Distributed Optimal Control for a Class of Switched Nonlinear Systems with the State Time Delay

Duan, Yuxing; Su, Baili

doi:https://doi.org/10.1155/2020/3672439

Mathematical Problems in Engineering

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Abstract Introduction Preliminaries Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2020 | Article ID 3672439 | https://doi.org/10.1155/2020/3672439

Distributed Optimal Control for a Class of Switched Nonlinear Systems with the State Time Delay

Yuxing Duan¹and Baili Su¹

Academic Editor: Jürgen Pannek

Received13 Jan 2020

Revised05 Apr 2020

Accepted15 Apr 2020

Published11 May 2020

Abstract

This paper is focused on a kind of distributed optimal control design for a class of switched nonlinear systems with the state time delay which have a prescribed switching sequence. Firstly, we design a bounded controller to make the system stable for each mode of the nominal system. Then, a distributed optimal controller which can satisfy input constraint is designed based on the bounded stabilization controller. A sufficient condition to guarantee ultimate boundedness of the system is given based on appropriate assumption. The significance of this paper is that distributed optimal control method is applied to switched nonlinear systems with the state time delay. Finally, a simulation example is given to verify the effectiveness of the proposed method.

1. Introduction

Switched time-delay systems are an important kind of hybrid systems, which have attracted extensive attention in recent years. There are some results for switched time-delay systems [1–4]. However, the input of switched time-delay systems is often constrained by objective conditions, and some switched systems need to operate according to a prescribed switching sequence in many practical control systems. So, there are still many challenging control problems need to be solved for this class of nonlinear systems.

Regression optimal control is an optimal control method that can deal with system constraints. It obtains the current control action by solving a finite time open loop optimal control problem at every sampling moment. It has well dynamic control effect and is helpful to improve the stability of the system. Therefore, its study has received considerable attention [5–8]. Moreover, the method of combining regression optimal control with the control Lyapunov function-based bounded control has made further development in the fields of single-objective control, multiobjective control, and so on. For example, the objective function in [9] adopts the nonquadratic cost function and studies a regression optimal control based on the Lyapunov economic model, which can directly solve economic problems. In [10], a fast feedback controller that stabilizes the fast dynamics and a regression optimal controller that stabilizes the slow dynamics and enforces desired performance objectives in the slow subsystem are designed for a kind of nonlinear singularly perturbed systems. He et al. [11] study an alternative utopia-tracking multiobjective economic regression optimal control scheme of constrained nonlinear systems with guaranteed asymptotic stability and convergence of average performance.

In most existing results, centralized optimal control methods are adopted. When this kind of control method is applied to the system, the computational complexity reduces the performance of the system with the increase of variables and the expansion of model size. Distributed optimal control can reduce computational burden and fault tolerance of the system by using communication and cooperation among multiple controllers. Therefore, more and more scholars study distributed optimal control. The method of combining distributed optimal control with the control Lyapunov function-based bounded control also has made further development. Heidarinejad et al. [12] study a distributed optimal control of the switched nonlinear system. It redesigns the local control system (LCS) and the new control system (NCS) by using an optimal control method based on Lyapunov function to coordinate their respective operations. The distributed structure adopted in [12] enables LCS to stabilize the closed-loop system, and NCS can communicate and cooperate with LCS, which can further improve the closed-loop performance. A distributed optimal control scheme with such distributed structure is considered for switched nonlinear time-delay systems with delayed measurements and communication disruption in [13]. Liu et al. [14] propose a distributed optimal control strategy for nonlinear systems with asynchronous and time-delay measurements. Heidarinejad et al. [15] study a distributed optimal control scheme of nonlinear systems subject to communication disruptions—communication channel noise and data losses—between distributed controllers. It is noted that the distributed optimal control method is seldom applied to nonlinear time-delay systems. Hence, it is necessary to study the problem of distributed optimal control for switched nonlinear systems with the state time delay.

In this paper, a distributed optimal control method is applied to switched nonlinear systems with the state time delay. The main ideas are as follows. A bounded controller to make system stable for each mode of the nominal system is designed. Then, a kind of distributed optimal control which can satisfy input constraint is designed based on the bounded stabilization controller. A sufficient condition to guarantee ultimate boundedness of the switched system is given. Finally, a simulation example is given to verify the effectiveness of the proposed distributed optimal control strategy.

2. Problem Statement and Preliminaries

Consider a class of switched nonlinear systems with the state time delay as follows:where is the state, is the input, is external disturbance, respectively, , , , , and are local Lipschitz functions satisfying , , , , and , and denotes time delay. The initial value is with being a continuous function and , denotes the Euclidean norm. is restricted on a nonempty set with being positive constant . with being a positive constant. is the right continuous switching symbol with which denotes the number of switched modes. In other words, for all , which means that a finite number of switches can only be switched in a finite time interval.

In this paper, and denote the time of the th switch in and out for the th mode respectively, i.e., . So, if , then system (1) can be described as

and denote the time sets of switching in and out of the th mode, respectively.

The objective of this paper is to propose a distributed optimal control scheme for a class of switched nonlinear systems with the state time delay. System (1) adopts the distributed structure (see Figure 1), where and are designed by controller 1 and controller 2, respectively.

The following assumption is satisfied at mode .

Assumption 1. For some , there holdswhere is a continuous positive definite nondecreasing function.

Remark 1. Assumption 1 illustrates that the decoupling between the delay state and the current state can be realized by the derivative of the Lyapunov function. This assumption is also used to realize the separation of time-delay state and nontime-delay state in [16].

Remark 2. The distributed optimal control can set up multiple part controllers to make the system stable and improve its performance. For simplicity, only two parts are considered in this paper. Distributed optimal controller designed in this paper is composed of controller 1 and controller 2. Controller 1 can stabilize the closed-loop system. Controller 2 can improve the performance of the closed-loop system by communicating with controller 1. If the distributed optimal controller is composed by multiple controllers, then controller 1 makes the closed-loop system stable, remaining controllers improve the performance of the closed-loop system through cooperation and communication with controller 1.

3. Main Results

3.1. Design of Bounded Controller

For each mode , we can design bounded controllers acting on the following proposition.

Proposition 1. If the nominal system of system (2)satisfies Assumption 1, we can choose bounded controllers and such thatwhereThen, system (4) is asymptotically stable under the control of and .

Proof. Based on controller (6), from [17,18], the following stable set is given:The maximum estimation of can be described bywhere is the largest number for and is invariant set of system (4).
Next, for all initial conditions in , we can prove that the controllersare bounded. From (6), we haveFor system (4), Razumikhin’s original idea is that if and are satisfied, then the system is stable. The derivative of function isBecause system (4) satisfies Assumption 1, we can obtainIf , then . We haveBecause , we have . System (4) is asymptotically stable under control of and .

3.2. Distributed Optimal Control

For each mode , a distributed optimal control scheme is designed.

Firstly, we design controller 2 based on the bounded controller and the received measurement at time . The input can be calculated by the following optimal problem:s.t.where is a family of piecewise constants function with a sampling time , is the predicted range, , , and are the right of positive definite matrix, is the trajectory of system (4) with , and is the input trajectory computed by (15)–(21).

The optimal solution is denoted by .

Once is calculated, it is sent to controller 1 and the corresponding control actuator. Then, we design controller 1 based on the measurement and . The input is calculated by the following optimal problem:s.t.

The optimal solution is expressed as .

Remark 3. We emphasize two points: (i) (18) and (26) ensure that system input satisfies the constraint. (ii) (21) and (28) guarantee the stability of mode .

3.3. Stability Analysis

In this section, the stability of distributed optimal control scheme is demonstrated.

Theorem 1. Considering that switched nonlinear system (1) satisfies Assumption 1, and under distributed optimal control of (15)–(21) and (23)–(28),(i)When there is no switch, let , , satisfy the following inequality: if initial state starts from the set , and , where , then is ultimately bounded in .(ii)When there is switch, the following constraints need to be added to distributed optimal control of (15)–(21) and (23)–(28):where denotes the value of Lyapunov functions of mode for th time and is the value of Lyapunov functions of mode for th time. If (29) is satisfied, then is ultimately bounded in .

Proof. The proof is divided into two parts.(i)When there is no switch, if , from (21) and (28), we have From (14), it can be deduced that The derivative of along the actual state trajectory in is It follows from (31) and (33) that is acontinuous differentiable and , , , , and are local Lipschitz. So, there are positive numbers , , , , and which make the following equations true for : Using (35)–(39), we can rewrite (34) as follows: For all , it follows from (32) that According to the smoothness of functions , , , and , there exists positive constants such that Because of the continuity of , for , there are By (40) and all initial states , we have For , if (29) is true, there exists such that Integrating this bound on , we have By recursively applying (46), we can obtain that if the initial state starts from , the state converges to . If the state converges to (or starts at ), the state remains in , determined by the definition of . So, is ultimately bounded in .(ii)When there is switch, there are two cases.In case of and , we consider that the system has an infinite number of switches. At this point, th mode is activated. If , then the constraints of (21) and (28) guarantee that , and the constraints of (30) ensure that . So, is always decrease. Even if th mode is not activated, there is th mode which is activated and guarantees that is decrease and . So, is ultimately bounded stable in .
In case of and , we consider that the system has an finite number of switches. Similarly, . When switching to the th mode, there is . From this point on, distributed optimal control strategy is not subject to any switching constraints. So, is ultimately bounded stable.
In conclusion, Theorem 1 is proved.
The manipulation inputs of the distributed optimal control scheme are defined:The executive strategy of optimal control in this paper is given as follows: Step 1: controller 2 receives the measurement at time , and then controller 2 calculates the optimal trajectory of . Step 2: the optimal trajectory of is transmitted to the actuator and controller 1. Step 3: once the optimal trajectory of is received by controller 1, the optimal trajectory of is calculated. Step 4: controller 1 sends the optimal trajectory of to the actuator. Step 5: go to the next moment.

4. Simulation Example

We consider the following switched system to verify the effectiveness of the proposed distributed optimal control scheme:where and ; and ; and ; and ; ; and and . Our goal is to adjust the initial state to the stable state for the system. The control constraints are and , , .

We choose the Lyapunov function , , and , Lyapunov function , , and to satisfy Assumption 1. We use Proposition 1 to construct the bounded controller and . We apply the distributed optimal control and the centralized optimal control, respectively. Both control methods use the same parameters. The prediction horizon is , the sampling interval is , and the weighted matrices are chosen as , , and (). Select the initial state as .

Set the system switching mode 2 from mode 1 at time (the system state enters the stable region of mode 2). The simulation results are shown in Figures 2–5:

The solid lines and the dotted lines represent the state trajectories under distributed optimal control and centralized optimal control, respectively. Simulation results indicate that optimal control via distributed optimal control method can ensure that the time-delay system state is ultimately bounded and stable. It also shows that both control designs give similar closed-loop performance and drive the state and the state close to the stable state in about 7.5 s and 8.5 s, respectively. To further illustrate that the two control designs have similar closed-loop performance, we illustrate this point from the perspective of the performance index. A series of simulation comparisons are made between the distributed optimal control and the centralized optimal control with the same parameters by using ten sets of different initial values. We calculated the total performance cost along the closed-loop system trajectories in 10s at different initial values. The comparison results are shown in Table 1.

It can be seen from Table 1 that the cost of distributed optimal control method is lower than that of the centralized optimal control method in 6 out of 10 simulations. This shows that the closed-loop performance of the distributed optimal control method is comparable to that of the centralized optimal control method. Among the 10 simulation results, we also compared the computation time of the distributed optimal control method and centralized optimal control method. The average time to calculate the centralized optimal control is 2.64 s. The average time to calculate controller 1 and controller 2 is 0.9 s and 1.05 s, respectively. Obviously, the computation time of the distributed optimal control method is less than that of centralized optimal control. This is because centralized optimization control must optimize two inputs simultaneously in an optimization problem, while distributed optimization control is to solve two optimization problems which only have one decision variable.

5. Conclusions

This paper solves the problem of distributed optimal control for switched nonlinear systems with the state time delay. For each mode, a bounded controller is designed to stabilize the system. Then, a distributed optimal control scheme which can satisfy the input constraint is designed based on the bounded stabilization controller. Finally, it is proved that the system state is ultimately bounded stable.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by the Natural Science Foundation of China (Grant nos. 61374004, 61773237, and 61473170) and Key Research and Development Programs of Shandong Province (Grant no. 2017GSF18116).

References

Y. Sun and J. Xu, “Finite-time boundedness and stabilization of networked control systems with time delay,” Mathematical Problems in Engineering, vol. 2012, Article ID 705828, 12 pages, 2012.
View at: Publisher Site | Google Scholar
L. Teng, Y. Wang, W. Cai, and H. Li, “Efficient robust fuzzy model predictive control of discrete nonlinear time-delay systems via Razumikhin approach,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 262–272, 2019.
View at: Publisher Site | Google Scholar
L. F. P. Silva, V. J. S. Leite, E. B. Castelan, and M. Klug, “Local stabilization of time-delay nonlinear discrete-time systems using Takagi-Sugeno models and convex optimization,” Mathematical Problems in Engineering, vol. 2014, Article ID 587510, 10 pages, 2014.
View at: Publisher Site | Google Scholar
Y. He, X.-M. Sun, J. Liu, and Y. Wu, “Stability analysis for homogeneous hybrid systems with delays,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 2018, Article ID 2872045, p. 8, 2018.
View at: Publisher Site | Google Scholar
T. Liu, C. Xia, X. Gu, and T. Shi, “An observer-based finite control set model predictive control for three-phase power converters,” Mathematical Problems in Engineering, vol. 2014, Article ID 351814, 9 pages, 2014.
View at: Publisher Site | Google Scholar
D. Q. Mayne, “Model predictive control: recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2867–2986, 2014.
View at: Publisher Site | Google Scholar
Z. Ma, X. Zhang, J. Huang, and B. Zhao, “Stability-constraining-dichotomy-solution-based model predictive control to improve the stability of power conversion system in the MEA,” IEEE Transactions on Industrial Electronics, vol. 66, no. 7, pp. 5696–5706, 2019.
View at: Publisher Site | Google Scholar
Y. Song, Z. D. Wang, D. R. Ding Derui et al., “Robust model predictive control for linear systems with polytopic uncertainties under weighted MEF-TOD protocol,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 7, no. 49, pp. 1470–1481, 2019.
View at: Publisher Site | Google Scholar
M. Heidarinejad, J. Liu, and P. D. Christofides, “Economic model predictive control of switched nonlinear systems,” Systems & Control Letters, vol. 62, no. 1, pp. 77–84, 2013.
View at: Publisher Site | Google Scholar
X. Chen, M. Heidarinejad, J. Liu, D. M. de la Peña, and P. D. Christofides, “Model predictive control of nonlinear singularly perturbed systems: application to a large-scale process network,” Journal of Process Control, vol. 21, no. 9, pp. 1296–1305, 2011.
View at: Publisher Site | Google Scholar
D. He, J. Sun, and W. Chen, “Multiobjective economic MPC of constrained non-linear systems,” IET Control Theory & Applications, vol. 10, no. 13, pp. 1487–1495, 2016.
View at: Publisher Site | Google Scholar
M. Heidarinejad, J. F. Liu, and P. D. Christofides, “Distributed model predictive control of switched nonlinear systems,” in Proceedings of the American Control Conference, pp. 3198–3203, Montreal, Canada, June 2012.
View at: Google Scholar
W. Li, W. Y. Mu, and B. T. Cui, “Distributed model predictive control of switched nonlinear systems,” Control and Decision, vol. 4, pp. 599–604, 2014.
View at: Google Scholar
J. Liu, D. Muñoz de la Peña, and P. D. Christofides, “Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements,” Automatica, vol. 46, no. 1, pp. 52–61, 2010.
View at: Publisher Site | Google Scholar
M. Heidarinejad, J. Liu, D. Muñoz de la Peña, J. F. Davis, and P. D. Christofides, “Handling communication disruptions in distributed model predictive control,” Journal of Process Control, vol. 21, no. 1, pp. 173–181, 2011.
View at: Publisher Site | Google Scholar
M. Frédéric and S. I. Niculescu, “Lyapunov stability analysis for nonlinear delay systems,” Systems and Control Letters, vol. 42, pp. 245–251, 2001.
View at: Google Scholar
B. Su, S. Li, and Q. Zhu, “The design of predictive control with characterized set of initial condition for constrained switched nonlinear system,” Science in China Series E: Technological Sciences, vol. 52, no. 2, pp. 456–466, 2009.
View at: Publisher Site | Google Scholar
B. Su and D. Chunyu, “Finite-time optimization stabilization for a class of constrained switched nonlinear systems,” Mathematical Problems in Engineering, vol. 2018, Article ID 6824803, 8 pages, 2018.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Yuxing Duan and Baili Su. 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.

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