Piotr Skupin

Metamorphic Controller for Collaborative Design of an Optimal Structure of the Control System

When designing a control system, the customer specifies some control requirements and the expert provides the parameterized optimal controller. A change of the control algorithm to a more advanced one may lead to a better performance of the closed loop system. On the other hand, implementation and parameterization of the advanced controllers require more extensive knowledge. A possible solution is a group of cooperating experts that are able to determine the most suitable control algorithm, depending on the customer’s requirements. However, in practice, hiring more experts is an expensive approach. Hence, the performance of majority of industrial systems is not optimal. The paper presents the metamorphic controller with extended functionality for selection of an optimal control algorithm (including advanced controllers). As a result, only one expert, cooperating with the customer, is sufficient to ensure the optimal system performance. The proposed solution has been implemented and tested on the industrial controller.

Adaptive dynamic matrix control with interpolated parameters

The classical (non-adaptive) Dynamic Matrix Control (DMC) algorithm is designed and tuned for a chosen operating point of the nonlinear plant. Hence, the performance of the control system may be deteriorated in a wide range of operating levels. The paper discusses a possibility of using an adaptive Dynamic Matrix Control (DMC) for a hydraulic nonlinear plant. Depending on the current value of the process variable, the idea is to use linear spline interpolation to calculate the controller parameters that appear in the final form of equation for the control increment. As a result, it is possible to reduce the number of necessary calculations. Numerical simulations of the closed-loop system show that the adaptive DMC algorithm ensures satisfactory performance over a wide range of operating levels in comparison to the classical DMC tuned for a single operating point.

Cooperative Access to Hierarchical Data from Biotechnological Pilot-Plant

A hierarchy of data is dependent on the level of its complexity and on the amount of knowledge needed for its interpretation. Cooperation between users of the data at different levels of hierarchy requires development of plans to access the data for individual users depending on their needs. Such plans can be built basing on the common field of experience notion. The work presented in the paper focuses on increasing the cooperation efficiency through the use of a social bookmarking system maintaining the unambiguity of hierarchical knowledge structures. Lack of such structures would significantly impair the cooperation process. The proposed multiagent system contains ontologies for classifying non-hierarchical components and enables the exchange and comparison of the data structures. Thus, tags associated with the graph-based rules of social bookmarking can be classified using strict hierarchical terms. Presented solution was evaluated by supporting microscopic observations during experimentation on biostimulation of biological wastewater treatment process.

The application of multi-agent system in monitoring and control of nonlinear bioprocesses

Most of the continuous processes (e.g. chemical processes) are monitored and controlled in the classical way, i.e. entirely by the process operator based on the measurement data. However, in many cases, due to the nonlinear nature of some continuous processes, the use of this approach may not always be sufficiently efficient. In particular, it concerns a large class of biological processes (bioprocesses) for which more complex data analysis is required. Hence, this paper presents the possibility of application of a Multi Agent System (MAS) as a support for the process operator. The proposed solution being a combination of the classical approach and the MAS, called Hybrid Intelligent Operations and Measurements (HIOM), is tested based on the simulation runs of a mathematical model of the bioprocess.

Cooperative operating control for induction or elimination of self-sustained oscillations in CSTB

The problem of cooperative control is especially important in the case of selection of an appropriate mode of operation for a wide class of bioprocesses. In classical approach, this can be achieved via SCADA systems used by process operators. However, due to the nonlinear nature of bioprocesses, the operators usually are not able to assess the efficiency of a bioprocess, especially in the presence of self-sustained oscillations (SSO) of the biomass concentration. Hence, they must cooperate with experts who are usually geographically dispersed. This paper presents the solution of the above-stated problems using an additional server application in the layer of supervisory control. The main tasks of the application are to provide the process data (collected by the SCADA system) to a group of experts and allow them to discuss possibilities of enhancing the efficiency of the bioprocess. The taken decisions are then sent to the operator.

Practical verification of adaptive dynamic matrix control with interpolated parameters

The paper presents the experimental studies of the adaptive dynamic matrix control (DMC) algorithm with interpolated parameters. The idea of adaptation mechanism is to determine the controller parameters that appear in the control law equation for several operating points of the system. In turn, the parameter values between the two consecutive operating points are calculated by using linear spline interpolation. The effectiveness of the adaptive DMC algorithm is studied for the electric flow heater for various number of operating points (interpolation nodes) in the presence of step changes in the set point and mean power of the electric heater. The obtained results show that it is possible to significantly reduce the number of interpolation nodes, while keeping the control system performance at a satisfactory level.

An Alternative Approach for Oscillatory Behaviour Control in a Nonlinear Bioprocess

Abstract The nonlinear nature of bioprocesses often leads to the occurrence of the self-sustained oscillations of the biomass concentration in continuous flow bioreactors. For some practical reasons, sometimes it is necessary to control the oscillatory behaviour, which is usually achieved by changing the dilution rate, but this is not always the best option. Hence, the main idea of this paper is to introduce an additional substrate, which has been previously diluted. Based on the numerical analysis of an unstructured mathematical model, it is shown that by mixing two various substrates (the main and the diluted one), it is possible to induce or eliminate the sustained oscillations. As a result, the contribution of both substrates to the mixture and the degree of dilution of the additional substrate can be treated as new control variables.

Optimization of Engineering Design Cycles in Enterprise Integration

The paper presents the concept of project life cycle optimization, which is based on the formalization of domain knowledge and decomposition of the controlled system into subsystems. The formalization of knowledge concerns each of the individual subsystems by describing its states and functions. Such an approach can greatly reduce costs and time, which is needed for multiple iterations during the project life cycle. This is because the formalization of knowledge simplifies modifications of the control system software and architecture, which means that there is no need to commence the designing process again. Moreover, owing to the presented approach, creation of ontology and more advanced control systems e.g. multiagent based algorithms are significantly shortened and simplified. The presented solution is currently being implemented in the designing process of a real micro-grid.

AI-Based Support for Experimentation in an Environmental Biotechnological Process

This paper presents an AI-based system that supports experimentation and control in the domain of environmental biotechnology. The objective of the experiments is to verify hypotheses on biostimulation of an activated sludge by sustaining oscillations in its metabolism to improve degradation of a hardly removable organic waste in the wastewater treatment plants. The presented system incorporates the application of a multi agent system (MAS), which uses ontologies and rules, and also a smart image processing method. One of the main tasks of the MAS is to provide a support for analysis of the off-line microscopic measurements based on both the rules describing the trends of analytical measurements and the quantitative on-line microscopic observations. Finally, the proposed MAS may keep track of results provided by the experts with results obtained on the basis of rules. As a result, the appropriate biostimulation control may prevent or reduce the climate changes.

Agent-Based control of self-sustained oscillations in industrial processes: a bioreactor case study

Application of the Agent and Multiagent Systems (AMAS) in the industrial continuous processes can be a quite interesting and effective solution, especially for monitoring and controlling purposes of bioreactor systems. In the classical approach, a process operator controls the process, but sometimes must take some essential decisions concerning the choice of control strategy. In the case of biological processes, due to their highly nonlinear nature, this can be quite difficult task. For instance, the oscillatory behavior of the bioreactor may lead to higher or lower average biomass concentrations. Hence, there is a need to support the operator by measuring and controlling some additional parameters and this cannot be achieved using only measuring devices and classical control algorithms alone. Based on the agent technology, it has been shown that it is possible to support the operator and to achieve process goals.