumatic Servo System Based on Neural Network PID Algorithm

The pneumatic servo system has characteristics of nonlinear, time-variant, large parameter variations and external disturbances, which is difficult to control. The conventional PID control is not suitable for the variable parameters of the controlled object, external disturbances. In this paper, the neural network controller combined with PID control is used to control the pneumatic servo system, and the structure diagram, algorithm and learning rule of the single neuron adaptive PID controller are put forward. The results show that,compared with the traditional PID control, the controller has significantly improved the control performance of system, Namely, the system has faster computational speed (real-time), stronger robustness and better adaptive ability.

Observer-based adaptive sliding mode control for pneumatic servo system

Abstract In this paper, an extended state observer (ESO) being incorporated with the adaptive sliding mode control theory is proposed to deal with a nonlinear pneumatic servo system characterized with input dead-zone, unknown system function, and external disturbance. The ESO is used to estimate system state variables of the unknown nonlinear system; the adaptive law is employed to compensate for dead-zone system behavior. Positioning experiments based on the derived control strategy were performed. As one example of positioning results, the positioning accuracy with sub-micrometers range was verified for both forward and backward actuations with step commands of 3 mm. The control scheme provided in this paper that can significantly improve the positioning performance of a traditional pneumatic servo system is demonstrated.

Neural-network adaptive controller for nonlinear systems and its application in pneumatic servo systems

In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.

Dynamic Constraint-Based Energy-Saving Control of Pneumatic Servo Systems

AbstractThis paper proposes a control approach that can provide significant energy savings for the control of pneumatic servo systems. The control methodology is formulated by decoupling the standard four-way spool valve used for pneumatic servo control into two three-way valves, then using the resulting two control degrees of freedom to simultaneously satisfy a performance constraint (which for this paper is based on the sliding mode sliding condition), and an energy-saving dynamic constraint that minimizes cylinder pressures. The control formulation is presented, followed by experimental results that indicate significant energy savings with essentially no compromise in tracking performance relative to control with a standard four-way spool valve.

Precise Position Control of Pneumatic Servo System Considered Dynamic Characteristics of Servo Valve

Precise positioning system is required in the fields of semi-conductor manufacturing process. A pneumatic servo table system is one of a precise positioning system. The features of the table are little friction force and little heat generation. Generally, the controller of this system is designed by neglecting the characteristics of servo valves. However, in high frequency band, the response of this system becomes unstable because of the characteristics of the valves. In this paper, we demonstrate that by considering the characteristics of servo valves, the position control of a pneumatic servo table could be improved. First, the dynamic characteristics of servo valves were examined. It became clear that the characteristics could be described as a second-order system. Then, the servo table system was designed as a fifth-order system. It became clear that the position accuracy of this system were greatly improved.

GA type IMC-PID control for pneumatic servo system

This paper presents a novel control scheme in which IMC(Internal Model Control)-PID control is combined with genetic algorithms(GA). IMC tuning rules have a number of advantages for enhancing control performance. IMC can minimize disturbance and use only one tuning parameter. IMC based PID control is attractive for industrial users because it has all the advantages of the IMC. For more aggressive control, we decrease the tuning parameter(more rapid response); for more robust response, we increase the tuning parameter(slower response). Usually we design the tuning parameter by trials and errors. But it is not so easy to design an optimal parameter for a specific system, especially for a nonlinear system. On the other hand, in the last twenty years, GA is attracted as one method which gives us optimal answers for global search, optimization and machine learning problems. In this paper, we propose an intelligent control scheme in which IMC-PID is combined with GA. GA will get the optimal or “near optimal” tuning parameter on-line after some generations. The effectiveness of the proposed scheme is confirmed by experiments using the existent pneumatic servo system.

Fuzzy-Neural Network Control for Pneumatic Servo System with Load Gravity.

The performance of a pneumatic servo-system is affected by compressibility of air, friction between cylinder and piston. Due to these non-linear factors, it would be difficult to accomplish the satisfactory control performance by using PID or adaptive control theory. Fuzzy control is one of effective control theory for such non-linear plant. However, synthesis of fuzzy rule including stability has not been well established yet. On the contrary, Neural network is excellent in learning performance. In this paper, we consider a learning method to acquire the appropriate fuzzy rules using error back propagation to improve control performance of pneumatic servo system. In the method, two criterions are defined and the fuzzy rules are adjusted so as to minimize them respectively using error back propagation. Moreover, differentiating coefficient of the plant used in error back propagation is acquired by newly established neural network. The method is applied to the vertical type pneumatic servo systems to prove it's effectiveness.

Double Structure Type Adaptive Control for Pneumatic Servo System with Disturbances.

In this paper, we propose a design scheme of a double structure type adaptive control for a pneumatic servo system with additive external forces. In this design scheme, the unknown parameters are identified at first and a choice is made on whether the plant model should be minimum-phase or not. This choice depends on the discrete-time plant model of the pneumatic servo system which changes to a minimum phase or a non-minimum phase through a difference in the sampling period and the load mass [1]. Then, a model reference adaptive control system (MRACS) is adopted in the case of the minimum phase and an adaptive pole assignment control system (APACS) is used in the other case. Furthermore, in this design scheme, a different process regards of the signals in the identification mechanism and the compensator in the controller are introduced to eliminate the influence of the constant disturbances caused by the additive external forces. The effectiveness of the proposed design scheme is confirmed by experiments using the existent pneumatic servo system with additive external forces.

Switching-type Adaptive Control for Pneumatic Servo System with Additive External Forces

Abstract In this paper, we propose a design scheme of a switching-type adaptive control for a pneumatic servo system with additive external forces. In this design scheme, the unknown parameters are identified at first and the plant model is decided whether to be minimumphase or not. Because the discrete-time plant model of the pneumatic servo system becomes to be a minimum phase or a non-minimum phase by the difference in the sampling period and the load mass [1]. Then, a model reference adaptive control system (MRACS) is adopted in the case of minimum-phase and an adaptive pole-assignment control system (APACS) is used in the other case. Furthermore, in this design scheme, deference process of the signals in the identification mechanism and the compensator in the controller are introduced to eliminate the influence of the constant disturbances caused by the additive external forces. The effectiveness of the proposed design scheme is confirmed by experiments using the existent pneumatic servo system with additive external forces.

Improved Scheme of Adaptive Pole-Assignment Control for Pneumatic Servo System

This paper presents an improved design scheme of the adaptive pole-assignment control for the pneumatic servo system with additive external forces. Using the conventional design scheme in the presence of the constant disturbances caused by the additive external forces, it is impossible to identify accurate parameters of the plant. Therefore, in this design scheme, the difference process of the identification signals is introduced to overcome the above mentioned problem. Furthermore, the controller of this design scheme is constructed with a compensator to eliminate the influence of the constant disturbances.