Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial Intelligence and Soft Computing Researc

Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm

In this paper, design of proportional- derivative (PD) controller, pseudo-derivative-feedback (PDF) controller and PDF with feedforward (PDFF) controller for magnetic suspending system have been presented. Tuning of the above controllers is achieved based on Bat algorithm (BA). BA is a recent bio-inspired optimization method for solving global optimization problems, which mimic the behavior of micro-bats. The weak point of the standard BA is the exploration ability due to directional echolocation and the difficulty in escaping from local optimum. The new improved BA enhances the convergence rate while obtaining optimal solution by introducing three adaptations namely modified frequency factor, adding inertia weight and modified local search. The feasibility of the proposed algorithm is examined by applied to several benchmark problems that are adopted from literature. The results of IBA are compared with the results collected from standard BA and the well-known particle swarm optimization (PSO) algorithm. The simulation results show that the IBA has a higher accuracy and searching speed than the approaches considered. Finally, the tuning of the three controlling schemes using the proposed algorithm, standard BA and PSO algorithms reveals that IBA has a higher performance compared with the other optimization algorithm

Design and Optimization of PID Controller using Various Algorithms for Micro-Robotics System

Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms:  Sparrow Search Algorithm (SSA), Flower Pollination Algorithm (FPA), Slime Mould Algorithm (SMA), Marine Predator Algorithm (MPA), and Multi-Verse Optimizer (MVO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model, and LABVIEW software was used to run the experimental tests. It is observed that the MPA technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the SSA approach reduces the settling error by 50% compared to former experiments. The results indicate that SSA is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters

Novel Robust Control of a 7-DOF Exoskeleton Robot

This paper proposes a novel robust control method for the control of a 7-DOF exoskeleton robot. The external disturbances and unknown dynamics in the form of friction forces, different upper-limb\u27s mass, backlash, and input saturation make robot unstable, which prevents the robot from correctly following the defined path. A new fractional sliding mode controller (NFSMC) is designed, which is robust against unknown dynamic and external disturbances. Fractional PID controller (FPID) has high trajectory tracking, but it is not robust against external disturbances. Therefore, by combining NFSMC and FPID controllers, a new compound fractional PID sliding mode controller (NCFPIDSMC) is proposed, which benefits high trajectory tracking of FPID and robustness of NFSMC. The stability of the proposed control method is verified by Lyapunov theory. A random noise is applied in order to confirm the robustness of the proposed control method

Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure

Intelligent position control for intelligent Pneumatic actuator with ball-beam (IPABB) system

A pneumatic actuator system is considered extremely nonlinear, making accurate position control of this actuator difficult to obtain. In this article, a novel cascade fractional-order PID (CFOPID) controller for the intelligent pneumatic actuator (IPA) positioning system utilizing particle swarm optimization (PSO) is presented. The pneumatic system was modeled using the system identification (SI) technique. To demonstrate the effectiveness of the CFOPID controller, a comparison to the FOPID controller is performed based on the rise, settling, and peak times, peak overshoot, and integral of square error (ISE). From the results obtained, the proposed CFOPID controller provides superior control over the FOPID controller. For the application of the position controller, the proposed system incorporates an intelligent pneumatic actuated ball and beam (IPABB) system. The mathematical model of the system was developed and validated through a simulation utilizing a PID (outer loop) and CFOPID controller (inner loop). The suggested controller’s accuracy and robustness have been studied by a comparative examination of the results obtained utilizing the proposed and other prior controllers on the same system. The results indicate that the intelligent pneumatic actuator, when coupled with a CFOPID controller, is capable of controlling the positioning of the ball and beam system

Optimization of non-linear control aerodynamic systems using metaheuristic algorithm Optimisation des commandes non linéaires des systèmes aérodynamiques par les méthodes méta-heuristiques

This thesis is part of the project "modelisation and control dynamic systems" carried by the laboratory of LMSE. This project aims to develop and optimize new control approaches for the UAV quadrotor tracking control. This thesis consisted of the modelling of the quadrotor, and then analysing, designing and implementing new optimal control strategies based on the model-free concept. In this context, the aim of the thesis is to propose new control strategies based on the model-free concept. The proposed strategies help to compensate the disturbances and model uncertainties. Regarding our work, we have proposed different control techniques for quadrotor control. First, an optimal model-free backstepping control law applied to a quadrotor UAV has been proposed. In addition to this work, the dynamic system has been estimated through a new proposed fuzzy strategy and merged with the BC under the model-free concept. Finally, an optimal fuzzy model-free control has been designed based on decentralized fuzzy control. The objective of these control strategies is to achieve the best tracking with unknown nonlinear dynamics and external disturbances. These proposed approaches are validated through analytical and experimental procedures and the effectiveness checked and compared with regard to the related controllers in the presence of disturbances and model uncertainties