Robust control of nonlinear systems with disturbances and uncertainties is addressed in this paper using disturbance observer based control technique. In this framework, the “disturbance” is a generalized concept, which may include external disturbances, unmodeled dynamics, and system parameter perturbations. The existing disturbance observer based control (DOBC) methods were only applicable fo...

In this paper, the passivity and passification problems are investigated for a class of uncertain stochastic fuzzy systems with time-varying delays. The fuzzy system is based on the Takagi-Sugeno (T-S) model that is often used to represent the complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning. To reflect more realistic dynamical behaviors of the system, both the parameter unc...

The problem of adaptive output tracking is researched for a class of nonlinear network control systems with parameter uncertainties and time-delay. In this paper, a new program is proposed to design a state-feedback controller for this system. For time-delay and parameter uncertainties problems in network control systems, applying the backstepping recursive method, and using Young inequality to...

Purpose – To propose a new computational approach for parameter estimation in the Bayesian framework. Aposteriori PDFs are obtained using the polynomial chaos theory for propagating uncertainties through system dynamics. The new method has the advantage of being able to deal with large parametric uncertainties, non-Gaussian probability densities, and nonlinear dynamics. Design/methodology/appro...

This paper presents a practical analytical approach to evaluating continuous, monotonic functions of independent fuzzy numbers. The approach is based on a parametric α-cut representation of fuzzy numbers and allows for the inclusion of parameter uncertainties into mathematical models.

We explain that part of the reduction in the parameter uncertainties in the computations of Apgar et al. (Mol. Biosyst. 2010, 6, 1890-900) is due to a greatly increased number of effective data points.

We pose and solve a parameter estimation problem in the presence of bounded data uncertainties. The problem involves a minimization step and admits a closed form solution in terms of the positive root of a secular equation.

In this paper, a neural network controller for constrained robot manipulators is presented. A feedforward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the feed-forward neural network controller. The neural network weights are tuned on-line, with no off-line learning phase required. It is shown that the controller ...

This paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. The robust Kalman filter of Durovic and Kovacevic is extended to include unknown-but-bounded parameter uncertainties in the state or observation matrix. We first formulate the robust state estimation problem as an M-estimation problem, which leads to an unconstrained nonlinear optimization problem....

Abstract—This paper is concerned with the problem of H∞ linear parameter-varying (LPV) filter design for discrete-time linear systems where the measurement of the scheduling parameters may be affected by additive and multiplicative uncertainties. By conveniently modeling the uncertainties and the timevarying parameters, new robust linear matrix inequality (LMI) conditions for the existence of a...