Fractional-Order Control of Pneumatic Position Servosystems

A fractional-order control strategy for pneumatic position servosystem is presented in this paper. The idea of the fractional calculus application to control theorywas introduced inmanyworks, and its advantages were proved. However, the realization of fractional-order controllers for pneumatic position servosystems has not been investigated. Based on the relationship between the pressure in cylinder and the rate of mass flow into the cylinder, the dynamic model of pneumatic position servo system is established. The fractional-order controller for pneumatic position servo and its implementation in industrial computer is designed. The experiments with fractional-order controller are carried out under various conditions, which include sine position signal with different frequency and amplitude, step position signal, and variety inertial load. The results show the effectiveness of the proposed scheme and verify their fine control performance for pneumatic position servo system.


Introduction
In the last decade, pneumatic servosystems are increasingly used and studied in a great deal of industrial applications because of a number of advantages over other servosystems in point of high-power weight and power volume ratios, high speed, low cost, and simple operational mechanism.However, the dynamic characteristics are complex and highly nonlinear due to compressibility of air, external disturbances such as friction and payload, and pressure supply variations 1, 2 .The compressibility of air results in very low stiffness leading to low natural frequency, and low damping system makes it difficult to be controlled.Especially, precise control of pneumatic position servosystem is more difficult in the presence of uncertainties and nonlinearities.In order to overcome those difficulties, many researchers have extensively studied the application of different control methods in pneumatic servosystems 3-10 .

System Model
The schematic diagram of pneumatic displacement servosystems is depicted in Figure 1.It is composed of control elements and actuating elements.The control valve is a 5-port proportional valve.
The mass flow rate through the orifice can be expressed as where P u , P d , R, C d , C k , and T are the pressures at upstream and downstream of the orifice, the gas constant, the flow coefficient, the dimensionless constant, and the absolute temperature, respectively.S is the effective area that changes according to the spool position.The flow function ϕ is defined as where k, θ are the ratio of specific heat and the ratio between the downstream and upstream pressure, respectively.The relationship between the pressure in cylinder and the rate of mass flow into the cylinder can be described by where V, x, and A are the volume of the air in chamber, the displacement of the rod, and the equivalent operational area of chamber, respectively.The overall dynamics of cylinder motion can be obtained by where a and b designate the chambers a and b, respectively.m, F, and F f are the mass of position, the external load, and the frictional fore, respectively.According to the above equations, we definite the system state vector X x, ẋ, P a , P b T , so the overall system state equations can be obtained by

Mathematical Problems in Engineering
Based on the above-system model analysis, it is obvious that the pneumatic servosystem possesses the inherent nonlinearity and parameter uncertainty because of air compressibility, uneven distributed, and large change friction.The precise dynamic system model required by control qualification is difficult to be obtained.The friction and other uncertainties in pneumatic position system are always considered as external disturbance and then compensated it using the value estimated by the observer technology 7 .Finally, the better control performance can be achieved in comparison to other conventional control methods such as PID controllers.However, they require a great deal of computation, and the cost of control system is largely increased, which are not expected in the real industrial applications.Besides, the dominant control methods working in the pneumatic position servosystem are based on the PID controllers.It is important to further improve the performance of PID controllers for the precision position control of pneumatic system, which would contribute significantly to the real industrial applications.Therefore, the ideas of fractional calculus improving the performance of traditional PID control strategy are introduced to overcome the difficulties in controlling pneumatic position servosystem.

Fractional-Order Control
Fractional-order control systems are described by fractional-order differential equations.Fractional calculus allows the derivatives and integrals to be any real number.The theory of fractional-order derivative and integral was developed mainly in the 19th century.It just has been in the last decades when the use of fractional-order operators and operations has become more and more attractive among many research areas.However, applying fractionalorder calculus to control engineering is a recent focus of interest.

Fractional Calculus
Fractional calculus is a generalization of integration and differentiation to nonintegerfractional -order fundamental operator: where a and t are the limits, and α is any real number and the order of the operation.There are two common definitions for the general fractional differentiation and integration, such as the Grunwald-Letnikov GL definition and the Riemann-Liouville RL definition 18 .The GL definition is perhaps the best known one because it is most suitable for the realization of discrete control algorithms.The GL fractional derivative of continuous function f t is given by where x is a truncation and x t − m /h; α j is binomial coefficients, it can be replaced by Gamma function, For convenience, Laplace domain notion is usually used to describe the fractional integrodifferential operation.The Laplace transform of the fractional derivative of f t is given by where F s is the Laplace transform of f t .The Laplace transform of the fractional integral of f t is given as follows: Obviously, the Fourier transform of fractional derivative can be obtained by substituting s with jω.

Fractional-Order Controllers
The differential equation of fractional-order PI λ D δ FOPID controller 19 is described by where K p , K i , and K d are the proportional, integral, and derivative coefficients, respectively.λ, δ are the orders of integral and derivative.The continuous transfer function of FOPID is obtained through Laplace transform, which is given by It is obvious that the FOPID controllers not only need to design three parameters K p , K i , and K d , but also to design two orders λ, δ of integral and derivative controllers.The various design methods of the FOPID controllers have been investigated, such as the crossover frequency and phase margin 19 , dominate pole in complex plane 19 , the twostage approach 14 , and the intelligent optimization method 20, 21 .The orders λ, δ are not necessarily integer, but any real numbers.As shown in Figure 2, the FOPID controller generalizes the conventional integer order PID controller and expands it from point to plane.This expansion could provide much more flexibility in PID control design.

Discretization Methods
To realize fractional-order controllers perfectly, all the past inputs should be memorized.Derived from Grunwald-Letnikov definition, the numerical calculation formula of fractional derivative can be achieved as where L is the length of memory, T , the sampling time, always replaces the time increment h during approximation.The weighting coefficients b j can be calculated recursively by With generating function s ω z −1 , the fractional-order differentiator s α can be transformed from s domain to z space.The well-known s → z schemes include Euler, Tustin, and Al-Alaoui methods.To obtain the coefficients of the approximation equations for fractional calculus, we can perform PSE or CFE calculation.If adopting PSE method, the approximation equations as FIR filter structure can be obtained, while adopting CFE method, the approximation equations as IIR filter structure.Through the pioneering research 24 , it has been shown that the low-order approximation equations with IIR structure can achieve the excellent approximating results, which can be obtained only by the high-order approximation equations with FIR structure.That is to say, the CFE method is preferable to PSE one.What is more, the experimental results indicate that the results of Al-Alaoui and Euler using CFE are suitable to physical applications in control engineering.We consider the Al-Alaoui operator as generating function and then can perform CFE; the discretized result is as follows: where CFE{u} denotes the continued fraction expansion of u; p and q are the orders of the approximation; P and Q are polynomials of degrees p and q.Normally, we can set p q n.The above-FOPID controller 3.7 can be approximated using discretization methods, which is given by where w i z is the discrete approximation equation of fractional-order integral s −λ , and w d z is the discrete approximation equation of s δ .The greater the truncation order, the better the approximation.Namely, the discretized model with higher order is more close to the real fractional-order systems.

Experiments
The experimental framework of fractional-order control for pneumatic system is shown in Figure 3.It mainly consists of pneumatic cylinder SMC, CDA50-600-PPV-A , 5-port proportional control valve Festo, MPYE-5-1/4-010B , linear positioner Festo, MLO-POT-600-TLF , two pressure transducers PT351-0.6MPa-0.3 , multifunctional data acquisition board Advantech, PCL-812PG , and industrial computer Advantech 610 .The linear positioner with a resolution of 0.5 mm was employed to measure the position.It can output the voltage signal from 0 to 10 V proportional to the position of pneumatic cylinder.The output of sensors is passed to computer through data acquisition with a sampling frequency of 1000 Hz.The control output is assigned to the proportional valve using the D/A channel of the data acquisition.It can adjust flow rate according to input voltage 0-10 V by changing the spool position.When input voltage is a half of the nominal value, the flow rate is theoretically equal to zero, so that the rod will stop.However, the voltage of stopping the rod is about 5.3 V in the proposed experiment.Additionally, the inaction region of pneumatic position servosystem is 4.9∼5.8V, that is to say, the pneumatic actuator only slowly creeps in this region.In order to improve the response speed, the inaction region is compensated in the design of control algorithms.The compensating scheme is given as follows: where Δ u , U FOC are the output of current control system and fractional-order controllers, respectively.P e is the position error between target value and current sampling value.
The approximation approach of fractional-order controllers in the following experiments is described in 3.11 .The sampling period in digital realization is 0.001 s.For example, the discrete results of fractional-order operator s −0.4 can be achieved as

4.2
According to the direct discretization methods using CFE, the FOPID controller can be approximated by where U k is the current output of the FOPID controller, l is the memory length, W −λ i k, k − l and W δ d k, k − l are the discrete approximation equation of s −λ and s δ , respectively.The previous research indicates that the low-order approximation equations using CFE can achieve the excellent approximating results 24 .So the experiments adopt direct discretization methods using CFE.From 4.3 , it is viewed that the implementation of FOPID controller algorithm in Labwindows/CVI requires the last l 1 error input, the last l output from fractional integral and derivative parts.They are designated as e k , e k−1 , . . ., e k−l , Then the FOPID controller output can be rewritten as The flowchart of FOPID controller algorithm is summarized as follows: It is obvious that the realization of FOPID controller algorithm requires five parameters of λ, δ, K p , K i , and K d in advance.Under the understanding of system model, the parameters of FOPID controllers can be designed through various methods, such as dominate pole, the two-stage approach, and intelligent optimization method.It is proposed that particle swarm optimization-based FOPID controllers design method is effective in our former research 20 .However, pneumatic position servosystem is nonlinear and parameter variable in backand-forth movement.It is difficult in exactly modeling pneumatic position servosystem.Meanwhile, the system model of back and forth movement is different.So the parameters of FOPID controllers are obtained by trial-and-error methods in the following experiments.

Classical PID Control Response
The above compensating scheme in 4.1 is adopted in classical PID control of pneumatic position servosystem.The parameters of PID controllers are also obtained by trial-and-error methods.Through experiment, the parameters of PID controllers are set as K p 0.8, K i 0.001, and K d 0.1.Figure 4 shows the experimental result of step position response from 350 mm to 450 mm with classical PID controllers.Figure 5 shows the result from 470 mm to 300 mm, and Figure 6 indicates the result from 400 mm to 300 mm.The maxim overshoot is 6.7 mm in Figure 4, while 18 mm in Figure 5.It can be seen that step position back-andforth tracking with PID controllers reveals comparatively large steady error.The fluctuating position tracking will result in the huge variation of cylinder pressure.The creep movement in pneumatic position tracking is clearly exhibited in classical PID control.

Different Fractional-Order Response
In the position tracking of step signal, the different results of fractional-order control are recorded, which are shown in Figure 7.The FOPID controllers PI λ D δ are implemented, whose fractional integral order λ varies from 0 to −1.It is obvious that the pneumatic displacement response result is excellent without creep, when λ is equal to −0.4.To overcome the creep, the following experiments adopt λ −0.4.Using traditional PID controllers, it is difficult to get rid of the creep phenomena in control.In general, only pressure compensation can remove it.But fractional-order controllers exhibit fine characteristics in this area.

Position Tracking in Various Conditions
For detecting the robust performance of FOPID controllers, the experiments with the external inertial load change are carried out.The M depicts the mass of inertial load in Figure 8.The pneumatic position system with M 3.3 kg response quickly and its overshoot only has 5 mm.When M is equal to 13.3 kg, the time of stabilizing the system has about 1.2 second, and the overshoot is limited in the range of 20 mm.As can be seen from Figure 8,  The x represents the practical position using fractional-order control strategy.In Figure 9, the desired position signal with 50 mm amplitude and 0.15 Hz frequency is tracked.It can be seen that the system has the high speed of response, and the dynamic error is confined in the range of 5 mm.When the frequency f is equal to 0.25 Hz, the error always does not reach to 20 mm.The pneumatic position servosystem with fractionalorder control possesses the fine tracking ability when the signal frequency is comparatively low.It is clear that the position tracking using the FOPID controllers is effective.

Conclusions
The fractional-order control of pneumatic position servosystem is proposed to solve the strong nonlinearity and low natural frequency problem.Fractional calculus provides the profitable extension of traditional control strategy and attracts much attention in control engineering.The experimental equipment for fractional-order control of pneumatic system is established, and how to digitally realize the software algorithm of fractional-order control is analyzed.Finally, the experiments under various conditions are carried out.The results verify the fine control performance for pneumatic position servosystem with the nonlinearity and parameter uncertainty.
sample the current value and calculate the error e k 1 , 3 update the error sequences e k − l e k − l 1 , ..., e k e k 1 , 4 calculate u i k 1 and u d k 1 from the discrete approximation equation, 5 update the output sequences from fractional integral and derivative parts of FOPID controllers