Time delay in attitude sensors is one of the performance limiting factors in controlling the satellite attitude. In this paper, we investigate attitude control of a fully actuated satellite in presence of constant and known delay in attitude measurement. The proposed controller consists of a matrix gain which is computed by solving a time varying matrix differential equation depending on the time delay. We assume that the moment-of-inertia matrix of the satellite is unknown. The controller guarantees asymptotic convergence of the satellite attitude and angular velocity to their desired values in presence of delay. Finally, a simulation example is presented that illustrates performance of the proposed controller in presence of delayed attitude measurement.

Time-delay in sensor measurements can be a frequent cause of instability and performance degradation in a robotic system. In this paper, motion tracking of rigid manipulators in presence of constant and known delay in sensors is investigated. By using non-minimal model of a manipulator, a dynamically smooth controller based on the Linear Matrix Inequality (LMI) approach is proposed which guarantees asymptotic tracking of desired joint angles and velocities in presence of delayed measurements. For a given controller the maximum amount of delay that preserves system stability is computed by solving an LMI optimization and also by numerical simulations, and the results are compared. Finally, a simulation example is presented that illustrates the performance of the proposed controller in comparison with standard motion controllers.

Ignoring actuator dynamics in control of rigid manipulators can in practice result in performance degradation or loss of system stability. However, consideration of actuator dynamics usually requires measurement of robot joint torques. This paper addresses motion tracking control of an n-DOF rigid robot by taking into account its actuator dynamics. Joint torque measurement is avoided by using an adaptive observer. The backstepping technique is adopted to develop a dynamically smooth adaptive nonlinear controller dealing with uncertainties in manipulator and actuator dynamics. Semi-global convergence of motion tracking errors as well as torque estimation error are proven without any persistency of excitation condition. Simulation examples demonstrate low noise sensitivity of the proposed method in comparison with those using torque measurement.

We present a method for global estimation of joint velocities in robot manipulators. A non-minimal model of a robotic manipulator is used to design an adaptive observer capable of handling uncertainties in robot dynamics. Dimension of the proposed observer is shown to be at least 3n where n stands for the manipulator degrees of freedom. This number is less than the dimension of most of existing globally convergent adaptive observers. Global asymptotic convergence of system state estimates to their true values is achieved under no persistency of excitation condition. Smoothness of the dynamics of the proposed observer allows its easy implementation in comparison with non-smooth observers. Simulation results illustrate low noise sensitivity of the proposed observer in comparison with non-smooth observers.

This paper proposes a method to suboptimally tune the control parameters in a conventional Lyapunov-based method which shares the same concept of control design with sliding mode approach as applied to the robot manipulators. Optimal tuning of such parameters involves handling of nonlinearities in system dynamics and cost functions, which makes the problem challenging. We propose a step-by-step numerical algorithm that select suboptimal parameters while ensuring system stability. The controller is, suboptimal due to the facts that (1) it is in the form of a Slotine-type sliding mode control, (2) the numerical recursive algorithm might fall into a local minimum, and (3) the controller coefficients depend on the initial conditions of the system. The method is successfully applied to a two-link robot manipulator and the results are compared in simulation with those of a conventional controller.

A system emulation that can be used for testing a spacecraft control system with all of its hardware in place, in a 1-g laboratory environment, is presented. The system is comprised of a manipulator whose end-effector rigidly grasps a functional spacecraft, a six-axis force/moment (F/M) sensor placed at the interface of the spacecraft and the manipulator, and a control system. The controller takes the values of the force/moment as well as the manipulator's joint angles and velocities and issues torque command to drive the manipulator so that the motion of the spacecraft model in the presence of external forces replicates 0-g motion dynamics of a flight spacecraft. The control system can also modify the inertia! properties of the spacecraft so as to match those of an actual spacecraft, even if the latter is flexible and the former is rigid. The stability of the overall system is analytically investigated, and the results show that the system remains stable provided that the inertia! properties of two spacecraft are different. Important practical issues such as calibration and analysis of the sensitivity to noise and disturbance are also presented. Finally, the concept of the 0-g emulation of spacecraft is demonstrated by conducting an experiment using a robotic testbed at the Canadian Space Agency (CSA)

The application of joint-torque sensory feedback (JTF) in robot control has been proposed in the past that, unlike the model-based controllers, does not require the dynamic model of the robot links. JTF, however, assumes precise measurement of joint torque and accurate friction model of the joints. This paper presents an adaptive JTF control algorithm that does not rely on these assumptions. First, the robot dynamics with JTF is presented in a standard form with a minimum number of parameters, where the inertia matrix appears symmetric and positive definite. Second, an adaptive JTF control law is developed that requires only incorporation of uncalibrated joint-torque signals, i.e., the gains and offsets of multiple sensors are unknown. Also, all physical parameters of the joints including inertia of the rotors, link twist angles, and friction parameters are assumed to be unknown to the controller. The stability analysis of the control system is presented. Experimental results demonstrating the tracking performance of the proposed adaptive JTF controller are presented.

The application of joint-torque sensory feedback (JTF) in robot control has been proposed in the past as a substitute for the computed torque method. A controller based on JTF does not require computation of link dynamics. However, the traditional JTF assumes precise measurement of joint torque. This paper presents an adaptive JTF control algorithm that does not rely on this assumption. First, the robot dynamics with JTF is presented in a standard form, where the inertia matrix appears symmetric and positive definite. Subsequently, properties of the dynamics is inves tigated and a condition on the number of parallel joint axes for dynamic decoupling is derived. This can lead to further simplification of control structure for a class of robots. Secondly, an adaptive control law is developed incorporating uncalibrated joint torque signals, i.e., the gains and offsets of multiple sensors are unknown, into the control system. No dynamic model of a robot link is required, and all physical parameters of the joints including inertia of the rotors, link twist angles, and friction parameters are assumed unknown to the controller. Stability analysis together with a condition for bounded control input are presented. The control algorithm is experimentally applied to a robotic arm and experimental results illustrate high tracking performance, albeit neither was the torque sensor calibrated nor the parameters were known.

Most studies on adaptive coordination of multi-robot systems assume exact knowledge of system kinematics and deal only with dynamic uncertainties. However, many industrial applications involve tasks in which a multi-robot system interacts with geometrically unknown environments. In this paper, we consider a multi-robot system grasping a rigid object in contact with a geometrically unknown surface. The proposed adaptive hybrid force-motion controller guarantees asymptotic tracking of desired motion and force trajectories while ensuring exact identification of constraint Jacobian matrix without persistency of excitation condition. The control signal is smooth and does not depend on contact force derivative. The proposed adaptive controller is robustified against environmental friction and nonparametric uncertainty in environment geometry. Simulation examples are presented to illustrate the results.

Most studies on adaptive coordination of multi-robot systems assume exact knowledge of system kinematics and deal with dynamic uncertainties. However, many industrial applications involve tasks in which a multi-robot system interacts with geometrically unknown environments. In this paper we consider a multi-robot system grasping a rigid object which is in contact with a frictionless surface with unknown geometry. The proposed adaptive hybrid force-motion controller guarantees asymptotic tracking of desired motion and force trajectories while ensuring exact identification of tangential and normal directions to the constraining surface without persistency of excitation condition. The control signal is smooth and no projection is used in parameter update law. A simulation example is presented to illustrate the results.

An emulating robot is required to verify functionality of a space robot, such as the special purpose dextrous manipulator (SPDM). Control of the emulating robot is very challenging; it requires matching the contact force frequency-response of the emulating robot and that of the space robot, attenuating sensitivity to force sensor noise, while maintaining contact stability in spite of uncertain environment impedance. A /spl mu/ synthesis based controller for minimizing the weighted distance between the realized and the specified force transfer functions, minimizing noise sensitivity, and maintaining closed-loop stability in spite of a broad variation of environment parameters (stiffness and damping) is proposed. Experimental results demonstrate robust performance of the controller.