Exponential stability analysis of nonlinear systems using LMIs

In this article, we provide a metaheuristic-based solution for stability analysis of nonlinear systems. We identify the optimal level set in the state space of these systems by combining two optimization phases. This set is in a definite negative region of the time derivative for a polynomial Lyapunov function (LF). Then, we consider a global optimization problem stated in two phases. The first phase is an external optimization to search for a definite positive LF, whose derivative is definite negative under linear matrix inequalities. The candidate LF coefficients are adjusted using a Jaya metaheuristic optimization algorithm. The second phase is an internal optimization to ensure an accurate estimate of the attraction region for each candidate LF that is optimized externally. The key idea of the algorithm is based mainly on a Jaya optimization, which provides an efficient way to characterize accurately the volume and shape of the maximal attraction domains. We conduct numerical experiments to validate the proposed approach. Two potential real-world applications are proposed.

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This paper considers a fuzzy Lyapunov method for stability analysis of nonlinear systems subjected to external forces. The nonlinear systems under external forces can be represented by Tagagi-Sugeno (T-S) fuzzy model. In order to design a nonlinear fuzzy controller to stabilize this nonlinear system, the parallel distributed compensation (PDC). scheme is used to construct a global fuzzy logic controller. We then propose the robustness design to ensure the modeling error is bounded and some stability conditions are derived based on the controlled systems. Based on the stability criterion, the nonlinear systems with external forces are guaranteed to be stable. This control problem can be reformulated into linear matrix inequalities (LMI) problem.

A symplectic numerical approach to the stability analysis of nonlinear parametric excited systems with multi-degree-of-freedom is developed and the stability of a nonlinear vibration system depends on the dynamic behavior of its perturbation. The nonlinear parameter-varying differential equations for the perturbation motion are expressed in the form of Hamiltonian equations with time-varying Hamiltonian, and are converted further into the classical Hamiltonian equations with extended time-invariant Hamiltonian by augmenting the state variables. The solution to the augmented Hamiltonian equations has the symplectic structure in terms of the symplectic transformation. Then the difference equations of the symplectic Runge-Kutta algorithm with a sufficient condition are constructed, which is proved to preserve the intrinsic symplectic structure of the original solution. In particular, the symplectic Gauss-Runge-Kutta algorithm with stage 2 and order 4 is proposed and applied to the stability analysis of a nonlinear system. Unstable regions based on the nonlinear periodic-parameter perturbation equation are obtained by using the symplectic Gauss-Runge-Kutta algorithm, analytical solution method and non-symplectic conventional Runge-Kutta algorithm to verify the higher accuracy of the proposed algorithm. Unstable regions based on the nonlinear perturbation are given to illustrate the improvement over those based on the linear perturbation. The developed symplectic approach to the stability analysis can preserve the symplectic structure of the original system and is applicable to nonlinear parametric excited systems with multi-degree-of-freedom.

This paper presents the stability analysis for polynomial fuzzy systems by utilizing the equality constraints of sum-of-squares (SOS) Program. Recently, T-S fuzzy systems have been extended to polynomial fuzzy systems, in which the consequent parts could be polynomial sub-systems. Moreover, stability and stabilization analyses for polynomial fuzzy systems have been done based on polynomial Lyapunov functions that contain quadratic Lyapunov functions as a special case. Since polynomials exist in the sub-systems and the positive definite matrices of the Lyapunov functions, the linear matrix inequality (LMI) tools are not valid for the polynomial analyses. Instead of LMI tools, SOSTOOLS of Matlab toolbox is applied to solve solutions for the polynomial analyses. The SOS program of SOSTOOLS can solve two kinds of constraints that are equality constraints and inequality constraints. The existing stability and stabilization analyses for polynomial fuzzy systems represent their conditions only in the inequality constraints. This paper analyzes the stability of the polynomial fuzzy systems by polynomial Lyapunov functions, and represents the new stability conditions not only in terms of the inequality constraints but also in terms of the equality constraints.

Almost sure exponential stability and stochastic stabilization of stochastic differential systems with impulsive effects

Stability and Stabilization of Nonlinear 2D Markovian Jump Systems with Applications

Delay-dependent exponential stabilization of nonlinear fuzzy impulsive systems with time-varying delay

This article concerns the problem of stability analysis of nonlinear systems under the sampled-data control (SDC) with actuator faults. The addressed nonlinear systems can be expressed by a number of linear arrangements, based on the Takagi–Sugeno (T–S) fuzzy technique. The time-dependent SDC with actuator faults of the addressed nonlinear systems delineates based on the input model of Markovian variability. The time-scheduled Lyapunov functional and the looped-functional are taken together in the production of a Lyapunov functional candidate. Based on a free matrix-based integral inequality and the Lyapunov functional, some sufficient linear matrix inequality conditions are delivered to guarantee the mean-square asymptotic stability under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance of the considered sampled-data T–S fuzzy systems with actuator faults. Finally, we demonstrate the feasibility and strength of our proposed method through some examples.

In this work we discuss some results on stability analysis for nonlinear systems with time-delays. A central theme of the work is to treat state variables with time-delays as disturbances and uncertainties. The advantage of such an approach is that by exploring the robust stability properties of a system with disturbances, one may obtain various properties for a system with time-delays.

In this note, we deal with the stability and stabilization problems for nonlinear quadratic discrete-time periodic systems. By using the quadratic Lyapunov function, and a so called periodic invariant set, delay-independent sufficient conditions for local stability and local stabilization for nonlinear quadratic discrete-time periodic systems are derived in terms of linear matrix inequalities (LMIs). Based on these sufficient conditions, iterative linear matrix inequality algorithms are proposed for maximizing the stability regions of the systems. Finally, two examples are given to illustrate the effectiveness of the methods presented in this paper.

— In this paper we introduce a new type of Lyapunov functions in a general framework particularly suitable for the analysis of systems with noise and uncertainty. These Lyapunov functions may depend on parameters possibly satisfying differential equations or inequalities. The main differences with respect to classical Lyapunov functions and classical tools for the design of composite Lyapunov functions are discussed through examples. A design tool for the design of composite filtered Lyapunov functions is given and examples show improvements over existing literature.

Although sum of squares programming has been used extensively over the past decade for the stability analysis of nonlinear systems, several fundamental questions remain unanswered. In this paper, we show that exponential stability of a polynomial vector field on a bounded set implies the existence of a Lyapunov function which is a sum of squares of polynomials. In particular, the main result states that if a system is exponentially stable on a bounded nonempty set, then there exists a sum of squares Lyapunov function which is exponentially decreasing on that bounded set. Furthermore, we derive a bound on the degree of this converse Lyapunov function as a function of the continuity and stability properties of the vector field. The proof is constructive and uses the Picard iteration. Our result implies that semidefinite programming can be used to answer the question of stability of a polynomial vector field with a bound on complexity.

This paper presents new stability conditions and LMI (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems. First, Takagi and Sugeno's fuzzy models and some stability results are recalled. To design fuzzy control systems, nonlinear systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel distributed compensation is employed to design fuzzy controllers from the Takagi-Sugeno fuzzy models. New stability conditions are obtained by relaxing the stability conditions derived in previous papers. The stability analysis of the feedback system is reduced to a problem of finding a common Lyapunov function for a set of linear matrix inequalities. Convex optimization techniques involving LMIs are utilized to find a common Lyapunov function. A procedure to design the fuzzy controller is constructed using the parallel distributed compensation and the relaxed stability conditions. Some stability issues are remarked. A simple example demonstrates the effects of the derived stability conditions.

Exponential stability of non-linear hyperbolic distributed complex-valued parameter systems: The linear fuzzy operator inequality approach