Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 2342391, 6 pages
https://doi.org/10.1155/2018/2342391

Research Article

Sliding Mode Control with Adaptive Fuzzy Compensation for Uncertain Nonlinear System

Lina Wang and Haihui Zhang

College of Mechanical and Electronic Engineering, Northwest A&F University, Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China

Correspondence should be addressed to Lina Wang; lina_wang@nwafu.edu.cn

Received 17 July 2018; Revised 27 November 2018; Accepted 9 December 2018; Published 27 December 2018

Academic Editor: Anna M. Gil-Lafuente

Copyright © 2018 Lina Wang and Haihui Zhang. 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.

Fuzzy sliding mode control as a robust and intelligent nonlinear control technique is proposed to control processes with severe nonlinearity and unknown models. This paper proposes a new adaptive tracking fuzzy sliding mode controller for nonlinear systems in the presence of fuzzy compensation. The main contribution of the proposed method is that the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. The fuzzy self-adaptive rate is derived through the Lyapunov method, and the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value. The performance of the proposed approach is evaluated for double joint rigid manipulator problems. The simulation results illustrate the effectiveness of our proposed controller.

1. Introduction

Fuzzy control method is widely used in control theory and control engineering systems due to its great theoretical value and successful applications in complex practical systems. Adaptive fuzzy control is efficient to control uncertain nonlinear system [1]. Since the adaptive fuzzy approaches can approximate the complex nonlinear functions through fuzzy rules, the application of the fuzzy logic to deal with the chattering problem is proven as an effective way; it has been used in many engineering applications, such as robot systems and active suspension system [2, 3]. Adaptive fuzzy control has two types: indirect adaptive fuzzy control and direct adaptive fuzzy control [4, 5]. In indirect adaptive fuzzy control, there is a common problem that controls compensation. In [6], introduce unwanted high gain at the control input. In direct adaptive fuzzy control, the fuzzy controller parameters can be modified by the adaptive mechanism. We need to decrease the high gain through choosing one limiter for control input. The compensation control was proposed for the direct adaptive fuzzy control [7]. The advantage of fuzzy logic is that it can easily integrate human control experience into the controller through fuzzy rules and realize high level controller by designing fuzzy rules [8]. Some researchers have made significant advances about adaptive fuzzy control schemes [9, 10]. The most direct adaptive fuzzy control with a robust control term is based on Lyapunov stability.

The Sliding Mode Control (SMC) was originally introduced for variable structure systems in continuous domain by Utkin [11, 12]. It is known to have robust to model uncertainty, parameter variations, and good disturbance rejection properties [13–15]. But its major drawback is the chattering problem in practical applications, such as robotics, aerospace, and chaos control [16–19]. Traditional methods that replace the relay control by a saturating approximation, integral sliding control, and boundary layer technique have been proposed to eliminate this chattering problem [20–22]. However, if systems uncertainties are too large, the SMC would require a high switching gain with a thicker boundary layer. In [23], we can see SMC techniques for direct adaptive fuzzy control. The adaptive fuzzy control method considered a discontinuous law and a robust control law, but the control bandwidth of the controller is so wide. It was effective to eliminate chattering, however, leading to the inferior tracking performance. It is well known that we can reduce the error by introducing a compensator into the system. In order to enhance the tracking performance, we can use some adaptive strategy for compensation. The universal approximation theorem shows that the fuzzy system is a new universal approximator besides the polynomial function approximator and neural network approximation [24–26]. The adaptive fuzzy SMC has simple control structure, online learning ability, and model-free feature [27]. Here, we developed an adaptive fuzzy compensator SMC to control double joint rigid manipulator system. It has the advantage of representing system’s nonlinear functions without the dynamic model requirement.

In this paper, during the control of the nonlinear system, the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. The fuzzy self-adaptive rate is derived through the Lyapunov method; the convergence properties of the tracking error are analytically proven. The fuzzy self-adaptive rate is derived through the Lyapunov method, and the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value. Combining the SMC with adaptive fuzzy compensation method, the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value.

The rest of this paper is organized as follows. Section 2 presents the system descriptions and problem formulation method. Section 3 introduces our proposed controller scheme. Simulation results are included in Section 4. Section 5 provides the concluding remarks.

2. System Description and Problem Formulation

Consider the uncertain nonlinear systems as follows [28]:

where is inertia moment, is centripetal force and the goth moment, is gravity component, and is consisted by friction , disturbance , and the load change uncertainties.

Because the fuzzy system has universal approximation property[29–31], we can use to . Aiming to fuzzy system input and , design five fuzzy sets, that is, , , and ; then we can obtain fuzzy rules. The following two steps are used to construct the fuzzy system ; the first, aiming to the variable , defines fuzzy sets [10]. The second, using fuzzy rules to construct fuzzy system , then the fuzzy rule is

where , , , is the fuzzy set of the conclusion. Then the first step and the 25th fuzzy rule can be expressed as

Fuzzy inference process takes the following four steps: using the product inference engine to realize the premise inference of the rule, the inference result is .

Use singleton fuzzifier to calculate , that is, the function value of the x-coordinate value corresponding to the maximum value (1.0) of the membership function.

Using product inference engine to realize the inference of rule premise and rule conclusion, then the inference result is . Calculate all the fuzzy rules; then the output of fuzzy system is .

Use average defuzzizer to get the output of fuzzy system:

where is the membership function of . Make free parameter and put it in set . Introduce the fuzzy basis vectors , than (5) is as follows:

where is fuzzy basis vectors; among the element is

Then using fuzzy system to approximate (1) , it can be shown

3. Controller Design

Assume that , , and are known and all state variables can be measured. Define SMC function [17, 32]

where is positively definite matrix and is tracking error, .

Define

Define Lyapunov function [33, 34]

where , is ideal parameter, and . The Gamma i matrix is symmetric and positive definite.

Due to , then

Then

Among is unknown nonlinear function, using to MIMO fuzzy system to approximate .

3.1. Adaptive Control Law Design

Design adaptive law

where , , , and

Fuzzy approximation error is

Substitute (14) into (13), then

where , is the basic vector of fuzzy system, and .

Adaptive control law is [35]

Then

Because approximate error is so small, when is big enough, we can guarantee . When and , according to LaSalle invariant theory, the closed-loop system is asymptotically stable, , and .

3.2. Robust Adaptive Control Law

In order to eliminate the influence of the approximation error and ensure the stability of the system [36, 37], robust items should be adopted in the control law. Here we design robust adaptive law

where and , .

Add (20) into (17); then

If we take MIMO fuzzy system to approximate , then we aim to every input variable design membership functions.

3.3. Fuzzy Compensation SMC for Friction

When only contain friction term, we can consider fuzzy compensation for approximate friction [26, 27, 38]. Due to the fact that is only related to the speed signal, the fuzzy system for approximate friction can be expressed as , and we can design control law according to traditional fuzzy compensation controller design method like (14), (18), and (20) [39, 40].

Design fuzzy adaptive control law as follows:

Design robust fuzzy adaptive control law as follows:

Design adaptive law as follows:

Fuzzy system can be designed

4. Illustrative Results and Discussion

Aiming to double joint rigid manipulator, the kinetic equation is [28]

among

Make , , and . Take system parameter , , , and . The control objective is to make the output of double joint rigid manipulator track desired trajectory and . Aiming for the fuzzy system inputs design five fuzzy sets separately, that is, , , and , then has fuzzy rules. Define membership functions is

where is , , 0, and , and is separately NB, NS, ZO, PS, and PB.

With the manipulator of friction fuzzy compensation, controller parameter is , , , and . Taking the initial state of the system , the friction item is and the interference term is . During robust control law, . Taking robust control law (23), adaptive law is (24) and the simulation results are shown in Figures 1–4.

Figure 1: Double joint angle position tracking.

Figure 2: Double joint angular velocity tracking.

Figure 3: Double joint friction and compensation.

Figure 4: Double joint control input.

5. Conclusion

In this paper, a new adaptive fuzzy SMC scheme based on fuzzy compensation is proposed to control a nonlinear system. Use fuzzy system to realize adaptive approximation of the unknown part of the model. In order to show the ease of use and performance of the proposed method, it has been used to control the double joint rigid manipulator. The stability of the overall system was proved by the Lyapunov method and simulation results showed that the designed adaptive fuzzy SMC controller performs quite well in set-point tracking.

Data Availability

We use Matlab programming and build mathematical model and simulation. The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This journal was supported by the Chinese Universities Scientific Fund (2452016154), Test Demonstration Station Technology Achievements Promotion Project (TGZX2017-33), the China Postdoctoral Science Foundation (2017M623253), the National Natural Science Foundation of China (61703231), and the Fundamental Research Funds for the Central Universities (2452017128).

References

X. Kang, W. Hao, H. Zhang, X. Qi, and C. Yin, “A New Approach to Derive Priority Weights from ADDitive Interval Fuzzy PREference Relations Based on Logarithms,” Mathematical Problems in Engineering, vol. 2018, Article ID 3729741, 11 pages, 2018.

E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, 1975.

S. W. Kim and J. J. Lee, “Design of a fuzzy controller with fuzzy sliding surface,” Fuzzy Sets Systems, vol. 71, no. 3, pp. 359–367, 1995.

M. Azaza, C. Tanougast, E. Fabrizio, and A. Mami, “Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring,” ISA Transactions®, vol. 61, pp. 297–307, 2016.

H. F. Ho, Y. K. Wong, and A. B. Rad, “Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems,” Simulation Modelling Practice and Theory, vol. 17, no. 7, pp. 1199–1210, 2009.

Y. Lee and S. Zak, “Uniformly Ultimately Bounded Fuzzy Adaptive Tracking Controllers for Uncertain Systems,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 6, pp. 797–811, 2004.

. Yao-Chu Hsueh, . Shun-Feng Su, C. W. Tao, and . Chih-Ching Hsiao, “Robust Gain Compensative Control for Direct Adaptive Fuzzy Control System Design,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 661–673, 2010.

Y. Lu, “Adaptive-Fuzzy Control Compensation Design for Direct Adaptive Fuzzy Control,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3222–3231, 2018.

X. Lin, W. Zhang, and B.-S. Chen, “H∞ Robust Tracking Control of Stochastic T-S Fuzzy Systems with Poisson Jumps,” Mathematical Problems in Engineering, Art. ID 5257972, 14 pages, 2018.

A. Shahraz and R. Bozorgmehry Boozarjomehry, “A fuzzy sliding mode control approach for nonlinear chemical processes,” Control Engineering Practice, vol. 17, no. 5, pp. 541–550, 2009.

V. I. Utkin, “Sliding mode control: mathematical tools, design and applications,” in Nonlinear and optimal control theory, vol. 1932 of Lecture Notes in Math., pp. 289–347, Springer, Berlin, 2008.

V. I. Utkin, “Variable structure systems with sliding modes,” IEEE Transactions on Automatic Control, vol. 22, no. 2, pp. 212–222, 1977.

P. P. Sindhuja, B. S. Kumar, and K. S. Manic, “Fast terminal sliding mode controller for temperature control of an unstable chemical reactor,” in Proceedings of the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4, Chennai, December 2016.

J. Oliveira, A. Araujo, and S. Dias, “Controlling the speed of a three-phase induction motor using a simplified indirect adaptive sliding mode scheme,” Control Engineering Practice, vol. 18, no. 6, pp. 577–584, 2010.

W. Garcia-Gabin, D. Zambrano, and E. F. Camacho, “Sliding mode predictive control of a solar air conditioning plant,” Control Engineering Practice, vol. 17, no. 6, pp. 652–663, 2009.

A. S. Yamashita, P. M. Alexandre, A. C. Zanin, and D. Odloak, “Reference trajectory tuning of model predictive control,” Control Engineering Practice, vol. 50, pp. 1–11, 2016.

W. Qi, J. H. Park, J. Cheng, and Y. Kao, “Robust stabilisation for non-linear time-delay semi-Markovian jump systems via sliding mode control,” IET Control Theory & Applications, vol. 11, no. 10, pp. 1504–1513, 2017.

T. Zhongda, L. Shujiang, W. Yanhong, and S. Yi, “A prediction method based on wavelet transform and multiple models fusion for chaotic time series, Chaos,” Solitons Fractals, vol. 98, pp. 158–172, 2017.

A. F. Amer, E. A. Sallam, and W. M. Elawady, “Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators,” Applied Soft Computing, vol. 11, no. 8, pp. 4943–4953, 2011.

I. Baik, K. Kim, H. Kim, G. Moon, and M. Youn, “Robust nonlinear speed control of PM synchronous motor using boundary layer integral sliding control with sliding load torque observer,” in Proceedings of the PESC Record. 27th Annual IEEE Power Electronics Specialists Conference, pp. 1242–1247, Baveno, Italy.

S. Frikha, M. Djemel, and N. Derbel, “A New Adaptive Neuro-sliding Mode Control for Gantry Crane,” International Journal of Control, Automation, and Systems, vol. 16, no. 2, pp. 559–565, 2018.

J.-X. Xu, “Discussion on: “Integral sliding mode control for a class of uncertain switched nonlinear systems'' [MR2656985],” European Journal of Control, vol. 16, no. 1, pp. 23–25, 2010.

Y.-C. Chang, “Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H∞ approaches,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 2, pp. 278–292, 2001.

Z. Tian, S. Li, and Y. Wang, “T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm,” International Journal of Modelling, Identification and Control, vol. 25, no. 4, pp. 323–334, 2016.

K. Lian and Y. Dong, “Sliding Mode Control for Electromagnetic Satellite Formation Based on,” in Proceedings of the RBF Neural Network, Sixth International Conference on Instrumentation Measurement, Computer, vol. , no, pp. 532–535, 2017.

W. M. Bessa, A. S. de Paula, and M. A. Savi, “Sliding mode control with adaptive fuzzy dead-zone compensation for uncertain chaotic systems,” Nonlinear Dynamics, vol. 70, no. 3, pp. 1989–2001, 2012.

S. Huang and H. Chen, “Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control,” Mechatronics, vol. 16, no. 10, pp. 607–622, 2006.

L. Jinkun, Radial Basis Function Neural Network Control for Mechanical Systems, Tsinghua University Press, Beijing, China, 2013.

X. Zheng, X. Wang, Y. Yin, and L. Hu, “Stability analysis and constrained fuzzy tracking control of positive nonlinear systems,” Nonlinear Dynamics, vol. 83, no. 4, pp. 2509–2522, 2016.

A. Karami-Mollaee and H. Tirandaz, “Adaptive Fuzzy Fault Tolerant Control Using Dynamic Sliding Mode,” International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp. 360–367, 2018.

S. D. Nguyen, S. Choi, and T. Seo, “Adaptive fuzzy sliding control enhanced by compensation for explicitly unidentified aspects,” International Journal of Control, Automation, and Systems, vol. 15, no. 6, pp. 2906–2920, 2017.

Y. Wang, H. Shen, H. R. Karimi, and D. Duan, “Dissipativity-based Fuzzy Integral Sliding Mode Control of Continuous-Time T-S Fuzzy Systems,” IEEE Transactions on Fuzzy Systems, pp. 1–1.

W. Qi, J. H. Park, J. Cheng, Y. Kao, and X. Gao, “Anti-windup design for stochastic Markovian switching systems with mode-dependent time-varying delays and saturation nonlinearity,” Nonlinear Analysis: Hybrid Systems, vol. 26, pp. 201–211, 2017.

J. Oliveira, J. Boaventura-Cunha, P. Moura Oliveira, and H. Freire, “A swarm intelligence-based tuning method for the sliding mode generalized predictive control,” ISA Transactions®, vol. 53, no. 5, pp. 1501–1515, 2014.

B. K. Yoo and W. C. Ham, “Adaptive control of robot manipulator using fuzzy compensator,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 2, pp. 186–199, 2000.

N. Noroozi, M. Roopaei, and M. Z. Jahromi, “Adaptive fuzzy sliding mode control scheme for uncertain systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 14, no. 11, pp. 3978–3992, 2009.

M. Roopaei, M. Zolghadri, and S. Meshksar, “Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems,” Communications in Nonlinear Science and Numerical Simulation, vol. 14, no. 9-10, pp. 3670–3681, 2009.

W. Chang, F. Hsu, and C. Ku, “Complex performance control using sliding mode fuzzy approach for discrete-time nonlinear systems via T-S fuzzy model with bilinear consequent part,” International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp. 1901–1915, 2017.

X. F. Jiang and Q.-L. Han, “Robust control for uncertain Takagi-Sugeno fuzzy systems with interval time-varying delay,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 2, pp. 321–331, 2007.

L. Wang and H. Lam, “A New Approach to Stability and Stabilization Analysis for Continuous-Time Takagi–Sugeno Fuzzy Systems With Time Delay,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 2460–2465, 2018.