has the better response than classical PID controllers [3, 6]. The FOPID controller is established by system with fractional-order control and IPID [1]. The tuning of control parameters is more tough in FOPID than IPID. In FOPID whose parameters are not optimized accurately, it will give poor performance of the system [1, 2]. In NPID controllers, the variation of nonlinear gain is exploited for greater accuracy [8].
In conventional pneumatic servo systems have been used in many fields [14, 17], such as the active suspension system on the Shinkansen bullet train, moulding machines for glass lenses, and amusement robots, because of the numerous advantages of high power, compliant property, and good force controllability. However, the characteristic of a pneumatic servo is nonlinear, which make control difficult [21, 22]. Therefore, control methods of a pneumatic servo, applying advanced control theories such as fuzzy control or robust control, have been investigated.
Pneumatic position servo system comprises of cylinder (Figure 2.1), variable resistor, solenoid valves, air pump, and piston. The compressed air is pumped through air pump and passed through the chamber A or Bin the cylinder, where the valve is regulating the flow level. The incoming valve regulates the mass flow in each chamber to get pressure difference among two chambers [15]. The difference in pressure will drive the piston and payload and the velocity of the system load is controlled by rheostat with the piston [1, 4]. The aim of the controller is to track the position of the piston and payload through a desired path [2] and its dynamic characteristics are represented by a fractional-order model. A 230-V supply is stepped down to 5 V using step down transformer and rectified using bridge rectifier and then given to variable resistor. The PIC microcontroller receives the position of the piston and converts it from analog to digital form before it is given to PC using serial bus. A 12-V relay board is used for tripping the supply when the position of the load is not at zero in initial position.
Figure 2.1 Pneumatic position servo system.
2.3 Existing System Analysis
The pneumatic position is controlled using self-regulation of NPID (SNPID) controller. The performance of the system is developed with the specific changes of nonlinear gain in NPID. The various test has taken and the error signal is reprocessed continuously for different values by self-regulation non-linear function. The controller is applied with changing of loading of pressure, compared with NPID and classical PID evaluation. Simulation and various experimental studies have been implemented using SNID. The initial performance of the system has been examined through simulation. Test has been conducted for various level of displacement to find out the consistency of the system performance. Different benchmark experiments and different load condition are conducted for system validation and slight differences can found them in transient state. The system using SNPID specifies excellent performance in accuracy, robust control, and fast response compared with other types. Also, SNPID provides minimum value of steady state error with minimal of peak overshoot. The servo system has the performance characteristics of time-variant, nonlinear, disturbances, and variation in parameters that will make the system is very difficult to control. Classical PID control does not provide the better accuracy of the system for various external disturbances.
Next, the neural network control combined with PID for pneumatic system was analyzed. Based on the learning rule, the system results are compared with the classical PID controller. Survey on neural network gives improved performances than PID control. It has fast response, adaptability, robustness, and reliability. Neural network + PID control performs better based on optimization of system response compare with conventional PID. It comprises inherent aspects as neurons, topological structure, and knowledge-based learning rule for improving system operation. Due to lack of control in pneumatic servo system, it is tough to adopt with conventional method. Literature research shows neural network control of pneumatic servo system has fast response, suitable static and dynamic control, robustness, and good adaptability. Neural network control can be used a system with complex, nonlinear, and uncertainty that has extensive challenging applications. It is used to tuning of FOPID control with particle swarm optimization (PSO) technique which gives strong stochastic optimizing output. It depicts the movement of swarm particles around search space to solve the problem. Comparison of SNPID, neural + PID, and optimization of FOPID using PSO techniques was analyzed.
The proposed approach defines the position control of pneumatic system using FOPID optimized with GA. The following parameters to be tuned up for optimum response. It consists of proportional, integral, and derivative gain, fractional-order integrator, and fractional-order differentiator. The approach defines the implementation of the system with fast tuning optimum parameters. Optimized FOPID-based [20] pneumatic servo system influences the improved performance of the system. In last decades, fractional-order dynamic system and various types of controllers have been studied in engineering. FOPID techniques were enhanced by Podulbny. He demonstrated the system output which compared with the classical PID controllers. PSO uses number of swarm particles that searching the optimal solution. Using fitness function, the system parameters can be optimized with fewer numbers of iterations. In PSO simulation, results show the better results than other methods. The proposed method can apply in practical system for their précised control of high power to weight ratio. The techniques give efficient results in optimal design controllers. Pneumatic servo system is used in automatic control industries. It requires high accuracy of control due to their compressibility of gas and friction. The rods less cylinder with two chambers are used. The controllers are not required any pressure sensors and reference value before connecting with the system. It has no preceding idea and uncertainty of the system. Based on the results, the proposed method achieves superior mechanism over sliding mode controllers (SMCs). The controllers track three reference signals with better précised output compare with SMCs.
Proposed linear model pneumatic system controller has main topologies: first, designing of adaptive back stepping controller without having any knowledge about actual model of the system; second, the model can able to design a controller without having previous model information of reference signal; and third, system can design a controller without expensive of pressure sensors and it has better practical application prospects. The control parameters of power factor correction (PFC) is designed by a small signal model. The output of PFC converter gives nonlinear response. GA is used to optimize the parameters of PFC converter for desired operation. From the assigned fitness function the quasi optimal control parameters are obtained. The fitness function of individual parameters of PFC converter is executed in MATLAB M-file. GA is used to get optimal parameters around search space through numbers of iteration. Simulation results shows the transient response of the system by optimizing the parameters. The control parameters of PFC converter are optimized using GA. MATLAB coding is established to calculate the performance of the PFC converter for various control parameters. After optimizing, the response of the system has reduced overshoot, settling time, better transient and steady state response. The proposed method is used to optimize the even topology of the converter and provides suitable approach for evaluating power electronic circuits.
Drawbacks:
The accuracy of integer order PID controller is low.
The existing system using integer order PID controller requires high power consumption.
The control performance of IPID controller with improved control parameters derived by GA can is poor in the sensor accuracy and energy consumption.
The robustness of the system is poor.
2.4 Proposed Controller and Its Modeling
