This paper is devoted to the construction of a probabilistic model of uncertain rigid bodies for multibody system dynamics. We first construct a stochastic model of an uncertain rigid body by replacing the mass, the center of mass and the tensor of inertia by random variables. The prior probability distributions of the stochastic model are constructed using the maximum entropy principle under t...

This paper is concerned with robust synchronization of a system of uncertain agents whose mutual communications are constrained by the topology of the associated communication graph. The uncertainty of each agent is modeled as a gain-bounded multiplicative uncertainty. A sufficient condition for achieving the robust synchronization is derived based on the input-output stability argument under t...

A robust power and rate control algorithm is proposed for distributed wireless networks where the network dynamics is modelled as an uncertain discrete-time state-delayed system. 2005 Elsevier Ltd. All rights reserved.

Conjugated polymer actuators can be employed to achieve micro and nano scale precision, and have wide range of applications including biomimetic robots and biomedical devices. In comparison to robotic joints, they do not have friction or backlash, but on the other hand, they have complicated electro-chemo-mechanical dynamics which makes modeling and control of the actuator really difficult. Bes...

We propose a mini-max feedback control (MMFC) model as a robust approach to human motor control under conditions of uncertain dynamics, such as structural uncertainty. The MMFC model is an expansion of the optimal feedback control (OFC) model. According to this scheme, motor commands are generated to minimize the maximal cost, based on an assumption of worst-case uncertainty, characterized by f...

Sensory noise and feedback delay are potential sources of instability and variability for the on-line control of movement. It is commonly assumed that predictions based on internal models allow the CNS to anticipate the consequences of motor actions and protect the movements from uncertainty and instability. However, during motor learning and exposure to unknown dynamics, these predictions can ...

Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy stability. To accurately compensate for the in sliding mode (SMC), a new parallel network (PCR) is proposed this paper. The parallelizes radial basis function convolutional neural network, which gives it advantage of making full use one-dimensional data fitting results...

In this paper, we consider the hierarchical control problem for a class of uncertain hybrid dynamical systems. The continuous dynamics of this class of uncertain hybrid systems are described by linear difference state equations, whose right side functions are unknown but lie within some convex hulls of known functions. Our control objective is for the closed loop system to exhibit a desired beh...