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Journal of Control Science and Engineering
Volume 2012 (2012), Article ID 964242, 5 pages
http://dx.doi.org/10.1155/2012/964242
Research Article

Discrete Model Reference Adaptive Control System for Automatic Profiling Machine

College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian 116605, China

Received 3 December 2011; Revised 15 May 2012; Accepted 6 June 2012

Academic Editor: Yunjun Xu

Copyright © 2012 Peng Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.

1. Introduction

Automatic profiling machine is a movement system which has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. The traditional linear control methods, such as PID, cannot meet present needs or requirements of advanced performance. The adaptive control not only solves the problem that the control plant cannot be observed directly, but also improved the abilities of resisting interference [1]. The applications of adaptive control to movement system are widespread [24], but it is infrequent in automatic profiling machine. A practical adaptive controlling scheme is proposed for automatic profiling machine in [5]. In this paper, a discrete model reference adaptive control (MRAC) method is applied to automatic profiling machine. The discrete control method is convenient for program and contributes to application of adaptive control theory in practice.

2. The Mathematics Model

The control system is double closed-loop control system whose current loop is PI control and speed loop is MRAC. The DC motor parameters [6] of automatic profiling machine are shown in Table 1. The equivalent plant of speed loop is consisted of current loop and motor, and the transfer function is 𝐺(𝑠)=2377011.5𝑠2.+2252.87𝑠+5747.23(1)

tab1
Table 1: The parameters of DC motor.

3. The Design of MRAC System

The difference equation of the plant is 𝐴𝑧1𝑦(𝑘)=𝑧1𝐵𝑧1𝑢(𝑘),(2) where 𝐴𝑧1=1𝑛𝑖=1𝑎𝑖𝑧𝑖𝑧,𝐵1=𝑚𝑖=0𝑏𝑖𝑧𝑖,(3)𝑦(𝑘) and 𝑢(𝑘) are the output and input of the plant, respectively; 𝑧1 is delay operator; 𝑘 is the discrete-time variable.

The difference equation of the reference model is 𝐴𝑚𝑧1𝑦𝑚(𝑘)=𝑧1𝐵𝑚𝑧1𝑟(𝑘),(4) where 𝐴𝑚𝑧1=1𝑛𝑖=1𝑎𝑖𝑧𝑖,𝐵𝑚𝑧1=𝑚𝑖=0𝑏𝑖𝑧𝑖,(5)𝑦𝑚(𝑘) and 𝑟(𝑘)are the output and input of the reference model.

The output error and its prediction are given by 𝑒(𝑘)=𝑦(𝑘)𝑦𝑚𝑒(𝑘),(𝑘)=𝑦(𝑘)𝑦𝑚(𝑘),(6) where 𝑒(𝑘) and the other variables with “∘” represent the predictions.

The structure of the adaptive control system is shown in Figure 1 where 𝐻𝑧1=𝑛𝑖=1𝑖(𝑘)𝑧𝑖+1,𝐺𝑧1=𝑚𝑖=0𝑔𝑖(𝑘)𝑧𝑖,𝐹𝑧1=𝑛𝑖=1𝑓𝑖(𝑘)𝑧𝑖+1.(7)

964242.fig.001
Figure 1: Block diagram of MRAC system.

From Figure 1, the following relationship can be obtained 𝐵𝑚𝑧1𝑧𝑟(𝑘)=𝐻1𝑦𝑚𝑧(𝑘)+𝐹1𝑧𝑒(𝑘)+𝐺1𝑢(𝑘).(8)

Introduc (8) into (4): 𝐴𝑚𝑧1𝑦𝑚(𝑘)=𝑧1𝐻𝑧1𝑦𝑚𝑧(𝑘)+𝐹1𝑒𝑧(𝑘)+𝐺1𝑢.(𝑘)(9)

Subtract (9) from (2): 𝐴𝑧1=𝐴𝑒(𝑘)𝑚𝑧1𝑧𝐴1𝑧1𝐻𝑧1𝑦𝑚+𝑧(𝑘)1𝐵𝑧1𝑧1𝐺𝑧1𝑢(𝑘)𝑧1𝐹𝑧1𝑒(𝑘).(10)

According to the Hyperstability theory, the discrete system control laws [1] are 𝐼𝑖(𝑘)=𝐼𝑖(𝑘1)+𝜆𝑖𝑒(𝑘)𝑦𝑚(𝑘𝑖),𝑃𝑖(𝑘)=𝜇𝑖𝑒(𝑘)𝑦𝑚(𝑘𝑖),(11) where 𝑖=1,2,,𝑛;𝜆𝑖>0;𝜇𝑖𝜆𝑖2,𝑔𝐼𝑖(𝑘)=𝑔𝐼𝑖(𝑘1)+𝜌𝑖𝑔𝑒(𝑘)𝑢(𝑘𝑖1),𝑃𝑖(𝑘)=𝜎𝑖𝑒(𝑘)𝑢(𝑘𝑖1),(12) where 𝑖=1,2,,𝑚;𝜌𝑖>0;𝜎𝑖𝜌i2,𝑓𝑃𝑖(𝑘)=𝑞𝑖𝑓𝑒(𝑘)𝑒(𝑘𝑖),𝐼𝑖(𝑘)=𝑓𝐼𝑖(𝑘1)+𝑙𝑖𝑒(𝑘)𝑒(𝑘𝑖),(13) where 𝑖=1,2,,𝑛;𝑙𝑖>0;𝑞𝑖𝑙𝑖2.(14)

𝐸(𝑘) cannot be found directly in the operations above, so it can be replaced by 𝑒(𝑘). According to (10), 𝑒(𝑘) becomes 𝑒(𝑘)=𝑛𝑖=1𝑎𝑖𝑓𝑖+(𝑘)𝑒(𝑘𝑖)𝑛𝑖=1𝑎𝑖𝑎𝑖𝑖̂𝑘𝑦𝑚+(𝑘𝑖)𝑚𝑖=0𝑏𝑖𝑔𝑖(𝑘)𝑢(𝑘𝑖1).(15)

The prediction error can be gained: 𝑒(𝑘)=𝑛𝑖=1𝑎𝑖𝑓𝐼𝑖+(𝑘1)𝑒(𝑘𝑖)𝑛𝑖=1𝑎𝑖𝑎𝑖𝐼𝑖̂𝑦𝑘1𝑚+(𝑘𝑖)𝑚𝑖=0𝑏𝑖𝑔𝐼𝑖(𝑘1)𝑢(𝑘𝑖1).(16)

Subtract (16) from (15) and link (11)~(13); the function becomes 𝑒(𝑘)𝑒(𝑘)=𝑛𝑖=1𝑙𝑖+𝑞𝑖𝑒2+(𝑘𝑖)𝑛𝑖=0𝜆𝑖+𝜇𝑖𝑦2𝑚+(𝑘𝑖)𝑚𝑖=0𝜌𝑖+𝜎𝑖𝑢2(𝑘𝑖1)𝑒(𝑘).(17) Equation (17) becomes (18) by calculating,𝑒𝑒(𝑘)=(𝑘)1+𝑛𝑖=1𝑙𝑖+𝑞𝑖𝑒2(𝑘𝑖)+𝑛𝑖=0𝜆𝑖+𝜇𝑖𝑦2𝑚(𝑘𝑖)+𝑚𝑖=0𝜌𝑖+𝜎𝑖𝑢2.(𝑘𝑖1)(18)

4. Simulation Studies

The reference model takes the form as follows: 𝑧1𝐵𝑚𝑧1𝐴𝑚𝑧1=0.1475𝑧1+0.1451𝑧211.95𝑧1+0.9512𝑧2.(19)

Make simulation according to the analysis above by SIMULINK. The structure of the adaptive control system is shown in Figure 2, and the parameters value are shown in Table 2.

tab2
Table 2: Parameter values of MRAC system.
964242.fig.002
Figure 2: Simulation block diagram of MRAC system.

The simulation results are shown in Figures 35. To make a calculation, the percentage overshoot is 6%, the rise time is 1 s, the settling time is 2 s (Δ=0.02). Figure 3 is shown that the maximal error value is less than 40 r/min, so the control plant could better track reference model. Input the interference signal into the front of plant transfer function between 10 s and 20 s. The simulation results (in Figure 4) indicate that the control plant could be stable in 2 seconds. Input white noise into the reference input of plant not the reference input of model, and the result is shown in Figure 5. Obviously the figure has no visible variation. So the control system has better abilities of resisting interference.

fig3
Figure 3: Output figures of simulation.
fig4
Figure 4: Output figure with interference signal.
964242.fig.005
Figure 5: Output figures with white noise.

5. Conclusion

The discrete model reference adaptive control system of automatic profiling machine is discussed in this paper. The results of simulation show that adaptive control system has favorable dynamic performance. The discrete design method is easy to realize by computer. The work in this paper will lay a foundation for the application of adaptive control in practice.

Acknowledgments

This paper was supported by the National Science and Technology Support Program Project (2009BAH41B05) and the Fundamental Research Funds for the Central Universities.

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