A survey of adaptive control technology in robotics

Previous work on the adaptive control of robotic systems is reviewed. Although the field is relatively new and does not yet represent a mature discipline, considerable attention has been given to the design of sophisticated robot controllers. Here, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review

Discrete-time adaptive control of robot manipulators

A discrete-time model reference adaptive control scheme is developed for trajectory tracking of robot manipulators. Hyperstability theory is utilized to derive the adaptation laws for the controller gain matrices. It is shown that asymptotic trajectory tracking is achieved despite gross robot parameter variation and uncertainties. The method offers considerable design flexibility and enables the designer to improve the performance of the control system by adjusting free design parameters. The discrete-time adaptation algorithm is extremely simple and is therefore suitable for real-time implementation

High speed, precision motion strategies for lightweight structures

Abstracts of published papers and dissertations generated during the reporting period are compiled. Work on fine motion control was completed. Specifically, real time control of flexible manipulator vibrations were experimentally investigated. A linear model based on the application of Lagrangian dynamics to a rigid body mode and a series of separable flexible modes was examined with respect to model order requirements, and modal candidate selection. State feedback control laws were implemented based upon linear quadratic regulator design. Specification of the closed loop poles in the regulator design process was obtained by inclusion of a prescribed degree of stability in the manipulator model. Work on gross motion planning and control is also summarized. A systematic method to symbolically derive the full nonlinear dynamic equations of motion of multi-link flexible manipulators was developed

Manipulation strategies for massive space payloads

Control for the bracing strategy is being examined. It was concluded earlier that trajectory planning must be improved to best achieve the bracing motion. Very interesting results were achieved which enable the inverse dynamics of flexible arms to be calculated for linearized motion in a more efficient manner than previously published. The desired motion of the end point beginning at t=0 and ending at t=t sub f is used to calculate the required torque at the joint. The solution is separated into a causal function that is zero for t is less than 0 and an accusal function which is zero for t is greater than t sub f. A number of alternative end point trajectories were explored in terms of the peak torque required, the amount of anticipatory action, and other issues. The single link case is the immediate subject and an experimental verification of that case is being performed. Modeling with experimental verification of closed chain dynamics continues. Modeling effort has pointed out inaccuracies that result from the choice of numerical techniques used to incorporate the closed chain constraints when modeling our experimental prototype RALF (Robotic Arm Large and Flexible). Results were compared to TREETOPS, a multi body code. The experimental verification work is suggesting new ways to make comparisons with systems having structural linearity and joint and geometric nonlinearity. The generation of inertial forces was studied with a small arm that will damp the large arm's vibration

Discrete-time explicit model reference adaptive control for robotic manipulators

In this dissertation a direct approach to discrete-time model reference adaptive control (MRAC) based on hyperstability theory is proposed to control industrial robotic manipulators. For industrial robots and manipulators, which usually have highly nonlinear and complex dynamic equations and often have unknown inertia characteristics, it is very difficult to achieve high performance with conventional control strategies. This desired high performance in terms of speed and accuracy can be obtained by adaptive control techniques. Considering the effects of gravity, process noise and payload uncertainty the MRAC approach is investigated using simulation for a three degree of freedom industrial robot. These simulation results show that adaptive control techniques can provide robust properties in spite of poor a priori information regarding the robot dynamics and operating circumstances