This paper presents a new strategy for task space control in the cooperative manipulation framework. We extend the cooperative dual task-space (CDTS) - which uses dual quaternions to represent the bimanual manipulation-to explicitly regard self-motion dynamics that arise from redundant kinematics. In this sense, we propose a flexible task execution criterion that enriches the Jacobian null space with additional degrees of freedom by relaxing control requirements upon specific geometric task objectives. The criterion is satisfied with a hysteresis-based switching strategy that ensures stability and convergence upon traditional and relaxed constraints. Simulation results highlight the importance and effectiveness of the proposed technique.

This paper addresses the H∞robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H∞control criteria that ensure asymptotic convergence, whereas reducing the influence of disturbances upon the system stability. Also, with a new metric of dual quaternion error in SE(3) we prove independence from robot coordinate changes. Simulation results highlight the importance and effectiveness of the proposed approach in terms of performance, robustness, and energy efficiency.

This work addresses the problem of stochastic data fusion for systems liable to heavy disturbances, which denote environmental perturbations strong enough to modify the system's internal structure, including signal interference, sensor faults, physical structure modification, and many other sources of disturbance. In these such cases, traditional filtering methods usually fail to provide reliable estimates because of the highly corrupted sensor measurements. This work proposes to model the data fusion problem for heavily disturbed systems through a hybrid systems modeling framework and presents an online hybrid stochastic filter capable of tracking the system's state in unfavorable operating conditions. Simulated and experimental results compare the proposed filter's performance with the traditionally used Extended Kalman Filter (EKF) and show its usefulness as a robust localization filter for an Unmanned Aerial Vehicle (UAV) designed for aerial power lines inspection.

This comment presents an enhancement over the robust predictor recently developed in the work by Dong and You. Besides uncertainties in both state and output matrices, the proposed design also allows dynamic and measurement noises to be with unknown time-variant expected values and to be correlated with unknown time-variant cross-covariance. The enhanced predictor leads to a less conservative design which is confirmed by a numerical example.

In the above titled paper (ibid., vol. 17, no. 2, pp. 213-216, Feb. 10), it was noticed that there are missing the mean of the noises in some equations. Corrected equations are presented here.

This paper presents a new application in the field of rehabilitation robotics. It is part of a research project which consists of the development of a robotic leg prosthesis for above knee amputees. This application aims at providing real-time estimates of the prosthesis foot orientation with respect to ground. An Extended Kalman Filter (EKF) is used in order to estimate the foot orientation based on gyroscope and infrared measurements. Moreover a constraint equation is also included in the EKF to guarantee the constraints in the estimates. Preliminary tests were performed in a platform built for the prosthesis prototype. The results discussed in this paper reveal the feasibility of such technique.

This paper deals with optimal prediction problem for uncertain discrete time singular systems. The parametric uncertainty is assumed to be norm bounded. The singular robust Kalman-type predictor and the corresponding recursive Riccati equation are obtained in their most general formulation where all parameter matrices of the underlying linear model are subject to uncertainties. The quadratic functional developed to deduce this filter combines least squares and penalty functions approaches.

This work is focused on the design of a rotor speed regulator for small-scale helicopters. A nominal model identification procedure for the main rotor system is performed using an iterative hybrid procedure. Such a model has both linear and nonlinear components and models not only engine throttle but also the effect of collective pitch variation. Collective pitch is the main disturbance source. The identified model is used in the design of different control strategies: polynomial, optimal linear quadratic, both with and without integral action, and H∞. Experimental evaluations were conducted to verify the controllers performance under different circumstances.