An Artificial Bee Optimization Based on Command Filtered CDM-Backstepping for Electro-Pneumatic System

The proposed manuscript presents a coefficient diagram method (CDM) controller for an electro-pneumatic system. In order to tune the controller parameters, an artificial bee colony (ABC) optimization method is applied. According to the simulation results, the optimized parameters can provide better dynamic and steady state performances and higher robustness to the control algorithm, than the conventional tuned parameters

A fast and accurate global maximum power point tracking controller for photovoltaic systems under complex partial shadings

The operating conditions of partially shaded photovoltaic (PV) generators created a need to develop highly efficient global maximum power point tracking (GMPPT) methods to increase the PV system performance. This paper proposes a simple, efficient, and fast GMPPT based on fuzzy logic control to reach the point of global maximum power. The approach measures the PV generator current in the areas where it is almost constant to estimate the local maximums powers and extracts the highest among them. The performance of this method is evaluated firstly by simulation versus four well-known recent methods, namely the hybrid particle swarm optimization, modified cuckoo search, scrutinization fast algorithm, and shade-tolerant maximum power point tracking (MPPT) based on current-mode control. Then, experimental verification is conducted to verify the simulation findings. The results confirm that the proposed method exhibits high performance for complex partial irradiances and can be implemented in low-cost calculators

Tuning PID attitude stabilization of a quadrotor using particle swarm optimization (experimental)

Proportional, Integral and Derivative (PID) controllers are the most popular type of controller used in industrial applications because of their notable simplicity and effective implementation. However, manual tuning of these controllers is tedious and often leads to poor performance. The conventional Ziegler-Nichols (Z-N) method of PID tuning was done experimentally enables easy identification stable PID parameters in a short time, but is accompanied by overshoot, high steady-state error, and large rise time. Therefore, in this study, the modern heuristics approach of Particle Swarm Optimization (PSO) was employed to enhance the capabilities of the conventional Z-N technique. PSO with the constriction coefficient method experimentally demonstrated the ability to efficiently and effectively identify optimal PID controller parameters for attitude stabilization of a quadrotor

Speed estimation of a Doubly Fed Induction Machine controlled by a Field Oriented Control Strategy

This paper studies the estimation problem of speed in a Doubly Fed Induction Machine (DFIM) controlled by a Field Oriented Control (FOC) Strategy. The DFIM is the most responsive in variable speed. The chosen configuration uses two voltage inverters connected to the stator and rotor windings, to adopt the power distribution between them through the pulses distribution of the stator and the rotor in motor operating mode. It is necessary to model the DFIM in three-phase equations and then in two-phase equations faithfully representing the characteristics of the machine. From this model, we can design and simulate the control. The control by oriented rotor flux can be realized by using the speed provided by sensors or estimators. In this paper, we have used the Extended Kalman Filter (EKF) in order to avoid problems caused by the motor speed sensor and improve the robustness of the control and its performance without using any speed sensor

Nonlinear continuous-time generalized predictive control of solar power plant

This paper presents an application of nonlinear continuous-time generalized predictive control (GPC) to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A brief description of the solar power plant and its simulator is given. After that, basic concepts of predictive control and continuous-time generalized predictive control are introduced. A new control strategy, named nonlinear continuous-time generalized predictive control (NCGPC), is then derived to control the process. The simulation results show that the NCGPC gives a greater flexibility to achieve performance goals and better perturbation rejection than classical control

Nonlinear observer to estimate polarization phenomenon in membrane distillation

This paper presents a bi-dimensional dynamic model of Direct Contact Membrane Desalination (DCMD) process. Most of the MD configuration processes have been modeled as steady-state one-dimensional systems. Stationary two-dimensional MD models have been considered only in very few studies. In this work, a dynamic model of a DCMD process is developed. The model is implemented using Matlab/Simulink environment. Numerical simulations are conducted for different operational parameters at the module inlets such as the feed and permeate temperature or feed and permeate flow rate. The results are compared with experimental data published in the literature. The work presents also a feed forward control that compensates the possible decrease of the temperature gradient by increasing the flow rate. This work also deals with a development of nonlinear observer to estimate temperature polarization inside the membrane. The observer gives a good profile and longitudinal temperature estimations and shows a good prediction of pure water flux production

Nonlinear observer to estimate polarization phenomenon in membrane distillation

This paper presents a bi-dimensional dynamic model of Direct Contact Membrane Desalination (DCMD) process. Most of the MD configuration processes have been modeled as steady-state one-dimensional systems. Stationary two-dimensional MD models have been considered only in very few studies. In this work, a dynamic model of a DCMD process is developed. The model is implemented using Matlab/Simulink environment. Numerical simulations are conducted for different operational parameters at the module inlets such as the feed and permeate temperature or feed and permeate flow rate. The results are compared with experimental data published in the literature. The work presents also a feed forward control that compensates the possible decrease of the temperature gradient by increasing the flow rate. This work also deals with a development of nonlinear observer to estimate temperature polarization inside the membrane. The observer gives a good profile and longitudinal temperature estimations and shows a good prediction of pure water flux production

Nonlinear continuous-time generalized predictive control of solar power plant

This paper presents an application of nonlinear continuous-time generalized predictive control (GPC) to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A brief description of the solar power plant and its simulator is given. After that, basic concepts of predictive control and continuous-time generalized predictive control are introduced. A new control strategy, named nonlinear continuous-time generalized predictive control (NCGPC), is then derived to control the process. The simulation results show that the NCGPC gives a greater flexibility to achieve performance goals and better perturbation rejection than classical control