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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 863902, 8 pages
Adaptive Neural Networks Synchronization of a Four-Dimensional Energy Resource Stochastic System
School of Civil and Architectural Engineering, Liaoning University of Technology, Jinzhou 121001, China
Received 31 December 2013; Accepted 19 January 2014; Published 2 March 2014
Academic Editor: Ming Liu
Copyright © 2014 Duo Meng. 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.
An adaptive neural networks chaos synchronization control method is proposed for a four-dimensional energy resource demand-supply system with input constraints. Assuming the response system contains unknown uncertain nonlinearities and unknown stochastic disturbances, the neural networks and robust terms are used to identify the nonlinearities and overcome the stochastic disturbances, respectively. Based on stochastic Lyapunov stability and robust adaptive theories, an adaptive neural networks synchronization control method is developed. In the design process, an auxiliary design system is employed to address input constraints. Simulation results, which fully coincide with theoretical results, are presented to demonstrate the obtained results.
Energy resource system is a kind of complex nonlinear system. Over the last two decades, much attention has been paid to the chaos synchronization in this class system. Reference  established a three-dimensional energy resource demand-supply system based on the real energy resources demand-supply system in the East and the West of China. By adding a new variable to consider the renewable resources, a four-dimensional energy resource system was proposed in . The dynamics behaviors of the four-dimensional energy resource system have been analyzed by means of the Lyapunov exponents and bifurcation diagrams. Also the same as the above-mentioned power systems, this four-dimensional energy resource system is with rich chaos behaviors. The problem of chaotic control for the energy resource system was considered in . Feedback control and adaptive control methods were used to suppress chaos to unstable equilibrium or unstable periodic orbits, where only three of the system’s parameters were supposed to be unknown. Reference  investigated the robust chaos synchronization problem for the four-dimensional energy resource systems based on the sliding mode control technique. The control of energy resource chaotic system was investigated by time-delayed feedback control method in . Four linear control schemes are proposed to a four-dimensional energy resource system in . Based on stability criterion of linear system and Lyapunov stability theory, respectively, the chaos synchronization problems for energy resource demand-supply system were discussed using two novel different control methods in .
In many practical dynamic systems (including the energy resource demand-supply system), physical input saturation on hardware dictates that the magnitude of the control signal is always constrained. Saturation is a potential problem for actuators of control systems. It often severely limits system performance, giving rise to undesirable inaccuracy or leading instability [8, 9]. The development of control schemes for systems with input saturation has been a task of major practical interest as well as theoretical significance. The proposed approaches in [1–7] assume that all the components of the considered energy resource demand-supply systems are in good operating conditions and do not consider the problem of saturation. Reference  proposed two different chaos synchronization methods for a class of energy resource demand-supply systems with input saturation, but the response system in  did not contain unknown uncertain nonlinearities and unknown stochastic disturbances. It is well known that stochastic disturbances also often exist in many practical systems. Their existence is a source of instability of the control systems; thus, the investigations on stochastic control systems have received considerable attention in recent years [11–22]. Since the emergence of the stochastic stabilization theory in the 1960s, the progress has been constructed by a fundamental technical Itô lemma, and the control design for stochastic systems is more difficult compared with deterministic systems.
Motivated by the above observations, an adaptive neural networks chaos synchronization method is proposed for a four-dimensional energy resource demand-supply system with input constraints. Assume that the response system contains unknown uncertain nonlinearities and unknown stochastic disturbances. In the design, the neural networks and robust terms are used to identify the nonlinearities and overcome the stochastic disturbances, respectively. Based on Lyapunov stability, an adaptive synchronization method is developed in order to make the states of two chaotic systems asymptotically synchronized. The new auxiliary design system is employed to address input constraints. Numerical simulations are provided to illustrate the effectiveness of the proposed approach.
Compared with the existing results, the main contributions of the proposed method are as follows: (i) the controlled response system of this paper contains unknown nonlinearities, and the proposed method can solve the unknown nonlinearity problem by neural networks, but the methods of [1–8, 10] cannot solve this problem; (ii) the controlled response system of this paper contains stochastic disturbances, and the proposed method can solve the stochastic disturbances problem based on Itô’s lemma and stochastic LaSalle’s theorem, but the methods of [1–8, 10] cannot solve this problem; (iii) an auxiliary design system is employed to address input constraints problem, and the methods of [1–8] can solve this problem.
2. Energy Resource Chaotic System
The four-dimensional energy resource system can be expressed as follows (see [2, 4, 6]): where is the energy resource shortage in A region, is the energy resource supply increment in B region, and and are energy resource import in A region and renewable energy resource in A region, respectively; , , , , , and are parameters that are all positive real. The dynamics of this system has been extensively studied in [2, 4, 6].
When the system parameters are taken as the following values, this system exhibits chaotic behavior: , , , , , , , , , , , , and . Without the particular statement, these values are adopted in this whole paper. Figures 1, 2, and 3 show the phase portraits with initial conditions of , , , and .
3. Synchronization of the Energy Resource System
In this section, a controller will be designed in order to make the response system track the drive system. The drive system with subscript 1 is written as Assume that the controlled response system with subscript 2 contained uncertain nonlinearities (unknown smooth nonlinear functions) and unknown external stochastic disturbance, and it can be expressed as the following dynamics: where is the actual controller to be designed and is the plant input subject to saturation type nonlinearly. , , are uncertain functions, and is an independent standard Brownian motion defined on a complete probability space, with the incremental covariance .
Remark 1. If no input saturation, uncertain nonlinearities, and unknown external stochastic disturbance (i.e., ), , and ) are included in (3), then (3) becomes the chaotic systems studied widely, see [8, 10], where can be described as where is a known bound of .
To design an adaptive controller, the following basic assumption is made for the system (3).
Assumption 2. The disturbance covariance , where and is a known function.
To establish stochastic stability as a preliminary, consider a stochastic nonlinear system: where , is an independent -dimensional Wiener process, defined on the probability space , with the incremental covariance , and and are locally Lipschitz continuous in , uniformly in , with and , for all .
Lemma 3 (see [16, 17] (stochastic LaSalle’s theorem)). Consider (5) and suppose that there exists a twice continuously differentiable function , which is positive definite, decrescent, and radially unbounded, and another nonnegative continuous function such that the infinitesimal generator along (5) satisfies where denotes the matrix trace. Then, the equilibrium is globally stable in probability and
An RBFNN can approximate a continuous function : , where the input vector , weight vector , the NN node number , and , with being Gaussian functions, which have the form where is the center of the receptive field and is the width of the Gaussian function.
According to the literatures , the neural network (8) can approximate any continuous function over a compact set to arbitrary any accuracy as where is an ideal constant weight, is the bounded approximation error, and is defined as
4. Adaptive Synchronization of the Energy Resource System
For different initial conditions of systems (2) and (3), the two coupled systems can achieve synchronization by designing an appropriate control input . First, we define the synchronization error vector between systems (2) and (3) as where is filter signal and will be given later.
In this section, we assume that all the parameters of the energy resource system are unknown. For convenience, similar to , we define , , , and ; the system (14) can be rewritten as Define the dynamic system as Choose the following Lyapunov function candidate as where , , , , and .
Similar to [16, 17], the infinitesimal generator of along with the solutions of (15) is Design the actual controllers and parameters update laws of , , , , and as follows: where are positive design parameters. Consider the following: Substituting (19)–(38) into (18) results in
5. Simulation Results
In this section, external perturbations ; uncertain nonlinear , , , and . Consider and . RBFNNs, , contain 25 nodes, with centers evenly spaced in and width is 2. is assumed to be Gaussian white noise with zero mean and variance 1.0.
The initial values are chosen as , , , , , , , and , and the other initial values are chosen as zeros. The saturation values are , , and . Design parameters in controllers are , , , and . The simulation results are shown in Figures 4, 5, 6, 7, 8, 9, 10, and 11.
Remark 4. It is worth pointing out that the method of  cannot be used to control the systems of this paper. There exist three reasons: (i) the system of this paper is four dimensional and the system in  is three dimensional; (ii) the system of this paper contains stochastic disturbances, and the system in  does not contain them; (iii) the controlled response system of this paper contains unknown nonlinearities, and  does not contain them.
This paper has solved the synchronization problems of a class of unknown parameters four-dimensional energy resource system. The main features of the proposed algorithm are that (i) the problems of the input constraint have been solved by employing a new auxiliary system; (ii) the unknown nonlinearities and stochastic disturbances that existed in the response system have been overcome by the neural networks and some special robust terms, respectively; (iii) the stability of the energy resource demand-supply system has been guaranteed based on stochastic Lyapunov theory.
Conflict of Interests
The author declares that there is no conflict of interests regarding the publication of this paper.
This work was supported by the National Natural Science Foundation of China (no. 51308275) and the Foundation of Liaoning Educational Committee (no. L2012225).
- M. Sun, L. X. Tian, and Y. Fu, “An energy resources demand-supply system and its dynamical analysis,” Chaos, Solitons & Fractals, vol. 32, no. 1, pp. 168–180, 2007.
- M. Sun, Q. Jia, and L. X. Tian, “A new four-dimensional energy resources system and its linear feedback control,” Chaos, Solitons & Fractals, vol. 39, no. 1, pp. 101–108, 2009.
- M. Sun, L. X. Tian, Y. Fu, and W. Qian, “Dynamics and adaptive synchronization of the energy resource system,” Chaos, Solitons & Fractals, vol. 31, no. 4, pp. 879–888, 2007.
- C.-F. Huang, K.-H. Cheng, and J.-J. Yan, “Robust chaos synchronization of four-dimensional energy resource systems subject to unmatched uncertainties,” Communications in Nonlinear Science and Numerical Simulation, vol. 14, no. 6, pp. 2784–2792, 2009.
- M. Sun, L. X. Tian, and J. Xu, “Time-delayed feedback control of the energy resource chaotic system,” International Journal of Nonlinear Science, vol. 1, no. 3, pp. 172–177, 2006.
- Z. L. Wang and X. R. Shi, “Synchronization of a four-dimensional energy resource system via linear control,” Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 1, pp. 463–474, 2011.
- X. C. Li, W. Xu, and R. H. Li, “Chaos synchronization of the energy resource system,” Chaos, Solitons & Fractals, vol. 40, no. 2, pp. 642–652, 2009.
- C. Y. Wen, J. Zhou, Z. T. Liu, and H. Y. Su, “Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance,” IEEE Transactions on Automatic Control, vol. 56, no. 7, pp. 1672–1678, 2011.
- Y. M. Li, T. S. Li, and X. J. Jing, “Indirect adaptive fuzzy control for input and output constrained nonlinear systems using a barrier Lyapunov function,” International Journal of Adaptive Control and Signal Processing, vol. 28, no. 2, pp. 184–199, 2014.
- D. Meng, “Robust adaptive synchronization of the energy resource system with constraint,” Mathematical Problems in Engineering, vol. 2013, Article ID 494218, 7 pages, 2013.
- Z. G. Yan and W. H. Zhang, “Finite-time stability and stabilization of Itô-type stochastic singular systems,” Abstract and Applied Analysis, vol. 2014, Article ID 263045, 9 pages, 2014.
- J. Cui, L. T. Yan, and X. C. Sun, “Existence and stability for stochastic partial differential equations with infinite delay,” Abstract and Applied Analysis, vol. 2014, Article ID 235937, 8 pages, 2014.
- L. Liu, Z. D. Yu, Q. Zhou, and H. R. Karimi, “State-feedback stabilization for a class of stochastic feedforward nonlinear time-delay systems,” Abstract and Applied Analysis, vol. 2013, Article ID 151374, 8 pages, 2013.
- L. G. Wu and D. W. C. Ho, “Sliding mode control of singular stochastic hybrid systems,” Automatica, vol. 46, no. 4, pp. 779–783, 2010.
- X. J. Su, L. G. Wu, P. Shi, and Y. D. Song, “ model reduction of Takagi-Sugeno fuzzy stochastic systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 42, no. 6, pp. 1574–1585, 2012.
- S.-C. Tong, Y. Li, and Y. M. Li, “Observer-based adaptive fuzzy backstepping control for a class of stochastic nonlinear strict-feedback systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 41, no. 6, pp. 1693–1704, 2011.
- S.-C. Tong, T. Wang, Y. M. Li, and B. Chen, “A combined backstepping and stochastic small-gain approach to robust adaptive fuzzy output feedback control,” IEEE Transactions on Fuzzy Systems, vol. 21, pp. 314–327, 2013.
- H. Li, C. Wang, P. Shi, and H. J. Gao, “New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays,” Neurocomputing, vol. 73, no. 16–18, pp. 3291–3299, 2010.
- M. Liu, D. W. C. Ho, and Y. G. Niu, “Stabilization of Markovian jump linear system over networks with random communication delay,” Automatica, vol. 45, no. 2, pp. 416–421, 2009.
- Q. Zhou, P. Shi, H. H. Liu, and S. Y. Xu, “Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 42, no. 6, pp. 1608–1619, 2012.
- L. G. Wu and W. X. Zheng, “- control of nonlinear fuzzy Itô stochastic delay systems via dynamic output feedback,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 5, pp. 1308–1315, 2009.
- Q. Zhou, P. Shi, S. Y. Xu, and H. Y. Li, “Observer-based adaptive neural network control for nonlinear stochastic systems with time-delay,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 1, pp. 71–80, 2013.
- F. Lewis, S. Jaganathan, and A. Yesildirek, Neural Network Control of Robust Manipulator and Nonlinear Systems, Taylor & Francis, New York, NY, USA, 1998.
- H. Y. Li, X. J. Jing, and H. R. Karimi, “Output-feedback based control for active suspension systems with control delay,” IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 436–446, 2014.
- S. C. Tong and Y. M. Li, “Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 168–180, 2012.
- H. Y. Li, X. J. Jing, H. K. Lam, and P. Shi, “Fuzzy sampled-data control for uncertain vehicle suspension systems,” IEEE Transactions on Cybernetics, 2013.
- Y. M. Li, S. C. Tong, Y. J. Liu, and T. S. Li, “Adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on small-gain approach,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 1, pp. 164–176, 2014.