Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

A novel intelligent adaptive control of laser-based ground thermal test

AbstractLaser heating technology is a type of potential and attractive space heat flux simulation technology, which is characterized by high heating rate, controlled spatial intensity distribution and rapid response. However, the controlled plant is nonlinear, time-varying and uncertainty when implementing the laser-based heat flux simulation. In this paper, a novel intelligent adaptive controller based on proportion–integration–differentiation (PID) type fuzzy logic is proposed to improve the performance of laser-based ground thermal test. The temperature range of thermal cycles is more than 200K in many instances. In order to improve the adaptability of controller, output scaling factors are real time adjusted while the thermal test is underway. The initial values of scaling factors are optimized using a stochastic hybrid particle swarm optimization (H-PSO) algorithm. A validating system has been established in the laboratory. The performance of the proposed controller is evaluated through extensive experiments under different operating conditions (reference and load disturbance). The results show that the proposed adaptive controller performs remarkably better compared to the conventional PID (PID) controller and the conventional PID type fuzzy (F-PID) controller considering performance indicators of overshoot, settling time and steady state error for laser-based ground thermal test. It is a reliable tool for effective temperature control of laser-based ground thermal test

Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions