Optimizing of the installed capacity of hybrid renewable energy with a modified MPPT model

The lack of wind speed capacity and the emission of photons from sunlight are the problem in a hybrid system of photovoltaic (PV) panels and wind turbines. To overcome this shortcoming, the incremental conductance (IC) algorithm is applied that could control the converter work cycle and the switching of the buck boost therefore maximum efficiency of maximum power point tracking (MPPT) is reached. The operation of the PV-wind hybrid system, consisting of a 100 W PV array device and a 400 W wind subsystem, 12 V/100 Ah battery energy storage and LED, the PV-wind system requires a hybrid controller for battery charging and usage and load lamp and it’s conducted in experimental setup. The experimental has shown that an average increase in power generated was 38.8% compared to a single system of PV panels or a single wind turbine sub-system. Therefore, the potential opportunities for increasing power production in the tropics wheather could be carried out and applied with this model.

Hybrid mass damper: theoretical and experimental power flow analysis

In this paper, a hybrid mass damper (HMD) and its hyperstability thanks to a power flow approach are studied. The HMD proposed combines an active control system with an optimal passive device. The initial passive system is an electromagnetic Tuned Mass Damper (TMD) and the control law is a modified velocity feedback with a phase compensator. The resulting hybrid controller system is theoretically hyperstable and ensures fail-safe behavior. Experiments are performed to validate the numerical simulation and provide good results in terms of vibration attenuations. Both excitation from the bottom in the frequency domain and shock response in the time domain are tested and analyzed. The different power flows in terms of active and reactive powers are estimated numerically and experimentally on the inertial damper (passive and active) and on the HMD. More over, through a mechanical analogy of the proposed system, it is shown that this hybrid device can be seen as an active realization of an inerter based tuned-mass-damper associated with a sky-hook damper. Observations and analysis provide insight into the hyperstable behavior imposed by the specific control law.

Adaptive RBF -SMC control for multi-point mooring system

A stable adaptive control scheme for multi-point mooring system (MPMS) with uncertain dynamics is proposed in this paper. The control scheme is designed by a hybrid controller based on RBF (Radial Basis Function) NN (Neural Network) and SMC (Sliding Mode Control), which learns the MPMS dynamic changes, and the compensation of external disturbances is realized through adaptive RBFNN control. Meanwhile the RBF-SMC control parameters are adapted by the Lyapunov method to minimize squares dynamic positioning (DP) error. The convergence of the hybrid controller is proved theoretically, and the proposed mooring control scheme is applied to the “Kantan3” mooring simulation system. Finally, the simulation results are compared with the traditional PID controller and standard RBF controller to demonstrate the effective mooring positioning performance of the control scheme for the MPMS.

A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear

This paper adopts an intelligent controller based on supervised neural network control for a magnetorheological (MR) damper in an aircraft landing gear. An MR damper is a device capable of adjusting the damping force by changing the magnetic field generated in electric coils. Applying an MR damper to the landing gears of an aircraft could minimize the impact at landing and increase the impact absorption efficiency. Various techniques proposed for controlling the MR damper in aircraft landing gears require information on the damper force or the mass of the aircraft to determine optimal parameters and control commands. This information is obtained by estimation with a model in a practical operating environment, and the accompanying inaccuracies cause performance degradation. Machine learning-based controllers have also been proposed to address model dependency but require a large number of drop test data. Unlike simulations, which can conduct a large number of virtual drop tests, the cost and time are limited in the actual experimental environment. Therefore, a neural network controller with supervised learning is proposed in this paper to simulate the behavior of a proven controller only with system states. The experimental data generated by applying the hybrid controller with the exact mass and force information, which has demonstrated high performance among the existing techniques, are set as the target for supervised learning. To verify the effectiveness of the proposed controller, drop test experiments using the intelligent controller and the hybrid controller with and without exact information about aircraft mass and force are executed. The experimental results from the drop tests of a landing gear show that the proposed controller maintains superior performance to the hybrid controller without using explicit damper models or any information on the aircraft mass or strut force.

Design and Implementation of an Intelligent Blade Pitch Control System and Stability Analysis for a Small Darrieus Vertical-Axis Wind Turbine

A few studies have been conducted recently in order to improve the aerodynamic performance of Darrieus vertical-axis wind turbines with straight blades (H-type VAWTs). The blade pitch angle control is proposed to enhance the performance of H-type VAWTs. This paper aims to investigate the performance of an H-type VAWT in terms of its power output and self-starting capability using an intelligent blade pitch control strategy based on a multi-layer perceptron artificial neural network (MLP-ANN) method. The performance of the proposed blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., a hybrid controller). The dynamics of an H-type VAWT is mathematically modeled in a nonlinear state space for the stability analysis in the sense of Lyapunov. The effectiveness of the proposed pitch control system is validated by building an H-type VAWT prototype model that is extensively tested outdoors under different conditions for both fixed and variable pitch angle configurations. Results demonstrated that the blade-pitching technique enhanced the power output of an H-type VAWT by approximately 22%. The hybrid controller that used a high percentage of the MLP-ANN controller achieved a better control performance by reducing the overshoot of the control response at high rotor speeds.

Real-Time High Performance of Induction Motor Drive Using Hybrid Fuzzy-Sliding Mode Controllers

Obtaining the required performance, stability, and robustness in real-time control of induction motors usually requires the use of complex controllers, however through multiple experimentations, many challenges have arisen from such methods. The complex structure of control methods in real-time applications is usually computationally challenging and energy consuming, hence the need for a simple control strategy to overcome these challenges, in this paper, we focus on designing an advanced hybrid control strategy with a simple design applied to an induction motor. Mainly, the hybrid controller used in this study has the benefits of joining the best performance of both fuzzy logic controller and sliding mode controller, specifically designed to handle each phase separately, the transition phase and the steady phase. A fuzzy controller intervenes as a supervisor in our control structure, more specifically it manages the switch from one type of control to the other taking into account the intervention phase of each type of controller by commanding the rate of both controllers. Control performance analysis was carried out in a real experimental setup to validate the efficiency and robustness of the proposed hybrid controller and confirm its effectiveness in handling the compromise between overshoot and response time.

A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

In this paper, the consensus problem is addressed for multi-agent systems. The dynamics of each agent contain unknown uncertain/nonlinear terms and unknown time delays. A type-3 fuzzy logic system is developed to tackle the effect of unknown dynamics and design a hybrid controller. The policy scheme involves two control signals for the stabilization of the approximation and consensus error of each agent dynamic. To this end, based on the concept of the model predictive control approach, the constrained control laws are designed and updated at each time step. The simulations results portray the error signals. Feasibility, appropriate convergence, and proper transient response are the main merits of the suggested method.

SLIDING MODE BASED PREDICTIVE CONTROLLER OF A SPHEROIDAL UNDERWATER VEHICLE

The following key points are made after reading this research paper: 1) The approach taken in the paper looks interesting and innovative; 2) The problem and solution are well motivated; 3) The design of proposed hybrid controller (Sliding Mode & Model Predictive Controller) is a new and robust technique. Both qualitative and quantitative analysis and stability analysis are discussed in this paper; and 4) The proposed flowchart for MPC looks interesting. MPC tuning is given in a standard and systematic way.