Jednostavna neizrazita identifikacija implementirana u naprednom regulatoru

The paper focuses on identification issues of the advanced controller ASPECT* that is implemented on a simple PLC platform with an extra mathematical coprocessor and is intended for the advanced control of complex plants. The model of the controlled plant is obtained by means of experimental modelling using an online learning procedure that combines model identification with pre- and post-identification steps that provide reliable operation. It is shown that acceptable performance of the system is obtained despite difficult conditions that may arise during operation.Ovaj se rad usredotočuje na problematiku identifikacije na osnovi naprednog regulatora ASPECT* implementiranog na jednostavnoj PLC platformi s dodatnim matematičkim koprocesorom, koji se želi koristiti za naprednu regulaciju složenih postrojenja. Model reguliranog postrojenja dobiva se eksperimentalnim modeliranjem, pri čemu se koristi on-line procedura učenja s pred- i post-identifikacijskim koracima koji osiguravaju pouzdan rad. Pokazano je da se prihvatljive performance sustava dobivaju unatoč teškim uvjetima koji se mogu pojaviti tijekom rada

Jednostavna neizrazita identifikacija implementirana u naprednom regulatoru

The paper focuses on identification issues of the advanced controller ASPECT* that is implemented on a simple PLC platform with an extra mathematical coprocessor and is intended for the advanced control of complex plants. The model of the controlled plant is obtained by means of experimental modelling using an online learning procedure that combines model identification with pre- and post-identification steps that provide reliable operation. It is shown that acceptable performance of the system is obtained despite difficult conditions that may arise during operation.Ovaj se rad usredotočuje na problematiku identifikacije na osnovi naprednog regulatora ASPECT* implementiranog na jednostavnoj PLC platformi s dodatnim matematičkim koprocesorom, koji se želi koristiti za naprednu regulaciju složenih postrojenja. Model reguliranog postrojenja dobiva se eksperimentalnim modeliranjem, pri čemu se koristi on-line procedura učenja s pred- i post-identifikacijskim koracima koji osiguravaju pouzdan rad. Pokazano je da se prihvatljive performance sustava dobivaju unatoč teškim uvjetima koji se mogu pojaviti tijekom rada

A Stable Self-Tuning Fuzzy Logic Control System for Industrial Temperature Regulation

A closed-loop control system incorporating fuzzy logic has been developed for a class of industrial temperature control problems. A unique fuzzy logic controller (FLC) structure with an efficient realization and a small rule base that can be easily implemented in existing industrial controllers was proposed. The potential of FLC in both software simulation and hardware test in an industrial setting was demonstrated. This includes compensating for thermo mass changes in the system, dealing with unknown and variable delays, operating at very different temperature set points without retuning, etc. It is achieved by implementing, in the FLC, a classical control strategy and an adaptation mechanism to compensate for the dynamic changes in the system. The proposed FLC was applied to two different temperature processes and performance and robustness improvements were observed in both cases. Furthermore, the stability of the FLC is investigated and a safeguard is established

Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Time-Delay Systems with Unknown Dead-Zone Input

The tracking control problem of uncertain nonlinear time-delay systems with unknown dead-zone input is tackled by a robust adaptive fuzzy control scheme. Because the nonlinear gain function and the uncertainties of the controlled system including matched and unmatched uncertainties are supposed to be unknown, fuzzy logic systems are employed to approximate the nonlinear gain function and the upper bounded functions of these uncertainties. Moreover, the upper bound of the uncertainty caused by the fuzzy modeling error is also estimated. According to these learning fuzzy models and some feasible adaptive laws, a robust adaptive fuzzy tracking controller is developed in this paper without constructing the dead-zone inverse. Based on the Lyapunov stability theorem, the proposed controller not only guarantees that the robust stability of the whole closed-loop system in the presence of uncertainties and unknown dead-zone input can be achieved, but it also obtains that the output tracking error can converge to a neighborhood of zero exponentially. Some simulation results are provided to demonstrate the effectiveness and performance of the proposed approach

Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Time-Delay Systems with Unknown Dead-Zone Input

The tracking control problem of uncertain nonlinear time-delay systems with unknown dead-zone input is tackled by a robust adaptive fuzzy control scheme. Because the nonlinear gain function and the uncertainties of the controlled system including matched and unmatched uncertainties are supposed to be unknown, fuzzy logic systems are employed to approximate the nonlinear gain function and the upper bounded functions of these uncertainties. Moreover, the upper bound of the uncertainty caused by the fuzzy modeling error is also estimated. According to these learning fuzzy models and some feasible adaptive laws, a robust adaptive fuzzy tracking controller is developed in this paper without constructing the dead-zone inverse. Based on the Lyapunov stability theorem, the proposed controller not only guarantees that the robust stability of the whole closed-loop system in the presence of uncertainties and unknown dead-zone input can be achieved, but it also obtains that the output tracking error can converge to a neighborhood of zero exponentially. Some simulation results are provided to demonstrate the effectiveness and performance of the proposed approach

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter’s motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators’ performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control

Adaptive Robust Control of Biomass Fuel Co-Combustion Process

The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications