Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different timescales to model the nonlinear system. Passivity-based approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are g...

The paper deals with connections between optimality and passivity-like properties in discrete time. The problem is set in the framework of differential/difference representations of discrete-time dynamics. The Hamilton–Jacobi–Bellman equality associated with a given cost and the corresponding optimal control solution are characterized. On these bases the connection with u-average passivity is c...

In this paper, a new approach is developed for robust control of nonlinear chemical processes. The methodology proposed is based on passivity theory. Uncertainty and perturbations are taken into account in the stability analysis and controller synthesis such that the resulting closed-loop system can achieve better robustness and performance. The suggested passivity framework can deal with a lar...

This work was supported in part by the Office of Naval Research under Grant F-149. Abstract An experimental and theoretical comparison of passivity-based and input-output linearization controllers for induction motors is presented. The comparison is done taking into account saturation of the magnetic material in the main flux path of the induction motor. Experimental results show that the use ...

In this paper two problems arising in the digital passive scheme for telemanipulation presented in [5] are addressed. At first, we show how to preserve system passivity in presence of quantization error introduced by position sensor (i.e. encoders) by introducing energy dissipation. Then, we introduce a scheme for a redundant communication channel that will compensate for missed packets improvi...

Passivity-based control is widely used in electronic circuit systems because it can utilize their internal structures to facilitate the controller design. In this paper, we first propose a dissipative Hamiltonian realization of power systems and discuss the disadvantages of the traditional passivity-based excitation controller. Then, a novel excitation controller is put forward to reassign the ...

In this paper, dynamic multilayer neural networks are used for nonlinear system on-line identification. Passivity approach is applied to access several stability properties of the neuro identifier. The conditions for passivity, stability, asymptotic stability and inputto-state stability are established. We conclude that the commonly-used backpropagation algorithm with a modification term which ...

In this paper, the problem of passivity analysis for uncertain neural networks with time-varying delays is considered. By constructing an augmented Lyapunov–Krasovskii’s functional and some novel analysis techniques, improved delay-dependent criteria for checking the passivity of the neural networks are established. The proposed criteria are represented in terms of LMIs (linear matrix inequalit...

This paper focuses on the lateral control of intelligent vehicles. The aim is to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral error displacement. After passivity analysis of the system to establish the properties of passivity between the inputs and outputs, we prese...

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the...