James I. Craig

Augmenting Adaptive Approach to Control of Flexible Systems

This paper describes an approach for augmenting a linear controller design with a neural-network-based adap- tive element. The basic approach involves formulating an architecture for which the associated error equations have a form suitable for applying existing results for adaptive output feedback control of nonlinear systems. The approach is applicable to non-affine, nonlinear systems with both parametric uncertainties and unmodelled dy- namics. The effect of actuator limits are treated using control hedging. The approach is particularly well suited for control of flexible systems subject to limits in control authority. Its effectiveness is tested on a laboratory experiment consisting of a three-disk torsional pendulum system, including control voltage saturation and stiction. HIS paper describes an approach for augmenting a linear con- troller design with a neural-network (NN)-based adaptive el- ement. Previous adaptive output feedback control approaches have been applied within a control architecture that uses an inverting type of controller for the nonadaptive portion of the control system. 1,2 Considering that the vast majority of controllers are locally linear controllers, it would be highly desirable to retrofit such systems with an adaptive element, rather than to replace them with an inverting controller. In particular, within the aircraft and automobile industries there is a legacy of experience with existing control system archi- tectures, and these industries would much prefer to augment their controllers with an adaptive process, rather than replace them with a totally new architecture. This is particularly the case in applications calling for control of flexible systems. Several attempts to develop a method for adding an adaptive ele- ment to an existing controller architecture have recently appeared in the literature. 3−9 The methods 3−6 are restricted to state feedback and impose restrictive conditions with respect to properties of the reg- ulated variable and the manner in which the uncertainty affects the plant. For example, they might require that the regulated output has full relative degree (meaning that the number of times the regulated variable must be differentiated before the control appears equals the number of state variables needed to describe the plant dynamics) or that the plant uncertainty is matched (meaning that the uncertainty enters the plant dynamics in the same manner as the control). Be- cause the methods 3−7 are based on matching the state response of an idealized model with that of the true plant, they cannot be applied to a system of higher order than the model used in the design process. As a consequence, they are not robust to the unmodeled dynam- ics. The methods in Refs. 8 and 9 use an adaptive technique called input error method 10 for reconfigurable flight control. It requires, however, that the open-loop system is stable. State feedback is very restrictive, and flexible systems provide a good example in which a state feedback approach is not useful. The controller architecture proposed in this paper relies on re- cent developments in the area of nonlinear adaptive output feedback

Adaptive output feedback control with input saturation

We consider the problem of adaptive output feedback control in the presence of saturating input characteristic. The adaptive control architecture augments an existing linear control design. The approach is applicable to non-affine, nonlinear systems with both parametric uncertainty and unmodeled dynamics subject to input saturation. Boundedness of signals is shown through Lyapunovs direct method. Experimental results with a 3-disk torsional pendulum are presented to demonstrate the approach.

Adaptive Output Feedback Control of a Flexible Base Manipulator

This paper considers augmentation of an existing inertial damping mechanism by neural network-based adaptive control, for controlling a micromanipulator that is serially attached to a macromanipulator. The approach is demonstrated using an experimental test bed in which the micromanipulator is mounted at the tip of a cantilevered beam that resembles a macromanipulator with its joint locked. The inertial damping control combines acceleration feedback with position control for the micromanipulator so as to simultaneously suppress vibrations caused by the flexible beam while achieving precise tip positioning. Neural network-based adaptive elements are employed to augment the inertial damping controller when the existing control system becomes deficient due to modeling errors and uncertain operating conditions. There were several design challenges that had to be faced from an adaptive control perspective. One challenge was the presence of a nonminimum phase zero in an output feedback adaptive control design setting in which the regulated output variable has zero relative degree. Other challenges included flexibility in the actuation devices, lack of control degrees of freedom, and high dimensionality of the system dynamics. In this paper we describe how we overcame these difficulties by modifying a previous augmenting adaptive approach to make it suitable for this application. Experimental results are provided to illustrate the effectiveness of the augmenting approach to adaptive output feedback control design.

Augmentation of an existing linear controller with an adaptive element

This paper describes an approach for augmenting an existing linear controller design with a neural network based adaptive element. The basic approach involves formulating an architecture for which the associated error equation has a form suitable for applying existing results for adaptive output feedback control of nonlinear processes. The approach is applicable to non-affine, nonlinear systems with both parametric uncertainty and unmodeled dynamics. There are no restrictions placed on the relative degree of the regulated output variable, and the uncertainties can be unmatched. New results related to disturbance cancellation in an adaptive context are presented. For simplicity, only the SISO case is treated. The overall approach is illustrated using a simple model for a flexible system.

PRELIMINARY DEVELOPMENT OF AGENT TECHNOLOGIES FOR A DESIGN INTEGRATION FRAMEWORK

Presented at the 5th AIAA/NASA/USAF/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Panama City, FL, September 7-9, 1994.

DREAMS and IMAGE: A Model and Computer Implementation for Concurrent, Life-Cycle Design of Complex Systems

Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks Built on the successes of Initial architectures, requirements for a next- generation design computing infrastructure can be developed These requirements concentrate on those needed by a designer in decision mak ing processes from product conception to recycling and can be categorized in two areas design process and design information management A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while ex pending fewer resources In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition A foundation has been laid that captures these requirements in a design architecture called DRE AMS (Developing Robust Engineering Analysis Models and Specifications) In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies

TESTING OF ENERGY DISSIPATING CLADDING CONNECTIONS

Properly designed precast concrete cladding could potentially provide lateral stiffness, ductility, and energy dissipation for an overall building structure, especially during earthquakes. This paper describes a set of advanced connections that take advantage of the interaction between facade panels and structure (mainly due to horizontal interstorey drift) to dissipate energy, thereby reducing the response of the main structure. The results of an experimental program to characterize the hysteretic behaviour of advanced connections are presented. Design equations for the advanced connections are then calibrated against the test results, and the corresponding design charts are presented. It is anticipated that this research will lead to innovative ways of viewing the entire cladding system of a building.

Passive Control of Building Response Using Energy Dissipating Cladding Connections

Ductile cladding connections take advantage of the cladding-structure interaction during an earthquake to dissipate energy. An experimental test program studied the behavior of the different components of a connection system. Analytical models of the connection were incorporated into a 2D model of a six story building with cladding. Time histories of the energy demand and supply to the building, both with and without cladding, trace the response of the structure to earthquake excitations. Results show that properly designed energy dissipative connector elements can be responsible for the total hysteretic energy dissipated in the structural system. A design criterion for the connection that is formulated in terms of energy provides the optimal balance of stiffness and strength to be added to the structure by the dissipators. It results in maximum energy dissipation in the connectors, no plastification in the structural members, and reduced structural response. This approach could be applicable to both new and retrofitted buildings.

Experimental Validation of an Augmenting Approach to Adaptive Control of Uncertain Nonlinear Systems

Presented at the AIAA Guidance, Navigation, and Control Conference and Exhibit, 11-14 August 2003, Austin, Texas.

Use of Agents to Implement an Integrated Computing Environment

Integrated Product and Process Development (IPPD) embodies the simultaneous application to both system and quality engineering methods throughout an iterative design process. The use of IPPD results in the time-conscious, cost-saving development of engineering systems. To implement IPPD, a Decision-Based Design perspective is encapsulated in an approach that focuses on the role of the human designer in product development. The approach has two parts and is outlined in this paper. First, an architecture, called DREAMS, is being developed that facilitates design from a decision-based perspective. Second, a supporting computing infrastructure, called IMAGE, is being designed. Agents are used to implement the overall infrastructure on the computer. Successful agent utilization requires that they be made of three components: the resource, the model, and the wrap. Current work is focused on the development of generalized agent schemes and associated demonstration projects. When in place, the technology independent computing infrastructure will aid the designer in systematically generating knowledge used to facilitate decision-making.