R.A. Hilhorst

A supervisor for control of mode-switch processes

Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, and adaptive controller could be used. When the operation point changes often, a large number of parameters would have to be adapted over and over again. This makes application of conventional adaptive control unattractive, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are co-ordinated by a ?supervisor?. This concept is referred to as a supervisor for control of mode-switch processes. It leads to an adaptive control structure which quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relearn each time. This approach has been tested on experimental set-ups of a flexible beam and of a flexible two-link robot arm, but it is directly applicable to other processes, for instance, in the (petro) chemical industry.

INTELLIGENT ADAPTIVE CONTROL OF MODE-SWITCH PROCESSES

Abstract The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For processes which operate only in a limited number of modes (so called mode-switch processes), constant adaptation is not needed or desired. In this paper an intelligent extension of adaptive control will be presented, in which process behaviour can be stored in a memory, retrieved from it and evaluated for each mode of operation. This intelligent memory concept leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to relearn every time. This approach has been tested on a simulation model of an assembly robot, but it is directly applicable to many processes in the (petro)chemical industry.

NEURAL NETWORK BASED CONTROL OF MODE-SWITCH PROCESSES

In ‘mode-switch processes’, the dynamics are fairly the same in one mode of operation, but are truly different in another one. For such processes, this paper presents a ‘supervisory system’ which is based on the storage and retrieval of controllers designed at various modes. Modes are recognized by a neural network based decision-rule which rewards and penalizes the active controller on the basis of its closed-loop performance. This approach has been tested in a chemical reactor simulation where the objective is to reject disturbances. However, the concept can be applied to other processes as well.

An Intelligent Supervisor for Adaptive Mode-Switch Control

Abstract An adaptive controller is described, based on the idea that information gathered when new controller settings are computed is not forgotten hut stored in a memory. If the same situation is encountered again, this information can he retrieved from the memory and used for immediate adjustment of the controller. Various methods of data storage and retrieval are discussed. Best results are obtained with a method which from a number of models which run in parallel with the process, selects the model which describes the present process behaviour most adequately. Experimental results with a flexible beam and with a two-link flexible robot arm demonstrate that this approach is an attractive alternative for conventional adaptive or robust control systems.

SUPERVISORY CONTROL OF MODE-SWITCH PROCESSES: APPLICATION TO A FLEXIBLE BEAM

Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, an adaptive controller could be used. Then, if the operation point changes often, a large number of parameters would have to be adapted over and over again. This prohibits application of conventional adaptive control, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are coordinated by a “supervisor”. This concept is referred to as supervisory control. It leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relcarn each time. This approach has been tested on an experimental set-up of a flexible beam, but it is directly applicable to processes in e.g. the (petro)chemical industry as well.