Читать книгу Smart Systems for Industrial Applications - Группа авторов - Страница 42

The flexibility of proportional integral differential (PID) controller is less, when the reference and other conditions of the system change considerably. The system has the following advantages as easy of maintenance, spotlessness, PWR, and modest assembly which has been used broadly in automation application as food industries, medical, mechatronics, and bio-engineering. Due to essential compressibility of airflow through orifice valve in cylinder, movement of piston based on the position, variation of system parameters yields a nonlinear system with uncertainties. In nonlinear PID (NPID) controllers, the variation of nonlinear gain is exploited for greater accuracy. Literatures show that fractional-order PID (FOPID) controller, which combines the concept of fractional system theory and integer-type PID (IPID) controller gives better response than standard PID controller. But the tuning of controller parameters in FOPID is tougher than IPID. If these parameters are not tuned accurately, then the system performance will be poor [2, 10]. Many optimization techniques such as GA, MFA, and PSO are used to tune the parameters of FOPID to improve the system response [11, 12]. The numerous governing techniques are proposed for pneumatic control system as PID controller, robust control, sliding mode control. Due to its reliability and control mechanism, FOPID control is commonly used in industries [18]. The implementation of PID are widely used in ON/OFF solenoid valve position which includes constrained integral term, forward loop position, compensation of friction element, and the performance indices of the function compare with the solenoid valve. The flexibility in PID controller is reduces due to its nonlinearity. In NPID controller, the variation of nonlinear gain is exploited for greater accuracy. In recent days, the numbers of intelligent control techniques are developed to progress the accuracy of the system with trajectory tracking. Neural control–based PID has the proposed compensation under various load operating conditions used to get optimized design in PID controllers.

The sliding mode adaptive control provides the stability in system parameters to achieve desire performance [5]. 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.