This paper describes a novel auto-calibration state-trajectory-based control method and its application to electronic flow control for independent metering systems. In this paper, the independent metering architecture that is considered uses five Electro-Hydraulic Poppet Valves (EHPV's). The proposed control method is applied to four of these valves, arranged in a Wheatstone bridge configuration, to regulate the flow of hydraulic oil coming into and out of an actuator. For simplicity, the fifth valve is operated via open-loop to control the supply pressure. Experimental data presented herein demonstrate that the control method learns the valve's conductance characteristics (i.e. the inverse input-state dynamic map of the valve) while simultaneously controlling the motion of the hydraulic actuator.

We present a simple modification of the iterative learning control algorithm of Arimoto et al. (1984) for the case where the inputs are bounded and time-rate-limited. The Jacobian error condition for monotonicity of input-error, rather than output-error, norms, is specified, the latter being insufficient to assure convergence, as proved herein. To the best of our knowledge, these facts have not been previously pointed out in the iterative learning control literature. We present a new proof that the modified controller produces monotonically decreasing input error norms, with a norm that covers the entire time interval of a learning trial.

A new perceptron neural network (PNN) for functional approximation and control of a general class of nonlinear systems is introduced. The basic structure of the network along with the conditions for its exponential convergence under a suitable training law are derived. A novel discrete-time control strategy is formulated that employs the PNN for direct online estimation of the feedforward control input. The developed controller can be applied to both discrete- and continuous-time plants. Unlike most of the existing direct adaptive or learning schemes, the nonlinear plant is not assumed to be feedback linearizable. The developed controller is then applied for tracking control of a nonholonomic (free-flying) robot. The simulation results of this application demonstrate a perfect tracking performance after the network is fully trained.

Tracking control of a general class of nonlinear systems using a perceptron neural network (PNN) is presented. The basic structure of the PNN and its training law are first derived. A novel discrete-time control strategy is introduced that employs the PNN for direct online estimation of the required feedforward control input. The developed controller can be applied to both discrete- and continuous-time plants. Unlike most of the existing direct adaptive or learning schemes, the nonlinear plant is not assumed to be feedback linearizable. The stability of the neural controller under ideal conditions and its robust stability to inexact modeling information are rigorously analyzed.

Synthesis and implementation results for a recently developed class of adaptive and repetitive controllers used for motion control of mechanical manipulators are presented. The repetitive controller, which learns the input torque corresponding to a repetitive desired trajectory, requires no explicit knowledge of the manipulator equations of motion. The adaptive controller, on the other hand, which estimates the robot dynamic parameters online, may be used for more general trajectories but requires more detailed modeling information. Both schemes are computationally efficient and require no acceleration feedback of any kind; only standard position and velocity feedback information is utilized. The performances of the above-mentioned controllers were experimentally evaluated on an IBM 7545 robotic manipulator and the results were compared to those of a simple PD (proportional-derivative) feedback system and a compound-torque controller. The repetitive algorithm outperformed the other controllers by a significant margin in terms of the tracking accuracy.>

An exponentially stable direct adaptive control law for motion control of robot manipulators is presented. This control law utilizes the desired trajectory information instead of the actual manipulator joint signals for feedforward compensations and parameter adaptation. Computationally, it is extremely efficient. The exponential stability of the adaptive control algorithm is proven without requiring the persistent excitation condition and by fully considering the nonlinear dynamics of the manipulator in the analysis.>

The digital implementation of two repetitive control schemes on a SCARA-type two-axes manipulator is described. The controllers consist of a linear proportional and derivative feedback term and a feedforward repetitive term. The repetitive feedforward is adjusted periodically by using a linear combination of the position and velocity tracking errors of the current and/or previous cycles. Explicit knowledge of the structure of the manipulator model is not required. Two different learning algorithms for updating the feedforward repetitive control action are investigated. A simulation is first conducted to evaluate the performance of the repetitive controllers. The implementation of the controllers on the Berkeley/NSK SCARA robot arm is then discussed. Simulation and implementation results are in agreement and show that the use of the repetitive feedforward action in the control scheme significantly improves the tracking performance of the robot.>

A hybrid direct adaptive motion control law for robot manipulators is presented. The algorithm is hybrid in the sense that the linear portion of the controller is continuous-time, while the parameter adaptation law is discrete-time. The algorithm utilizes the desired joint signals instead of the actual manipulator joint signals in the computation of the control and parameter adaptation laws. The algorithm is extremely computationally efficient when the desired trajectories are known in advance. The stability of the algorithm is proven utilizing the full nonlinear model of the manipulator in the analysis, and without linearization of the manipulator dynamic equations. The full digital implementation of this algorithm is also discussed, and experimental results on the control of a two-degree-of-freedom SCARA arm are presented.>

The stability analysis of an adaptive control scheme for robotic manipulators, originally introduced by Horowitz and Tomizuka (1980), is presented in this paper. In the previous stability proof it was assumed that the manipulator parameter variation is negligible compared with the speed of adaptation. It is shown that this key assumption can be removed by introducing two modifications in the adaptive control scheme: 1. Reparametrizing the nonlinear terms in dynamic equations as linear functions of unknown but constant terms. 2. Defining the Coriolis compensation term in the control law as a bilinear function of the manipulator and model reference joint velocities, instead of a quadratic function of the manipulator joint velocities. The modified adaptive control scheme is shown to be globally asymptotically stable.