P.M. Bruijn

Brief paper: A time-optimal control algorithm for digital computer control

A time-optimal control algorithm for digital computer control allowing bounds on control variables and state variables is presented. Through linear programming techniques a time-optimal sequence is computed by using a linear discrete model of the process. For real-time applications feedback control is achieved by recalculating the control sequence each sampling period. In addition, an adaptive control strategy based on on-line estimation of the state variables and the parameters of the system is introduced. In order to be able to apply the algorithm in a real-time environment, computational efficiency is emphasized. An application of the algorithm to a sixth-order multivariable system is given.

Predictive Control Using Impulse Response Models

Abstract This paper describes a controller using an impulse response model of the process to predict the output of this process for a certain period of time. The predicted trajectory is compared with a desired trajectory and an algorithm is derived to calculate the sequence of future input values which minimize the difference between both trajectories. After deriving the control strategy for a single-input single-output process, this strategy is extended for a multivariable controller. Results are shown of the control of a process with two inputs and two outputs, using a minicomputer.

The Role of Defuzzification Methods in the Application of Fuzzy Control

Abstract In this article the role of defuzzification methods in the application of fuzzy controllers is adressed. In most applications described in literature the concepts of the fuzzy controllers used are presented without motivation. The defuzzifier of a fuzzy controller can deteriorate a great deal of the results achieved by the inference of fuzzy rules. Therefor a closer look at the influence of this specific part of the fuzzy controller is worthwile. It appears that the application of a specific defuzzification method can reduce completely the fuzziness of the controller. Otherwise, it is possible to use crisp representations of fuzzy sets and still being able to realize a fuzzy character of the controller. Examples are given which show the (dis)advantages of several defuzzification methods.

Demystification of Fuzzy Control

This contribution addresses a “down-to-earth” approach to- wards fuzzy control. A description is done of the basic operations in fuzzy controllers. Besides the global description critical remarks are placed at those parts which are necessary for a correct understanding of the working of fuzzy controllers. The application of fuzzy logic in control can be re- garded as a concept for the design of non-linear controllers, which is even understandable for non-control engineers.

MUSIC: A Tool for Simulation and Real-time Control

Abstract A program package, called MUSIC, has been developed for implementing simulation and real-time control algorithms on PDP-11 and VAX family computers. The control structure can be described by means of a block diagram. The blocks represent the algorithms and control functions, while the topology is defined by the connections of the blocks. A set of standard blocks is available and the user can define and add his own blocks to the system using a standard software interface. Blocks can have adjustable parameters and an arbitrary internal complexity. From the selected set of blocks an actual simulation or real-time control program and a database can be built using a program generator. The interaction with this control program is performed via a separate command interface program. With this interface the user may supply interactively all the information describing the control system. A user-written optional third program can also be added to the system to perform additional calculations outside the control loop or to realise individual command interfaces. A graphic display facility facilitates the evaluation of results and the comparison of a set of solutions. The whole system is embedded in a user-friendly menu, so the user hardly needs any computer-dependent particular knowledge.

Single-layer networks for nonlinear system identification

Abstract The modelling of unknown nonlinear system dynamics is a useful activity for model-based nonlinear control. Single-layer network models, a special class of neural networks, make interesting, practical modelling of nonlinear system dynamics possible. The optimization and analysis of single-layer network models are tractable problems, since single-layer networks constitute linear regression models. Several model representations can be cast into single-layer network form, like Wiener models and parametric approximating functions. Wiener models suffer from the disadvantage of having large numbers of parameters, while approximating functions generally allow a quite modest model size. Especially, certain radial basis type functions (RBF) for function approximation not only have effective modelling capacities, but provide stable models as well. The application of combined linear/RBF models appears to improve model optimization and quality significantly. The orthogonal least squares selection algorithm has proved to be an effective and reliable off-line optimization tool for single-layer network-type models of any representation, but it suffers from high calculation loads. Examples are given, demonstrating the capabilities of the described methods.

REQUIREMENTS AND USE OF CAD PROGRAMS FOR CONTROL SYSTEM DESIGN

Abstract In the past ten years many interactive computer-aided control system design programs have been developed at the Laboratory for Control Engineering. Both programs for the analysis and synthesis of linear and non-linear, scalar and multivariable systems, simulation programs and realtime control programs are writen and used. Each type of program has its own requirements for computer resources, like computing capacity, display facilities, operating system etc. Moreover, the interaction (conversational mode and display) between the designer and the program is very important to make these programs accepted and used, both by students and staff. In this paper several ways of implementing this interaction will be discussed, together with our experiences in writing and using these interactive design programs in a university environment.

Dice: A framework for intelligent real-time control

Abstract This article presents the development of a real-time intelligent control environment: DICE. Main emphasis is given to the requirements of knowledge-based systems to be realized in a real-time system. This results in a multi-task environment with several expert system kernels running in parallel. A blackboard mechanism is used for intertask communication. A kernel of the system uses demons to control other expert system kernels. DICE offers both boolean and multiple valued logic reasoning in separate expert system modules. As a worked out example an adaptive fuzzy logic based controller is described. Finally attention is paid to a general intelligent control scheme. Shown is that DICE is very well suited for the tasks involved in such a strategy. The role of neural networks in this configuration is also treated. The article ends with some conclusions about the use of DICE as a framework for intelligent control.

Knowledge-Based Tuning and Control

Abstract This article presents an in-line knowledge-based supervisory and tuning system for predictive control. The set up for the system is discussed and a summary is given of the control algorithm and the parameters to be tuned and initialized. After an initialisation phase in which certain parameters are set in accordance to the data required by the system, fine-tuning is performed in-line and the control system is brought to a state in which the control requirements are fulfilled. The results of the system, tuning a predictive controller, are shown in a real-time experiment.

An In-Line Time Optimal Algorithm for Digital Computer Control

Abstract A time-optimal algorithm for digital computer control is presented. Equivalent to other algorithms, which have been published recently, bounds on control variables are allowed. Additionally, constraints on the state variables can also be imposed on the process. Through linear programming a time-optimal control sequence is computed by using a linear discrete model of the system. For real-time applications an adaptive control strategy, which is yielded by calculating a control sequence each sampling period, will be given. The control sequences are based on estimated values of the^ state variables and parameters of the system. In order to apply the algorithm in a real-time environment, computational efficiency is emphasized.