Complexity

Volume 2017, Article ID 3517280, 7 pages

https://doi.org/10.1155/2017/3517280

## Global External Stochastic Stabilization of Linear Systems with Input Saturation: An Alternative Approach

^{1}School of Automation, Southeast University, Nanjing, China^{2}Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China

Correspondence should be addressed to Qingling Wang; moc.liamg@lqwusc

Received 19 April 2017; Accepted 11 July 2017; Published 9 August 2017

Academic Editor: Sigurdur F. Hafstein

Copyright © 2017 Qingling Wang. 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

This paper presents results concerning the global external stochastic stabilization for linear systems with input saturation and stochastic external disturbances under random Gaussian distributed initial conditions. The objective is to construct a class of control laws that achieve global asymptotic stability in the absence of disturbances, while guaranteeing a bounded variance of the state for all the time in the presence of disturbances. By using an alternative approach, a new class of scheduled control laws are proposed, and the global external stochastic stabilization problem can be solved only through some routine manipulations. Furthermore, the reported approach allows a larger range of the design parameter. Finally, two numerical examples are provided to validate the theoretical results.

#### 1. Introduction

In decades, the stabilization of linear systems with input saturation has been widely studied by many researchers; see, for instance, [1–4] and the references therein, and in general, internal stabilization and external stabilization are two kinds of main research focus on this subject [5]. It is now well known [6] that global internal stabilization is possible if and only if the linear system is asymptotically null controllable with bounded controls (ANCBC); that is, the linear system is stabilizable and all its open-loop poles are located in the closed left-half plane. Generally speaking, a class of nonlinear control laws should be constructed to achieve global internal stabilization; see, for instance, [7, 8]. By designing a class of low gain control laws, the semiglobal internal stabilization framework was introduced in [9, 10] with linear control laws.

On the other hand, external stabilization requires that internal stabilization is guaranteed when considering input saturation, which means external and internal stabilization have to be achieved simultaneously. By introducing the framework of external stability, it was proved in [11] that the general ANCBC systems with input saturation can achieve external stability with a linear control law when the external disturbance is input-additive and the global/semiglobal internal stabilization is guaranteed with this linear law in the absence of the external disturbance. For the non-input-additive disturbance, it is known [12] that in general external stability via a linear control law is almost never possible. Notable results from [13] reveal that, for double-integrator systems with input saturation and the non-input-additive disturbance, any linear control laws achieve external stability for , but no linear control laws can achieve external stability for . Considering the input-to-state stability (ISS) framework, it is pointed out [14] that the external and internal stabilization of double-integrator systems can not be achieved via a linear control law. Moreover, it is impossible to get a good stable response if we consider such systems with the non-input-additive disturbance. Based on these observations, the external stochastic stabilization problem should be considered, where the considered ANCBC system is subject to input saturation, stochastic external disturbances, and random Gaussian distributed initial conditions; see, for instance, [5] for neutrally stable systems, [15] for a chain of integrators, and [16] for general ANCBC systems. The objective is to construct a class of nonlinear control laws that achieve global asymptotic stability in the absence of disturbances, while guaranteeing a bounded variance of the state in the presence of disturbances.

A common feature in the aforementioned works of [15, 16] lies in a conjecture, and some additional restrictions have been imposed on certain design parameter. In this paper, we revisit the simultaneous external and internal stabilization of general ANCBC systems with input saturation and stochastic external disturbances. The main challenge of this study is how to solve the global external stochastic stabilization problem only through some routine manipulations. Moreover, it is worth pointing out that the main contributions are stated as follows.(1)An alternative approach is first introduced, and the global external stochastic stabilization can be solved through some routine manipulations, which means that the conjecture/lemma in [15, 16] is no longer needed.(2)Compared with existing results [15, 16], there is no restriction imposed on the design parameter, which allows a larger range.

*Notations*. The notations and denote the transpose and trace of matrix , respectively. Matrices, if their dimensions are not explicitly stated, are assumed to be compatible for algebraic operations. The notation (≥0) means that is a real symmetric positive (semipositive) definite matrix. and represent, respectively, the identity matrix and zero matrix.

#### 2. Problem Formulation

We consider the following stochastic differential equation:where the state , the control input , and the disturbance are vector-valued signals of dimensions , , and , respectively. Here is a Wiener process (a Brownian motion) with mean and rate . The initial condition of (1) is a Gaussian random vector which is independent of , and is a standard saturation function given as We firstly assume that the pair is stabilizable and all the eigenvalues of are in the closed left-half plane. As is well known, all eigenvalues of that have negative real parts will not affect the stabilizability property of the system. Without loss of generality, we will give the following assumption.

*Assumption 1. *The pair is controllable and all the eigenvalues of are on the imaginary axis.

In what follows, the global external stochastic stabilization problem can be defined as follows.

*Definition 2. *Consider system (1); the global external stochastic stabilization problem is to find a control law such that, for all possible values for the rate of the stochastic process , the following properties hold.(i)In the absence of the disturbance , the equilibrium point of system (1) with is globally asymptotically stable.(ii)In the presence of the disturbance , the variance of the state of the controlled system (1) with is bounded over .To solve the global external stochastic stabilization problem, a class of possibly nonlinear control laws are proposed in [15] aswhere the parameter is scheduled according toprovided is sufficiently small, and the state control gain is given as where is the positive definite solution to the following parameter dependent algebraic Riccati equation (ARE): where .

To avoid the trivial results, the case that is not considered in this paper. Meanwhile, it has been shown in [15], using a class of possibly nonlinear control laws (3), that the global external stochastic stabilization problem is solved provided the following conjecture holds.

Conjecture 3. *There exists a scalar such thatfor all .*

Although it is proved in [16] that the above conjecture always holds, it is noted that the parameter is restricted to the interval . As can be seen in the following section, the above conjecture/lemma is no longer needed, a new class of scheduled control laws will be proposed, and we can solve the global external stochastic stabilization only through some routine manipulations. Furthermore, there is no restriction imposed on the positive design parameter , which allows a larger range.

#### 3. Main Results

To find a solution to the problem of global external stochastic stabilization, the following parameter dependent ARE should be revisited:where is a scalar. This parameter dependent ARE was first introduced in [17] for a linear system, and the properties can be summarized as follows.

Lemma 4 (see [17]). *Let Assumption 1 hold. Then, for an arbitrary , the ARE (8) has a unique positive solution , where satisfies the parametric Lyapunov equation:Furthermore, is a polynomial function in for a scalar input. In general, is a rational function in .*

*Remark 5. *According to [18], one property of (8) can be found as

In what follows, we propose a new class of scheduled control laws to solve the global external stochastic stabilization problem, which can be carried out in three steps.

*Step 1. *Letwhere is a scalar.

*Step 2. *Solve the following ARE:where the existence of is guaranteed by Lemma 4.

*Step 3. *Construct a control law aswhere is the state control gain.

*Remark 6. *It is observed from (11) that if needed, the scalar can be given by any positive values, which means there is no restriction imposed on the positive parameter . Compared with the scheduled parameter in (4), the positive parameter in (11) allows a larger range. Furthermore, it is known from [17] that represents the convergence rate of the closed-loop system. In fact, a larger parameter may indicate a slower convergence speed.

The following lemmas play crucial roles.

Lemma 7. *For any positive scalar and all , one haswhere is defined in (12).*

*Proof. *Definewhere is the state of system (1). Note from (11) that , and we haveAccording to the It stochastic differential rule, we obtainNote thatand substituting (17), we find thatAccording to (10), we can continue (19) asIt follows from (15) that This implies which completes the proof.

*Remark 8. *Compared with (7) in Conjecture 3, the above relationship between and in Lemma 7 is analytically discovered through some routine manipulations. Meanwhile, if needed, the scalar can be given by any positive values, which indicates a larger range.

Lemma 9. *Let Assumption 1 hold. Then one has*

*Proof. *In view of (11), it is clear that which is less than or equal to .

Lemma 10 (see [19]). *Let be random variables and let be a filtration to which is adapted and suppose that there exist constants , and , and such that is bounded for and for all Then for any , there is such that for all .*

The following is the main result on the global external stochastic stabilization problem.

Theorem 11. *Let Assumption 1 hold. Consider the system described by stochastic differential equation (1); the control law (13) can solve the global external stochastic stabilization problem as defined in Definition 2.*

*Proof. *By using (1) and (13), we obtainRecalling from Lemma 9 that , we can continue (27) as follows:Consider a Lyapunov function as According to Lemma 4, it is easily verified that is a global Lyapunov function for system (28) in the absence of the disturbance , which establishes global asymptotic stability.

It remains to show that the variance of the state of system (28) is bounded in the presence of the disturbance . According to Lemma 10, we will use for and is the natural filtration generated by the stochastic process .

Firstly, we will prove (26) for arbitrary integers . According to the It stochastic differential rule, we obtainNote from Lemma 7 that It follows thatDefine which satisfies for some suitable constant , where we have used . Moreover, define which satisfies for some suitable constant . Thus we have It follows from [20] that all higher order moments of are bounded, and given that is bounded, we trivially find that is bounded for any and for any which implies (26).

Next, we will prove (25). Since , there exists such that . Consider such that implies that According to (32), we have Assuming with , and using Doob’s martingale inequality [21], we have That is, for any , we can choose such that However, for , we have where is an upper bound for and . Since we have given , we obtain which is less than for provided is small enough. This implies that (25) is satisfied. Therefore, we conclude from Lemma 10 that is bounded for all positive integers .

In what follows, we will prove that the variance of is bounded under the condition that is bounded for all positive integers . Since is a rational function in , there exist and an integer such that which implies This yields It follows from that which implies the expectation of the term on the right is bounded. Therefore, we have , which completes the proof.

*Remark 12. *From Theorem 11, we can know that the global external stochastic stabilization problem is solved for the case of Assumption 1. Moreover, it is easy to prove that the results can be extended to the case that the pair is stabilizable and all the eigenvalues of are in the closed left-half plane.

#### 4. Simulation Examples

In this section, we provide two numerical examples to illustrate the effectiveness of the proposed control laws.

*Example 1. *Consider a one-link manipulator with an elastic joint actuated by a DC motor [22, 23] with stochastic disturbances. Then, the dynamics are given as where Since the eigenvalues of are , it is easily verified that the pair is stabilizable and all the eigenvalues of are in the closed left-half plane. We choose , which means . Following the design procedure from (11) to (13) in this paper, the simulation results are given in Figure 1, where Figure 1(a) shows the state trajectories and Figure 1(b) shows the evolution of scheduled parameter . It is seen that the results yield a bounded variance of the state, and the state is bounded by . Moreover, it seems that the state responses converge to constant values. However, from the data sheet of the simulation example, the state response of zero eigenvalue converges to zero with very small stochastic values (see the green line), and state responses of other eigenvalues converge to constant values. This may arise from the reason that three eigenvalues are on the left-half plane and only one zero eigenvalue is on the imaginary axis.