A Closed-Loop Output Error Approach for Physics-Informed Trajectory Inference Using Online Data

While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.

Adaptive-observer-based robust control for a time-delayed teleoperation system with scaled four-channel architecture

This article presents an innovative adaptive-observer-based scaled four-channel (4-CH) control approach applying damping injection for nonlinear teleoperation systems, which unify the study of robotic dynamic uncertainties, operator/environment force acquirements and asymmetric time-varying delays in the same framework. First, a scaled 4-CH scheme with damping injection is developed to handle time-varying delay while guaranteeing the passivity of communication channels. Then, the improved extended active observer (IEAOB) is deployed to derive the operator/environment force while addressing the issues of measurement noise and model uncertainties. Furthermore, the system stability is analyzed by choosing Lyapunov functional. Finally, the proposed method is validated through simulation.

Humanoid Robot Hand and its Applied Research

Humanoid robot hands are expected to replace human hands in the dexterous manipulation of objects. This paper presents a review of humanoid robot hand research and development. Humanoid hands are also applied to multifingered haptic interfaces, hand rehabilitation support systems, sEMG prosthetic hands, telepalpation systems, etc. The developed application systems in our group are briefly introduced.

Control and disturbances compensation in underactuated robotic systems using the derivative-free nonlinear Kalman filter

The Derivative-free nonlinear Kalman Filter is used for developing a robust controller which can be applied to underactuated MIMO robotic systems. The control problem for underactuated robots is non-trivial and becomes further complicated if the robot is subjected to model uncertainties and external disturbances. Using differential flatness theory it is shown that the model of a closed-chain 2-DOF robotic manipulator can be transformed to linear canonical form. For the linearized equivalent of the robotic system it is shown that a state feedback controller can be designed. Since certain elements of the state vector of the linearized system cannot be measured directly, it is proposed to estimate them with the use of a novel filtering method, the so-called Derivative-free nonlinear Kalman Filter. Moreover, by redesigning the Kalman Filter as a disturbance observer, it is shown that one can estimate simultaneously external disturbance terms that affect the robotic model or disturbance terms which are associated with parametric uncertainty. The efficiency of the proposed Kalman Filter-based control scheme is tested in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

Sensor fusion-based dynamic positioning of ships using Extended Kalman and Particle Filtering

The paper examines the problem of dynamic ship positioning with the use of Kalman Filter- and Particle Filter-based sensor fusion algorithms. The proposed approach enables to estimate accurately the ship's state vector by fusing the vessel's position and heading measurements coming from on-board sensors together with distance measurements coming from sensors located at the coast (e.g. radar). The estimated state vector is used in turn, in a control loop, to regulate the horizontal position and heading of the vessel. The performance of dynamic positioning of the ship based on Kalman and Particle Filtering is evaluated through simulation experiments.

Haptic feedback control in medical robots through fractional viscoelastic tissue model

In this paper, we discuss the design of an adaptive control system for robot-assisted surgery with haptic feedback. Through a haptic device, the surgeon teleoperates the medical instrument in free space, fixed on a remote robot or in contact. In free space, the surgeon feels the motion of the robot. In the present paper, we evaluated the performance of the controller on viscoelastic tissue, modeled by a fractional derivative equation. In addition, we propose a novel controller using an integer formalization process that is suitable for these tissue properties. The simulation results suggested that performance, in terms of force control and telepresence, became poorer when the conventional controller, which was designed for elastic target object, was applied to the viscoelastic tissues. In contrast, the results suggested that our proposed controller maintained its performance on the viscoelastic tissues.

Robot multiple contact control

This paper addresses the problem of contact force control for multiple contacts distributed over multiple links in a robot. This is of importance when performing complex tasks in unstructured environment, particularly in humanoid robot applications. The proposed multicontact control framework provides a new way of defining the operational space coordinates, which facilitates the specification of multiple contact control. The contact force space on multiple links is constructed as an operational space for the highest priority task. Motion control, given lower priority, can be executed using the rest of degree of freedom within the null-space of the force control. The dynamic control structure, then, provides a means to control each contact force and motion independently. This dynamic decoupling enables each contact force controller to utilize linear control theories. In particular, the contact force controllers adopt full state feedback control and estimation methods to produce robust performance with respect to modeling and parameter uncertainties. The effectiveness of the multiple contact control framework was demonstrated using a PUMA560 manipulator, with multiple contacts on the end-effector and third link. The demonstrated tasks involved controlling each of the contact forces with null-space motion.