Stochastic Systems: Modeling, Analysis, Synthesis, Control, and their Applications to EngineeringView this Special Issue
Adaptive State-Feedback Stabilization for High-Order Stochastic Nonlinear Systems Driven by Noise of Unknown Covariance
This paper further considers more general high-order stochastic nonlinear system driven by noise of unknown covariance and its adaptive state-feedback stabilization problem. A smooth state-feedback controller is designed to guarantee that the origin of the closed-loop system is globally stable in probability.
In this paper, we consider the following high-order stochastic nonlinear system: where , , are the state and control input, respectively. , . is odd integer. is an -dimensional standard Wiener process defined in a complete probability space with being a sample space, being a -field, being a filtration, and being the probability measure. is an unknown bounded nonnegative definite Borel measurable matrix function and denotes the infinitesimal covariance function of the driving noise . and , , are assumed to be smooth with and .
When , system (1.1) reduced to the well-known normal form whose study on feedback control problem has achieved great development in recent years. According to the difference of selected Lyapunov functions, the existing literature on controller design can be mainly divided into two types. One type is based on quadratic Lyapunov functions which are multiplied by different weighting functions, see, for example, [1–5] and the references therein. Another essential improvement belongs to Krstić and Deng. By introducing the quartic Lyapunov function, [6, 7] presented asymptotical stabilization control in the large under the assumption that the nonlinearities equal to zero at the equilibrium point of the open-loop system. Subsequently, for several classes of stochastic nonlinear systems with unmodeled dynamics and uncertain nonlinear functions, by combining Krstić and Deng’s method with stochastic small-gain theorem , and with dynamic signal and changing supply function [9, 10], different adaptive output-feedback control schemes are studied.
When , some intrinsic features of (1.1), such as that its Jacobian linearization is neither controllable nor feedback linearizable, lead to the existing design tools hardly applicable to this kind of systems. Motivated by the fruitful deterministic results in [11, 12] and the related papers and based on stochastic stability theory in [13–15], and so forth,  firstly considered, this class of systems with stochastic inverse dynamics. Subsequently, [17–21] considered respectively, the state-feedback stabilization problem for more general systems with different structures. [22, 23] solved the output-feedback stabilization, and  addressed the inverse optimal stabilization.
All the papers mentioned above, however, only consider the case of . In this paper, we will further consider more general high-order stochastic nonlinear system driven by noise of unknown covariance and its adaptive state-feedback stabilization problem. A smooth state-feedback controller is designed to guarantee that the origin of the closed-loop system is globally stable in probability. A simulation example verifies the effectiveness of the control scheme.
The paper is organized as follows. Section 2 provides some preliminary results. Section 3 gives the state-feedback controller design and stability analysis, following a simulation example in Section 4. In Section 5, we conclude the paper.
2. Preliminary Results
The following notations definitions and lemmas are to be used throughout the paper.
stands for the set of all nonnegative real numbers, is the -dimensional Euclidean space, is the space of real -matrixes. denotes the family of all the functions with continuous second partial derivatives. is the usual Euclidean norm of a vector . , where is its trace when is a square matrix and denotes the transpose of . denotes the set of all functions: , which are continuous, strictly increasing and vanishing at zero; is the set of all functions which are of class and unbounded; denotes the set of all functions : , which are of class for each fixed and decrease to zero as for each fixed . To simplify the procedure, we sometimes denote by for any variable .
Consider the nonlinear stochastic system where is the state, is an -dimensional independent Wiener process with incremental covariance , that is, , where is a bounded function taking values in the set of nonnegative definite matrices, and are locally Lipschitz functions.
Lemma 2.3 (see ). Consider the stochastic system (2.1). If there exist a function , class function and , constants and , and a nonnegative function such that for all , then,(a)there exists an almost surely unique solution on for each ,(b)when , , , and is continuous, then the equilibrium is globally stable in probability and the solution satisfies .
Lemma 2.4 (see ). Let be real variables, for any positive integers , positive real number and nonnegative continuous function , then when , is a positive constant, then the above inequality is
Lemma 2.5 (see ). Let and , , be real variables and let and be smooth mappings. Then for any positive integers , and real number , there exist two nonnegative smooth functions and such that the following inequalities hold:(i), (ii).
Lemma 2.6 (see ). Let , , be positive real variables, then
3. Controller Design and Stability Analysis
The objective of this paper is to design a smooth state-feedback controller for system (1.1), such that the solution of the closed-loop system is GAS in probability. To achieve the control objective, we need the following assumption.
Assumption 3.1. There are nonnegative smooth functions , , , such that
3.1. Controller Design
Now, we give the controller design procedure by using the backstepping method.
First, we introduce the following coordinate change: where , , are smooth virtual controllers which will be designed later, is the estimation of , and Then, by Itô’s differentiation rule, one has where
Step 1. Define the first Lyapunov function where is a positive constant, is the parameter estimation error. By (3.2)–(3.4) and Assumption 3.1, there exist nonnegative smooth functions and such that Choosing the first smooth virtual controller and the tuning function one has where is a design parameter.
Step i (). For notational coherence, denote . Assuming that at step , one has where . In the sequel, we will prove that (3.11) still holds for the th Lyapunov function . By (3.4) and (3.11), one has To proceed further, an estimate for each term in the right-hand side of (3.12) is needed. Using Itô’s differentiation rule, Lemmas 2.4–2.6, (3.2), and (3.3), it follows that where , , , , , , , are positive constants and , , , , , , , , , , , , are nonnegative smooth functions. Substituting (3.13) into (3.12), one has Choosing the th smooth virtual controller and tuning function and substituting (3.15) and (3.16) into (3.14), it follows that where , .
Hence at step , the smooth adaptive state-feedback controller such that the th Lyapunov function satisfies where , , are nonnegative smooth functions, , , are constants, and
3.2. Stability Analysis
Theorem 3.2. If Assumption 3.1 holds for the high-order stochastic nonlinear system (1.1), under the state-feedback controller (3.18), then(i)the closed-loop system consisting of (1.1), (3.2), (3.8), (3.9), (3.15), (3.16), and (3.18) has an almost surely unique solution on for each ,(ii)the origin of the closed-loop system is globally stable in probability,(iii) and exists and is finite.
Proof. It is easy to verify that is on and . For , choose the design parameter , , then by (3.21), , . Since is continuous, positive, and radially bounded, by (3.20), (3.21), and Lemma 4.3 in , there exist two class functions and such that . Hence, the condition of Lemma 2.3 holds.
By Lemma 2.3, it follows that conclusion (i), (ii) hold, and . In view of and , one has . By (3.20) and the definition of in (3.19), it holds that converges a.s. to a finite limit as , therefore converges a.s. to a finite limit as .
4. A Simulation Example
We now give the design of state-feedback controller for system (4.1).
Step 1. Define , . A smooth virtual controller and the tuning function yield , where
Step 2. Defining , , by (3.12), one has
where , . By Lemma 2.4, the definition of , and (4.2), one can obtain
by (4.3), Lemmas 2.4, 2.6, and the definitions of and , one has
where , , , , , , , , , , , , , , , , , , , , , , are positive constants.
Choosing the smooth adaptive controller and substituting (4.6)–(4.8) into (4.5), one has where .
In simulation, we choose , the parameters , , , , , , , , , , , , , , the initial values , , , the sampling period . Figure 1 verifies the effectiveness of the control scheme.
In this paper, we further consider more general high-order stochastic nonlinear system driven by noise of unknown covariance and its adaptive state-feedback stabilization problem.
This work was supported by the National Natural Science Foundation of China (nos. 10971256 and 61104222), the Specialized Research Fund for the Doctoral Program of Higher Education of China (no. 20103705110002), and Natural Science Foundation of Jiangsu Province (BK2011205), Natural Science Foundation of Xuzhou Normal University.
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