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Control Design of Detectable Periodic Markov Jump Systems
An infinite horizon control problem is addressed for discrete-time periodic Markov jump systems with -dependent noise. Above all, by use of the spectral criterion of detectability, an extended Lyapunov stability theorem is developed for the concerned dynamics. Further, based on a game theoretic approach, a state-feedback control design is proposed. It is shown that under the condition of detectability feedback gain can be constructed through the solution of a group of coupled periodic difference equations.
control has been one of the most active areas of modern control theory since the 1970s. Owing to the introduction of state-space approach , many researchers have been inspired to extend the deterministic control theory to various stochastic systems; see [2–5]. In the development of stochastic theory,  can be regarded as a pioneering work, which firstly established a stochastic version of bounded real lemma for linear Itô-type differential systems. Besides, initialed from , considerable progress has been made in the study of stochastic control. By combing index with an quadratic cost performance, the resulting multiobjective control strategy is more attractive than the sole control in engineering applications.
The main objective of this paper is to settle an infinite horizon control problem for periodic Markov jump systems with multiplicative noises. By now, Markov jump systems have been extensively investigated [7–9]. For example, stochastic and robust stability have been elaborately discussed in [10, 11] for networked dynamics with Markovian jump. As concerns theory, an estimation problem was tackled in  for a class of discrete homogeneous Markov jump systems. On the other hand, an infinite horizon control problem was handled in  for nonlinear Itô systems with homogeneous Markov process. However, few results have been reported for control of periodic Markov jump systems. To some extent, this study will generalize the work of  to the case of periodically time-varying coefficients and transition probabilities, as in [15–17].
The remainder of this paper is organized as follows. Section 2 gives basic preliminaries and problem formulations. In Section 3, the intrinsic relationship between asymptotic mean square stability and detectability is addressed. As a result, a Barbashin-Krasovskii-type theorem is established for periodic Markov jump systems with state-dependent noises. Section 4 contains an internally stabilizing control design, which can not only fulfill the prescribed disturbance attenuation level, but also minimize the output energy. To verify the effectiveness of the proposed approach, a numerical example is supplied in Section 5. Finally, Section 6 concludes this paper with a concluding remark.
Notations. () is -dimensional real (complex) space with the usual Euclidean norm ; is the space of all real matrices with the operator norm ; is the set of all symmetric matrices whose entries may be complex; : is a positive (semi)definite matrix; is the transpose of a matrix (vector) ; is the identity matrix; and ; ; is the operation of Kronecker product; is the kernel of a matrix; is a (block) diagonal matrix.
On a complete probability space , we consider the following discrete-time periodic Markov jump system with -dependent noise: where , , , and denote the system state, control input, exogenous disturbance, and measurement output, respectively. Assume that is a sequence of independent random vectors such that and ( is a Kronecker function). The Markov chain takes values in with a nondegenerate transition probability matrix and the initial distribution for all . As usual, we set that is independent of the stochastic process and its mode is measurable in real time. Moreover, the coefficients of (1) are -periodic (e.g., ) and the transition probability of satisfies , where . Let be -algebra generated by . In the case of , . Denote by the space of -valued, nonanticipative square summable stochastic processes which are -measurable for all and . It is clear that is a real Hilbert space with the norm induced by the usual inner product: .
Definition 1 (see ). The zero-state equilibrium of discrete-time periodic Markov jump systemsor is called asymptotically mean square stable (AMSS) if for all and . Here, is the state of (2) corresponding to the initial state and . Moreover, if there exists -periodic sequence such that the zero-state equilibrium of the closed-loop systemis AMSS for any , then is called stochastically stabilizable and is called a stabilizing feedback.
Definition 2 (see ). The periodic Markov jump system with measurement equation or is called (uniformly) detectable if for any , , and , there holds
In this paper, we will deal with the infinite horizon optimal control problem about (1). More specifically, for a prescribed disturbance attenuation level , we aim to find a linear, memoryless, periodic state-feedback controller such that (i)when , the closed-loop state of (1) corresponding to is AMSS;(ii)the -induced norm of satisfies , where is the perturbation operator defined by ; it is notable that is the output of (1) corresponding to and , while is arbitrary random disturbance;(iii)when the worst-case disturbance , if existing, is imposed on (1), minimizes the corresponding output energy .
3. Stability and Detectability
In this section, we will focus on the detectability of periodic Markov jump system (1). This structural property will play an essential role in the treatment of control problem. Firstly, we present several instrumental operators.
Let (resp., ) indicate the set of all sequences with (resp., ). Thus, is a Hilbert space with the inner product:
Given , let be a Lyapunov operator defined as , whereThen, associated with inner product (6), the adjoint operator of is given by :
In terms of , we can construct a causal evolution ; when , (i.e., the identity operator).
To proceed, let us introduce the following two linear operators (cf. [14, 21]):where and is the entry of . It is easy to verify that and are both invertible and satisfy where is a constant matrix of full column rank andIn (11), is called the induced matrix of and (if for , then ). Repeating the above steps, the induced matrix of is realized to be . Particularly, the induced matrix of is denoted by .
Next, we will give two useful lemmas, which have been shown in .
Lemma 3. is AMSS if and only if , where denotes the spectral set of an operator (or a matrix) and .
Lemma 4. is detectable if and only if for some , there does not exist any nonzero such that
We are prepared to establish the following Barbashin-Krasovskii stability criterion for (4).
Theorem 5. If is detectable, then is AMSS if and only if the PLEhas a unique -periodic solution .
Proof. By Theorem 2.5 , if is AMSS, then the PLE (13) admits a unique -periodic solution . Next, we will show the converse assertion. If (13) has -periodic solution but is not AMSS, by Lemma 3, there must exist with . Denote by the spectral radius of ; then . According to the Krein-Rutman theorem, there is a positive definite such that . Since is detectable, by Lemma 4, for some , there exists at least one such thatThus, for inner product (6), it can be computed from (13) thatDue to the periodicity of , (15) leads to the fact that which implies for and . That is, for , which contradicts (14). Hence, is AMSS.
Remark 6. In , a similar result has been proven under the condition of stochastic detectability. According to , (uniform) detectability is a weaker prerequisite than stochastic detectability. Therefore, Theorem 5 has improved the result of Theorem 4.1  within the concerned framework.
In this section, a game theoretic approach will be employed to deal with the infinite horizon control problem of (1). Under the assumption of detectability, a necessary and sufficient condition can be provided for the existence of controller.
Theorem 7. For system (1), if the following coupled periodic difference equations (CPDEs) admit -periodic quaternion solution , ; , on ,where and , are detectable, then the state-feedback control is given by .
Conversely, if is detectable and the control problem about (1) is solved by , then CPDEs (17)–(20) admit a unique -periodic quaternion solution , ; , on .
Proof. “”: (a) Let us first show that stabilizes system (1) internally . To this end, we rewrite (19) as follows:where Since is detectable, by Lemma 4, we can prove that is also detectable. From (22) and Theorem 5, it follows that is AMSS, which means and . Similarly, we can prove that is also AMSS. Hence, can stabilize system (1) internally.
(b) Consider the following: . Implementing into system (1), we getNoting that is AMSS, is a stabilizing solution of (17). By use of Theorem 1 , we deduce that system (24) satisfies .
(c) minimizes the performance . By (17) and (24), we can complete square as follows:which implies that is the worst-case disturbance associated with . Applying to system (1), we haveIt remains to show that fulfills the following optimal index:which is an LQ optimal control problem. Since (19) is equivalent to (22) and is detectable, by Theorem 5, is a stabilizing solution of (19). Making use of Proposition 6.3 , we arrive atwhere . This justifies the sufficiency statement.
“”: Assume that and solve the considered control problem. Thus, stabilizes system (1) internally and . By Theorem 1 , we conclude that (17) admits a stabilizing solution , which implies that is AMSS. Since system (24) is internally stable, by Corollary 3.9 , we deduce that for any . As shown in the sufficient part, by use of (17) and (24), we will come to (25), which indicates . Further, imposing on system (1) gives (26). Because is the optimal control, solves LQ control problem (27). Moreover, from detectability of , we have that is detectable. Recalling that is AMSS, by Theorem 5, (22) (i.e., (19)) has a stabilizing solution . Finally, by completing square in terms of (19) and (26), we obtain (28), which justifies that . This ends the proof.
Remark 8. If the coefficients of (1) reduce to be time-invariant and the Markov chain is homogeneous, then Theorem 7 is reduced to the conclusion of Theorem 3 . Hence, the current study can be regarded as a periodic extension of . At present, there still exists some difficulty in generalizing the above method to design controller for time-varying Markov jump systems as in . To this end, a time-varying version of PBH criterion has to be developed.
5. Numerical Example
Consider the following two-dimensional Markov jump system with the periodic coefficients listed as follows: Moreover, and . It is clear that . The transition probability matrix is determined by , , , and . For a prescribed disturbance attenuation level , by use of the Runge-Kutta algorithm, we can solve CPDEs (17)–(20) and get the feedback gains of : By Lemma 4, it can be verified that and are both detectable. Applying to the periodic Markov jump system, we get the closed-loop state trajectory and corresponding performance. Figure 1(a) has displayed 50 sampled state trajectories originating from , while Figure 1(b) demonstrates the cumulative energy of the system output.
In this paper, an infinite horizon control problem has been settled for discrete-time periodic Markov jump systems with multiplicative noise. Under the condition of (uniform) detectability, a game theoretic control is produced by solving a group of CPDEs. Note that there remain some open topics on this issue. For example, it is interesting as well as challenging to investigate the control problem with input or output saturation constraint , which no doubt deserves a further study.
Conflict of Interests
The authors declare 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. 61304074), the Research Award Fund for Outstanding Young Scientists of Shandong Province (nos. BS2013 DX009 and BS2013DX012), the Research Fund for the Taishan Scholar Project of Shandong Province, the SDUST Research Fund (no. 2014JQJH103), and the Shandong Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources.
- J. C. Doyle, K. Glover, P. P. Khargonekar, and B. A. Francis, “State-space solutions to standard and control problems,” IEEE Transactions on Automatic Control, vol. 34, no. 8, pp. 831–847, 1989.
- D. Hinrichsen and A. J. Pritchard, “Stochastic ,” SIAM Journal on Control and Optimization, vol. 36, no. 5, pp. 1504–1538, 1998.
- A. El Bouhtouri, D. Hinrichsen, and A. J. Pritchard, “-type control for discrete-time stochastic systems,” International Journal of Robust and Nonlinear Control, vol. 9, no. 13, pp. 923–948, 1999.
- E. Gershon and U. Shaked, “ output-feedback control of discrete-time systems with state-multiplicative noise,” Automatica, vol. 44, no. 2, pp. 574–579, 2008.
- Z. Wang, D. W. C. Ho, H. Dong, and H. Gao, “Robust finite horizon control for a class of stochastic nonlinear timevarying systems subject to sensor and actuator saturations,” IEEE Transactions on Automatic Control, vol. 55, no. 7, pp. 1716–1722, 2010.
- B.-S. Chen and W. Zhang, “Stochastic control with state-dependent noise,” IEEE Transactions on Automatic Control, vol. 49, no. 1, pp. 45–57, 2004.
- O. L. V. Costa, M. D. Fragoso, and M. G. Todorov, Continuous-Time Markov Jump Linear Systems, Springer, Berlin, Germany, 2012.
- Q. Zhu and J. Cao, “Stability analysis of markovian jump stochastic BAM neural networks with impulse control and mixed time delays,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 3, pp. 467–479, 2012.
- Q. Zhu, “pth moment exponential stability of impulsive stochastic functional differential equations with Markovian switching,” Journal of the Franklin Institute: Engineering and Applied Mathematics, vol. 351, no. 7, pp. 3965–3986, 2014.
- Q. Zhu, J. Cao, T. Hayat, and F. Alsaadi, “Robust stability of Markovian jump stochastic neural networks with time delays in the leakage terms,” Neural Processing Letters, vol. 41, no. 1, pp. 1–27, 2013.
- Q. Zhu, R. Rakkiyappan, and A. Chandrasekar, “Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control,” Neurocomputing, vol. 136, pp. 136–151, 2014.
- L. Zhang, “ estimation for discrete-time piecewise homogeneous Markov jump linear systems,” Automatica, vol. 45, no. 11, pp. 2570–2576, 2009.
- Z. Lin, Y. Lin, and W. Zhang, “A unified design for state and output feedback control of nonlinear stochastic Markov jump systems with state and disturbance-dependent noise,” Automatica, vol. 45, no. 12, pp. 2955–2962, 2009.
- T. Hou, W. Zhang, and H. Ma, “Infinite horizon / optimal control for discrete-time Markov jump systems with (x,u,v)-dependent noise,” Journal of Global Optimization, vol. 57, no. 4, pp. 1245–1262, 2013.
- S. Aberkane and V. Dragan, “ filtering of periodic Markovian jump systems: application to filtering with communication constraints,” Automatica, vol. 48, no. 12, pp. 3151–3156, 2012.
- V. Dragan, T. Morozan, and A.-M. Stoica, “Output-based optimal controllers for a class of discrete-time stochastic linear systems with periodic coefficients,” International Journal of Robust and Nonlinear Control, vol. 25, no. 13, pp. 1897–1926, 2014.
- T. Morozan and V. Dragan, “An H2-type norm of a discrete-time linear sotchastic systems with periodic coefficients simultaneously affected by an infinite Markov chain and multiplicative white noise perterbations,” Stochastic Analysis and Applications, vol. 32, no. 5, pp. 776–801, 2014.
- V. Dragan, T. Morozan, and A.-M. Stoica, Mathematical Methods in Robust Control of Discrete-Time Linear Stochastic Systems, Springer, New York, NY, USA, 2010.
- T. Hou, H. Ma, and W. Zhang, “Spectral tests for observability and detectability of periodic Markov jump systems with nonhomogeneous Markov chain,” Automatica, In press.
- H. Ma, W. Zhang, and T. Hou, “Infinite horizon / control for discrete-time time-varying Markov jump systems with multiplicative noise,” Automatica, vol. 48, no. 7, pp. 1447–1454, 2012.
- W. Zhang and B.-S. Chen, “cal H-representation and applications to generalized Lyapunov equations and linear stochastic systems,” IEEE Transactions on Automatic Control, vol. 57, no. 12, pp. 3009–3022, 2012.
- V. Dragan and T. Morozan, “Stability and robust stabilization to linear stochastic systems described by differential equations with Markovian jumping and multiplicative white noise,” Stochastic Analysis and Applications, vol. 20, no. 1, pp. 33–92, 2002.
- G. Wei, Z. Wang, H. Shu, and J. Fang, “A delay-dependent approach to filtering for stochastic delayed jumping systems with sensor non-linearities,” International Journal of Control, vol. 80, no. 6, pp. 885–897, 2007.
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