In this paper, we begin by showing that the fuzzy model reference learning controller (FMRLC) can be used to reconfigure the nominal controller in an F-16 aircraft to compensate for various actuator failures without using explicit failure information. Next, we show that the performance of the FMRLC can be significantly enhanced by exploiting failure detection and identification (FDI) information to achieve a "performance adaptive" system that seeks an appropriate performance level depending on the type of failure that occurred. We develop an expert supervision strategy for the FMRLC that uses only information about the time at which a failure occurs and show that it achieves higher performance control reconfiguration than an unsupervised FMRLC. In addition we show that similar performance can be achieved if we only use estimates of the failure time and magnitude obtained from a fuzzy estimator. We close our study with a brief assessment of the advantages and disadvantages of the approaches used in this paper.>

There are two main drawbacks in fuzzy control: 1) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters; and 2) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we illustrate these two problems on a two-link flexible robot testbed by: 1) developing, implementing, and evaluating a fuzzy controller for the robotic mechanism, and 2) illustrating that payload variations can have negative effects on the performance of a well designed fuzzy control system. Next, we show how to develop and implement a fuzzy model reference learning controller for the flexible robot and illustrate that it can automatically synthesize a rule-base for a fuzzy controller that will achieve comparable performance to the case where it was manually constructed, and automatically tune the fuzzy controller so that it can adapt to variations in the payload.>

This investigation focuses on the design and implementation of rule-based controllers for end point position control of a two-degree-of-freedom robot with very flexible links. We begin by showing how our intuitive understanding of how to control the robot, that was gained over several years of experience, can be used in the construction of a direct fuzzy controller Next, we investigate the use of a two-level hierarchical rule-based controller with a simple upper-level "expert controller" that captures our knowledge about how to supervise the application of lower-level fuzzy controllers during movements in the robot workspace. Overall, the rule-based supervisory control results have proven to be extremely effective for vibration suppression in the laboratory test bed of this study comparing favorably (in terms of performance, design complexity, and implementation issues) to a variety of conventional techniques attempted to date.>

In this paper we show that two ellipsoid algorithms can be used to train single-layer neural networks with general staircase nonlinearities. The ellipsoid algorithms have several advantages over other conventional training approaches including (1) explicit convergence results and automatic determination of linear separability, (2) an elimination of problems with picking initial values for the weights, (3) guarantees that the trained weights are in some “acceptable region,” (4) certain “robustness” characteristics, and (5) a training approach for neural networks with a wider variety of activation functions. We illustrate the training approach by training the MAJ function and then by showing how to train a controller for a reaction chamber temperature control problem.

Analysis and experimentation are described for a two-link apparatus in which both members are very flexible. Attention is focused on endpoint position control for point-to-point movements, assuming a fixed reference frame for the base, with two rotary joints. Each link in instrumented with acceleration sensing and is driven by a separate motor equipped with velocity and position sensing. The control perspective adopted is to implement a two-stage control in which the vibration control problem for fine motion endpoint positioning is considered separately from the gross motion, large angle skew problem. In the first stage the control law shapes the actuator inputs for the large angle movement in such a way that minimal energy is injected into the flexible modes, while in the second phase an endpoint acceleration feedback scheme is employed in independent joint control for vibration suppression at the link endpoints.>

The problem of endpoint position control for a planar manipulator which has two very flexible links is considered. Discussions on system identification techniques are presented relative to the laboratory apparatus under consideration. The resulting models are used in static and dynamic fixed-controller designs, as well as in a self-tuning controller design for the case in which the manipulator carries an unknown payload at the endpoint of the second link. Experimental results are presented to illustrate the effectiveness of the control and system identification.>

The authors present experimental results on endpoint position control of a single-link, very flexible robot arm carrying an unknown, varying payload. The control objective is to maintain endpoint position accuracy in the presence of flexure effects after rapid movement to a rigid-body slew-angle commanded position. Fast, simple, and efficient frequency-domain schemes are used for online controller gain adjustment within an effective scheduling framework. Only endpoint acceleration measurements and motor shaft angle measurements are utilized in relatively simple control laws, where the appropriate gains have been scheduled in accordance with modal frequency information corresponding to a varying, unknown payload.>

The authors report laboratory results for a single-link flexible manipulator arm in which three separate control strategies are compared and contrasted: compensation using classical root locus techniques with endpoint position feedback, a full state feedback observer-based design, and compensation using endpoint acceleration feedback. The last technique, using accelerometer feedback, has received little attention to date. The presented results indicate great promise for its use in flexible manipulator control.>

A systematic algorithm is presented for the generation of the kinematic and dynamic equations of multilink rigid and/or flexible manipulators. The MACSYMA symbolic algebraic manipulation language is utilized to implement this algorithm, and an optimum code in terms of memory space is generated. Kinematic equations are derived using homogeneous transformation matrices, and the dynamic equations are obtained subsequently using the Euler-Lagrange formulation. The advantages of this algorithm and simulation results for control implementation are presented.>

The control problem for a two-arm robotic system in co-ordinated motion is addressed. A hierarchical framework, employing two levels of control hierarchy, is utilized, the decentralized model reference adaptive control approach using variable structure controllers (DMRA-VSC) is applied. Within the control hierarchy, the DMRA-VSC strategy is accomplished at the lower level, where control is responsible for the servoing of each joint. These local controllers are coordinated by the high-level, central controller, whose task is to provide the local controllers with the upper bound on the dynamical interactions with other subsystems. Advantages of the DMRA-VSC approach for multiple manipulator control include the inherent robustness properties to nonlinearities and interaction effects, the decentralization structure facilitating ease in multiple manipulator system programming and implementation, and the general structure of the controller which allows further extensions.

This paper presents a straightforward control strategy applied to an N-link manipulator holding an unknown load and driving its end effector along a prespecified trajectory. The control is constituted into two primary components. The non-adaptive component is derived from the inverse problem technique while the adaptive component is computed via the application of sensitivity analysis applied to the completes centralized dynamic model of the manipulator. The result is a robust adaptive controller which tunes its parameters at specified time instants and can withstand all expected variations of the payload. The control synthesis is illustrated by simulations in a 2-link planar manipulator holding an unknown load.

A back somersault maneuver is studied for a four-link planar biped by a digital computer simulation. The maneuver consists of the following. 1) The takeoff phase: the system is in contact with the ground, and the ground reaction forces are instrumental in propelling the system in the air with appropriate position angles and angular velocities. 2) The flight phase: this is the airborne phase of motion for the system. The motion in this phase is governed by the trajectory of the center of gravity and the conservation of angular momentum. 3) The landing phase: in this phase the biped reestablishes a point of contact with the ground. The contact is such that impulsive forces are minimum. The kinetic energy of the system is dissipated, and a final vertical standing stance is achieved. Two control strategies are proposed. In the takeoff and landing phases a feedback strategy is needed. The airborne phase is ballistic, and an open-loop control strategy is sufficient. In all three phases of motion the biped model follows a set of desired trajectories which are approximations of a gymnast's performance. Digital computer simulations are presented to illustrate the motions and the effectiveness of the control strategies.