Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRegion of Attraction Estimation Using Invariant Sets and Rational Lyapunov Functions
101
0
0.0
(
0
)
Added by
James Anderson
Publication date
2016
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
This work addresses the problem of estimating the region of attraction (RA) of equilibrium points of nonlinear dynamical systems. The estimates we provide are given by positively invariant sets which are not necessarily defined by level sets of a Lyapunov function. Moreover, we present conditions for the existence of Lyapunov functions linked to the positively invariant set formulation we propose. Connections to fundamental results on estimates of the RA are presented and support the search of Lyapunov functions of a rational nature. We then restrict our attention to systems governed by polynomial vector fields and provide an algorithm that is guaranteed to enlarge the estimate of the RA at each iteration.
rate research
Read More
This paper considers the problem of designing accelerated gradient-based algorithms for optimization and saddle-point problems. The class of objective functions is defined by a generalized sector condition. This class of functions contains strongly convex functions with Lipschitz gradients but also non-convex functions, which allows not only to address optimization problems but also saddle-point problems. The proposed design procedure relies on a suitable class of Lyapunov functions and on convex semi-definite programming. The proposed synthesis allows the design of algorithms that reach the performance of state-of-the-art accelerated gradient methods and beyond.
In this paper we revisit the problem of computing robust controlled invariant sets for discrete-time linear systems. The key idea is that by considering controllers that exhibit eventually periodic behavior, we obtain a closed-form expression for an implicit representation of a robust controlled invariant set in the space of states and finite input sequences. Due to the derived closed-form expression, our method is suitable for high dimensional systems. Optionally, one obtains an explicit robust controlled invariant set by projecting the implicit representation to the original state space. The proposed method is complete in the absence of disturbances, with a weak completeness result established when disturbances are present. Moreover, we show that a specific controller choice yields a hierarchy of robust controlled invariant sets. To validate the proposed method, we present thorough case studies illustrating that in safety-critical scenarios the implicit representation suffices in place of the explicit invariant set.
Stochastic uncertainties in complex dynamical systems lead to variability of system states, which can in turn degrade the closed-loop performance. This paper presents a stochastic model predictive control approach for a class of nonlinear systems with unbounded stochastic uncertainties. The control approach aims to shape probability density function of the stochastic states, while satisfying input and joint state chance constraints. Closed-loop stability is ensured by designing a stability constraint in terms of a stochastic control Lyapunov function, which explicitly characterizes stability in a probabilistic sense. The Fokker-Planck equation is used for describing the dynamic evolution of the states probability density functions. Complete characterization of probability density functions using the Fokker-Planck equation allows for shaping the states density functions as well as direct computation of joint state chance constraints. The closed-loop performance of the stochastic control approach is demonstrated using a continuous stirred-tank reactor.
In this paper, we investigate geometric properties of monotone systems by studying their isostables and basins of attraction. Isostables are boundaries of specific forward-invariant sets defined by the so-called Koopman operator, which provides a linear infinite-dimensional description of a nonlinear system. First, we study the spectral properties of the Koopman operator and the associated semigroup in the context of monotone systems. Our results generalize the celebrated Perron-Frobenius theorem to the nonlinear case and allow us to derive geometric properties of isostables and basins of attraction. Additionally, we show that under certain conditions we can characterize the bounds on the basins of attraction under parametric uncertainty in the vector field. We discuss computational approaches to estimate isostables and basins of attraction and illustrate the results on two and four state monotone systems.
The Lyapunov rank of a proper cone $K$ in a finite dimensional real Hilbert space is defined as the dimension of the space of all Lyapunov-like transformations on $K$, or equivalently, the dimension of the Lie algebra of the automorphism group of $K$. This (rank) measures the number of linearly independent bilinear relations needed to express a complementarity system on $K$ (that arises, for example, from a linear program or a complementarity problem on the cone). Motivated by the problem of describing spectral/proper cones where the complementarity system can be expressed as a square system (that is, where the Lyapunov rank is greater than equal to the dimension of the ambient space), we consider proper polyhedral cones in $mathbb{R}^n$ that are permutation invariant. For such cones we show that the Lyapunov rank is either 1 (in which case, the cone is irreducible) or n (in which case, the cone is isomorphic to the nonnegative orthart in $mathbb{R}^n$). In the latter case, we show that the corresponding spectral cone is isomorphic to a symmetric cone.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
