Pneumatic equipment is widely used in a variety of industries [1,2,3] due to lots of advantages, such as low cost, simple structure, easy maintenance [4]. The occurrence and development of electro-pneumatic proportional control valves place pneumatic control techniques beyond the limitation of point-to-point control. Electro-pneumatic proportional control valves provide the necessary components for pneumatic servo control systems, such as position [5,6,7,8,9], speed [10], force [11, 12], and pressure [13,14,15,16,17].
In the past decades, a great interest has been shown in pneumatic position servo systems. Compared with that of position servo controls, research on the design of pressure controllers at present is quite limited although the pneumatic pressure control systems have been used in the fields of robots, pressure calibration and various industrial processing systems. In some cases, the pressure controller is designed for improving the control performance of pneumatic position servo system [7,8,9]. In Ref. [7], a position control for a rodless cylinder was investigated. The proposed controller had an inner linearization pressure control loop and an outer position control loop. A PID controller with feedback linearization was used in the pressure control loop to nullify the nonlinearity arising from the compressibility of air. Noritsugu et al. [8] investigated a positioning control system with pressure control loop for improving control performance. A disturbance observer was employed to improve the pressure response and compensate the influence of friction force and parameter change. Igo et al. [9] used a conventional proportional controller with a variable offset pressure controller for achieving quick response and less overshoot of pneumatic robots.
The independent pressure control system generally consists of air supply, electro-pneumatic proportional valve, chamber and pressure sensor, for example, constant pressure system [14], pneumatic-pressure-load system [15] and pneumatic pressure signal generator [16]. Lu et al. [14] presented a constant pressure control system that consisted of frictionless cylinders, a large tank and a pneumatic proportional pressure valve. A hybrid controller combined with Bang-Bang, PD controller and fuzzy PID was proposed to minimize the pressure fluctuations in cylinders. The pneumatic-pressure-load system researched in Ref. [15], applied to intensity testing devices, was constructed by electro-pneumatic proportional pressure valve. In order to adapt to the parameter variability of the pressure load system and obtain better dynamic and static performances, a linear quadratic Gaussian self-tuning pressure regulator was proposed to realize an adaptive control of pressure in the chamber. In the pneumatic pressure signal generator [16], electro-pneumatic proportional directional valve was used to control the air-flow rates of injecting and outflowing the chamber to regulate the pressure. Because of the nonlinear characteristics, an intelligent coordinate control method, combining expert intelligent coordinator, expert controller, and fuzzy neural network controller, was designed to improve dynamic response and steady state accuracy of the generator.
At present, most of the researches can only regulate pressure to certain values. Poor dynamic characteristics and strong nonlinearity of such systems limit its application in the field of pressure tracking control. Positive and negative pneumatic pressure servo system (PNPPSS) is a very important equipment of the hardware-in-the-loop simulation of aerospace engineering [17], which controls the sealed chamber pressure according to the altitude command to simulate the atmospheric environment variation during flight. Currently, air data test systems in aerospace applications, for example the product ADTS405 from Druck, can only adjust the pressure or the vacuum to set values. However, the dynamic characteristics of such test systems are too poor to meet the requirements of the hardware-in-the-loop simulation. Moreover, the flight altitude is progressively increasing with the development of aerospace craft. Therefore, the continually enlarged pressure range of PNPPSS is demanded. In this work, the pressure range of the system is from 2 kPa to 140 kPa, and the frequency and amplitude of tracing curve are 2 Hz and 0.4 kPa respectively.
The principle sketch of the PNPPSS is shown in Figure 1. The system uses a compressor and a vacuum pump as positive and negative pressure source. The chamber pressure is measured by a pressure sensor. Computer gets pressure signal and outputs control command to an electro-pneumatic proportional control valve (EPPCV), which controls airflow rate and process of chamber charging and discharging.
In fact, it is difficult for the PNPPSS to obtain desired dynamic and static performances because of the nonlinearity associated with air compressibility and the asymmetry of charging and discharging process. In addition, the parametric variation due to leakage, setting pressure and vacuum pumping speed will further complicate the problem. It is known that the distinct advantages of PID controllers are simple structure and robust performance [18]. However, it is difficult to achieve the ideal result for the conventional PID controllers due to the nonlinearities mentioned above. Fuzzy controller is a good candidate, since it is not based on the model of the process and the accurate model of the system is not required [19, 20]. Fuzzy rule based controllers are found to improve tracking performance over fixed gain PID by upwards of 70% [21], and have been applied to pneumatic systems [22, 23]. However, regular fuzzy controller is not suitable to the system due to its lack of adaption to wider operational range and serious asymmetry. To improve robustness and achieve consistent control performance, some auto adjusting mechanisms need to be introduced. Recently, many auto adjusting mechanisms for fuzzy controller have been presented [24,25,26,27,28], which offer better performance. In Refs. [24, 25], both the input and output scaling factors (SFs) were tuned with rule-base defined on the error and change in error of the controlled variable. Since the output SF has strong influence on the performance and stability of the system [26], some fuzzy logic controllers with auto-adjusting mechanism only tuned the output SF, which was regulated by a properly designed rule base [27, 28].
In this article, a fuzzy inference module is added to conventional PID controller to adaptively tune the PID gains. Further, an asymmetric fuzzy compensator is developed to online adjust output gain of the fuzzy PID controller. The charging or discharging state of chamber can be judged by the output of fuzzy PID controller. Thus, different from conventional adjusting mechanisms employing the error and change in error of the controlled variable as inputs, the current chamber pressure and the output of fuzzy PID controller, which are related to the system features, are chosen as input parameters of the asymmetric fuzzy compensator to improve adaptability.
The rest of this paper is organized as follows. The experimental setup and system characteristics are given in Section 2. Section 3 offers designing details of the fuzzy PID controller with asymmetric fuzzy compensator. In Section 4, experiments and results are provided to verify the proposed control method. Finally, conclusions are drawn in Section 5.