PSO-based PID controller design for an energy conversion system using compressed air

U ovom se radu predlaže optimalni kontrolni algoritam za rješavanje problema niske performanse zbog nelinearnih značajki pneumatskog motora u sustavima za pretvorbu energije pomoću stlačenog zraka. Učinkovitost predloženog algoritma se ispituje na sustavu za pretvorbu energije koji uključuje kompresor, proporcionalni ventil, pneumatski motor (PM), generator istosmjerne struje s trajnim magnetom (PMDC) i kontrolnu karticu. Kontrolna funkcija sustava provodi se pogonjenjem proporcionalnog ventila s kontrolnim signalima što se postiže ovisno o greški napona na izlazu PMDC generatora. U toj konstrukciji, optimalni proporcionalni-integralni-derivativni (PID) regulator izravno podešava vlastite parametre pojačanja algoritmom optimizacije roja čestica - particle swarm optimization (PSO) u skladu s radnim uvjetima primijenjenog sustava. U svrhu promatranja učinaka PID-regulatora zasnovanog na PSO na rad sustava, sustav za pretvorbu energije se kontrolira PID regulatorom diskretnog vremena. Eksperimentalni rezultati pokazuju da PID regulator zasnovan na PSO osigurava robustniju regulaciju rada nego PID regulator diskretnog vremena kod različitih radnih uvjeta.In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions

Fuzzy control turns 50: 10 years later

In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.Guerra, T.; Sala, A.; Tanaka, K. (2015). Fuzzy control turns 50: 10 years later. Fuzzy Sets and Systems. 281:162-182. doi:10.1016/j.fss.2015.05.005S16218228

PSO-BASED PID CONTROLLER DESIGN FOR AN ENERGY CONVERSION SYSTEM USING COMPRESSED AIR

In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities

Background Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That’s why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Methods Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Results Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values

A Modelling Study of Renewable and Stored Energy Sharing and Pricing Management System Developed for Multi-Apartment Complexes

5th IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) -- OCT 12-15, 2014 -- Istanbul, TURKEYWOS: 000393467600070A unique system that will enable the efficient and shared usage of PV panels in multi-apartment complexes and sharing of energy is developed in this study along with a modeling scheme. Efficient and shared usage of the panels which is suggested as the solution requires sharing and pricing the energy in the apartment complex. In this modeling study which is conducted in Matlab and Simulink environment, the available energy is initially allocated for all the apartments and has them use it for free. The modeled system is applied to a three-apartment complex and the simulation results are obtained based on the electricity pricing tariff in Turkey. The amounts reflected in the electricity bill in the cases where the solar energy used and not used are calculated separately and compared later in the experimental results. It is demonstrated that the shared energy of the apartment complex can be allocated in an efficient and fair way. Moreover, it is proved that the budget generated by using the cheap energy can pay off the fix costs such as battery charge and renewal costs.IEEE PE