Smart Systems for Industrial Applications. Группа авторов

In pneumatic system with back stepping adaptive sliding control, the parameters are not essential to its design. This makes the superior control than other supplementary techniques. In many systems, the gain control is not specified, while controlling the system would not give a proper approach. Adaptive back stepping approach is used to control the performance of the system with positioning. This approaches need not requires related information about the parameters of the system and gain control. Most of the control techniques are adaptive control, intelligent control, and sliding mode control results to improved system performance, even though the controller gives better response they have high value of computational cost related to PID. Nowadays, FOPID controllers have attracted more with its collective performance with the Podlubny’s work in which the concept being demonstrated with improved performance than conventional PID controllers. Based on effort of Podlunby, FOPID would give enhanced results based on their control mechanism in pneumatic servo system. Combined fractional position system and integer-order proportional integral differential (IPID) controller are constructed as FOPID. Additionally, two parameters are available in FOPID compared with IPID. The tuning of controlled parameters remains difficult in this type. Though the parameters are not optimized, FOPID is used to control the pneumatic position system whose tracking accuracy decreases. The literature work gives this problem in integer order model. Intelligent controller is used to get optimized solution in FOPID.

      In order to get the improved gain value and superior control, MATLAB/ Simulink is developed to estimate the working of the converter. Next, the fitness function and objective functions are used to control the stable position of transient state and steady state performance for different reference signals. The fitness functions are selected based on the different objectives and reference signals [13]. Finally, least distance for minimum points is suggested as the best non-dominate solution from the non-dominate individual to reach complete optimal parameters. The fractional-order system is specified as proposed work by replacing the integer order control. The FOC 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].

Genetic algorithm (GA) is used to optimize fractional-order system with evolutionary control [11, 12]. GA allows to tune all the parameters and get balance of different objectives to overcome the problems caused by the physical arrangement of the weight [9]. The proposed work insists the following for controlling the system with FOPID. GA searches the optimal factors of FOPID online for better performance than conventional method. In order to get the improved gain value and superior control, MATLAB/Simulink is developed to estimate the working of the converter. The fitness and objective functions are aids to control the stable position of system response with various references [13]. Smallest distance for minimum points is suggested as the best non-dominate solution from the non-dominate individual to reach complete optimal parameters. The fractional-order system is specified as proposed work by replacing the integer order control. The objective and fitness functions are aids to control the stable position of system response with various references [13]. An advanced algorithm such as GA allows to tune five control parameters of FOPID online to get balance of different objectives and hence overcomes the problems produced by the physical arrangements of the weights to multi-objectives [9, 16]. In this work, GA-based FOPID controller is implemented for pneumatic control positioning system, which gives satisfactory solution during uncertain conditions. Fractional-order model of the system is used for MATLAB simulation, where the tuning of proportional, integral, and derivative, λ and µ, are done using GA [14, 15]. The proposed algorithm is validated by implementing the optimized values of the controller in the hardware using microcontroller. Results shows that FOPID offers superior control over IPID for different conditions and the changes in throttle control.

2.2 Pneumatic Servo System

      Pneumatic system has numerous advantages, such as smooth construction, consistency, ruggedness, and easy maintenance [3]. They are commonly used in automated industries. Due to essential compressible gas, there is a difficulty in controlling the gas out flow using valve, chamber friction, and inconstancy in system parameters. They are fundamentally nonlinear time-varying systems [3, 10]. In practical, PID controller is used to modify the acceleration feedback and compensate the nonlinearity. The flexibility of PID control is weak, when the reference and other conditions of the system changes considerably [6]. The proposed method will give satisfactory solution about uncertain conditions. Error changes due to the different conditions and the changes in throttle control. For other intelligent control techniques, it provides better result, but high cost, related to PID control [19]. The FOC 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