Lyapunov–Krasovskii based FPID controller for LFC in a time-delayed micro-grid system with fuel cell power units

Purpose In this paper, a novel Lyapunov–Krasovskii stable fuzzy proportional-integral-derivative (PID) (FPID) controller is introduced for load frequency control of a time-delayed micro-grid (MG) system that benefits from a fuel cell unit, wind turbine generator and plug-in electric vehicles. Design/methodology/approach Using the Lyapunov–Krasovskii theorem, the adaptation laws for the consequent parameters and output scaling factors of the FPID controller are developed in such a way that an upper limit (the maximum permissible value) for time delay is introduced for the stability of the closed-loop MG system. In this way, there is a stable FPID controller, the adaptive parameters of which are bounded. In the obtained adaptation laws and the way of stability analyses, there is no need to approximate the nonlinear model of the controlled system, which makes the implementation process of the proposed adaptive FPID controller much simpler. Findings It has been shown that for a different amount of time delay and intermittent resources/loads, the proposed adaptive FPID controller is able to enforce the frequency deviations to zero with better performance and a less amount of energy. In the proposed FPID controller, the increase in the amount of time delay leads to a small increase in the amount of overshoot/undershoot and settling time values, which indicate that the proposed controller is robust to the time delay changes. Originality/value Although the designed FPID controllers in the literature are very efficient in being applied to the uncertain and nonlinear systems, they suffer from stability problems. In this paper, the stability of the FPID controller has been examined in applying to the frequency control of a nonlinear input-delayed MG system. Based on the Lyapunov–Krasovskii theorem and using rigorous mathematical analyses, the stability conditions and the adaptation laws for the parameters of the FPID controller have been obtained in the presence of input delay and nonlinearities of the MG system.

Scaled conjugate gradient ANN for industrial sensors calibration

In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.

Learning poly-synaptic paths with traveling waves

Traveling waves are commonly observed across the brain. While previous studies have suggested the role of traveling waves in learning, the mechanism remains unclear. We adopted a computational approach to investigate the effect of traveling waves on synaptic plasticity. Our results indicate that traveling waves facilitate the learning of poly-synaptic network paths when combined with a reward-dependent local synaptic plasticity rule. We also demonstrate that traveling waves expedite finding the shortest paths and learning nonlinear input/output mapping, such as exclusive or (XOR) function.

Optimizing the control and automation of variable torque on 3-phase induction motor using programable neuro logic controller and variable frequency drive

This paper presents optimizing the control and automation of variable torque on 3-phase induction motor using programmable neuro logic controller (PNLC) and variable frequency drive (VFD). The system was designed by developing a transfer model of the nonlinear input current from the load cells and feed to an improved PLC model for an approximate current function which is identified by the VFD with respect to the controlled load torque to power the 4.07KW rated 3-phase induction motor. The models were implemented using control system toolbox, neural network toolbox and simulated with Simulink in MATLAB. From the simulation of the improved controller model, the step response time performance of the PLC was improved from 2.25s to 1.22s with the PNLC.The simulation result of the VFD controlled 3-PIM motor shows that 37.07% of energy (power) was conserved compared to a characterized system with 20% energy conservation rate. Keywords: 4.07KW 3-phase induction motor, PLC, ANN, VFD, Energy conservation rate