D.K. Frederick

An Expert System Architecture for Coping with Complexity in Computer-Aided Control Engineering

Abstract We discuss an architecture for an expert system to assist a control engineer in coping with the complexity of computer-aided control engineering, with major emphasis placed on implementation. Issues to be treated include: rule base organization, knowledge to be contained in various rule bases, a mechanism to switch between rule bases as the problem solution proceeds, a protocol to coordinate the symbolic computations of the inference engine with the numeric computations of conventional analysis and design software, support of non-monotonic reasoning to permit retracting and revising steps in the design process, and conversion between numeric and symbolic data. We present our approach to these issues, and give examples of rules used to represent operational knowledge and to guide the solution of the problem.

Expert-aided Control Engineering Environment for Nonlinear Systems

Abstract We have been working to develop an expert-system-based environment for Computer-Aided Control engineering (CACE). Our goal is to create a high-level user interface to conventional CACE tools, with substantial capabilities in areas that are either very complicated, or that require heuristic logic or specialized knowledge, or both. So far, we have developed rule bases for linear system diagnosis, specification development, lead lag compensator synthesis, and design validation. We report here on recent research in expert-aided CAGE for nonlinear systems. Our approach is to perform extensive experimentation (using both numerical and symbolic processing) under the direction of a rule base containing “expertise” in and heuristic strategies for nonlinear CACE. The results and status of this effort are described in detail. This work represents the first phase in an iterative process; much is being learned that will be folded back into the rule base to improve the capabilities of the expert system. We should stress that our expert system is not particularly deep at this time - our present objective is to aid the user in making the most effective use of rather complicated procedures and conventional CACE software; in many instances, this simply involves “common sense”.

Smart grid modeling approach for wide area control applications

This paper describes an approach for modeling smart grids for wide area control applications. More specifically, it is proposed to model smart grids as a set of interdependent composite networks. A composite network is one whose evolution in time and/or space is described as a composition of more than one category of networks. This work proposes an interconnection of a communication network, information network, and a power system network to model smart grids. More specifically this work proposes a quantitative model focusing on bulk generation and transmission. The resulting model will be used for studying and simulating wide area measurement and control techniques and contingencies of components. The modeling methodology is based on the initial partitioning by the National Institute of Standards and Technology (NIST) of the smart grid domains. A basic example with control of load (demand response) and generator set points over a communication link is presented.

A Second-Generation Expert System for Computer-Aided Control System Design

Abstract The CACE-III expert system for designing series lead-lag compensators for single-input/single-output plants to satisfy three frequency-response specifications has been implemented using the Gold Works inference engine and expert-system development tools. The implementation makes use of the frames and user-interface features of Gold Works to provide an environment from which further extensions of the design approach or other design methods can be created in an orderly fashion. The control-analysis program Matrix x has been used to perform the required control-system modeling and analysis calculations at the request of the expert system The tools that have been developed for handling the varied interactions between the expert system and the numerical applications program are described. Also, a number of ways in which the present design system can be improved and expanded are presented

Benchmark Problems for Computer-Aided Control System Design

Abstract The need for having benchmark problems for the evaluation of software for computer-aided control system analysis and design is discussed. The two benchmark problems that have been released to date by the Benchmark Working Group of the Technical Committee on Computer-aided Control System Design of the IEEE Control Systems Society are described, as are the plans for future problems. Information that has been learned about the preparation, distribution, and dissemination of results for this type of problem is included.

Development of an interactive graphical user interface for the UMIST control design suite

A collaborative effort has been undertaken at both UMIST and Rensselaer to use interactive computer graphics to develop an improved user interface for the UMIST suite of programs for performing analysis, design, and synthesis of control systems. The objective is to explore ways in which presently available and newly emerging interactive graphics terminals can be employed with this specific suite and with control design packages in general. This paper is a presentation of the plans for this project and a report on its progress to date.

Plant Models for Use with Computer-Aided Control System Analysis and Design Packages

Abstract A portfolio of plant models based upon real-world industrial plants and processes has been developed for use in control system analysis and design. The seven nonlinear models in this portfolio are Written In two widely-used simulatIon languages, ACSL and TSIM 2. A set of linear models has been extracted from the control literature and put in the form of Matlab and Ecstasy files. The nonlinear models are being formed into a model library that will be accessible over an academic data network in the United Kingdom

Extensions to the Ash Algorithm for Finding Root Loci

This paper describes extensions to the Ash root-locus-finding algorithm that increase its efficiency and robustness. Also described are improvements to the implementation that increase the programs portability and the amount of information available to the user. An example of the use of the improved program is given.

The relationship between periodic solutions and stability in a class of third-order relay control systems

The global asymptotic stability of third-order relay control systems with real, nonpositive eigenvalues is related to conditions necessary for the existence of periodic solutions of such systems. By application of Popovs theorem it is shown that conditions which guarantee that a system will have no symmetric periodic solutions are also sufficient to insure the absolute stability of the origin. This result allows a relay system to be designed by choosing the switching function subject to the constraint that the switching plane avoid a certain easily defined region of state space.

MATLAB Implementation of the Extended Ash Algorithm for Finding Root Loci

Abstract This paper describes extensions to the Ash algorithm for finding root loci, and the implementation of this Extended Ash algorithm within Matlab. The Ash algorithm is a method for finding the root locus of a linear system that finds each point on the locus within a specified computational area by following each branch in that area from beginning to end. Given a point on the locus and the slope angle of that point, it uses a Newton-Raphson iteration scheme to find the next point. It includes special features for finding breakaway points (points where two or more branches intersect) to high accuracy. The Extended Ash algorithm includes enhancements to the original algorithm which improve its efficiency and accuracy. This report describes the important features of the extended algorithm and describes its Matlab implementation. Also included are the results of several test problems that serve to compare the performance of the Matlab implementation of the Extended Ash algorithm with the built-in Matlab function