scholarly journals Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 22
Author(s):  
Josenalde Oliveira ◽  
Paulo Moura Oliveira ◽  
José Boaventura-Cunha ◽  
Tatiana Pinho
Keyword(s):  
Control Systems ◽  
Sliding Mode ◽  
Tracking Error ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Linear Control Systems ◽  
Classical Particle ◽  
Grey Wolf Optimizer ◽  
Local Optima ◽  
Chattering Effect

The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

DESIGN AND ANALYSIS OF SUPER TWISTING SLIDING MODE CONTROL FOR MACHINE TOOLS

2016 ◽  
Vol 78 (10-3) ◽  
Author(s):  
Chiew Tsung Heng ◽  
Zamberi Jamaludin ◽  
Ahmad Yusairi Bani Hashim ◽  
Nur Aidawaty Rafan ◽  
Lokman Abdullah ◽  
...  
Keyword(s):  
Machine Tools ◽  
Sliding Mode ◽  
Tracking Error ◽  
Control Algorithms ◽  
Sliding Mode Controller ◽  
Control Input ◽  
Positioning System ◽  
Direct Drive ◽  
Chattering Effect

High demands of precision on machine tools are hardly cope by using existing classic control algorithms. This paper focuses on the design, analysis and validation of a super twisting sliding mode controller on a single axis direct drive positioning system for improved tracking performances. The second order positioning system parameters were determined using input and output of measured data. Effects of two gain parameters in control algorithm on the quality of the control input and tracking error were analysed experimentally. The gain parameters were selected based on magnitude reduction in chattering during practical application. The performance of tuned super twisting sliding mode controller was compared with a traditional sliding mode controller using sigmoid-like function. Results showed that super twisting sliding mode controller reduced the chattering effect and improved the performance of system in terms of tracking error by 16.5%.  

Air Hybrid Engine Torque Control Using Adaptive Sliding Mode Control

2010 ◽  
Author(s):  
Amir Fazeli ◽  
Meysar Zeinali ◽  
Amir Khajepour ◽  
Mohammad Pournazeri
Keyword(s):  
Sliding Mode ◽  
Tracking Error ◽  
Mean Value ◽  
Torque Control ◽  
Disturbance Attenuation ◽  
Adaptive Sliding Mode Control ◽  
Engine Torque ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Chattering Effect

In this work, a new air hybrid engine configuration is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. A Mean Value Model (MVM) of the engine is developed at braking mode and a new Adaptive Sliding Mode Controller (ASMC), recently proposed in the literature, is applied to control the engine torque at this mode. The results show that the controller performs remarkably well in terms of the robustness, tracking error convergence and disturbance attenuation. Chattering effect is also removed by utilizing the ASMC scheme.

scholarly journals Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

2021 ◽  
Vol 13 (24) ◽  
pp. 13709
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Dhanasekar Ravikumar ◽  
Vijayaraja Loganathan ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Objective Functions ◽  
Distributed Generations ◽  
Multi Objective ◽  
Metaheuristic Optimization

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

Fractional Vector-Order h-Realisation of the Impulse Response Function

2020 ◽  
Vol 14 (2) ◽  
pp. 108-113
Author(s):  
Ewa Pawłuszewicz
Keyword(s):  
Control Systems ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Linear Control ◽  
Linear Control Systems

AbstractThe problem of realisation of linear control systems with the h–difference of Caputo-, Riemann–Liouville- and Grünwald–Letnikov-type fractional vector-order operators is studied. The problem of existing minimal realisation is discussed.

scholarly journals Robust Tracker of Hybrid Microgrids by the Invariant-Ellipsoid Set

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1794
Author(s):  
Hilmy Awad ◽  
Ehab H. E. Bayoumi ◽  
Hisham M. Soliman ◽  
Michele De Santis
Keyword(s):  
Sliding Mode ◽  
Tracking Error ◽  
Nonlinear Dynamical System ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Nonlinear Dynamical ◽  
Load Variations ◽  
Invariant Ellipsoid ◽  
Steady State Responses ◽  
Linearized Model

This paper introduces a new ellipsoidal-based tracker design to control a grid-connected hybrid direct current/alternating current (DC/AC) microgrid (MG). The proposed controller is robust against both parameters and load variations. The studied hybrid MG is modelled as a nonlinear dynamical system. A linearized model around an operating point is developed. The parameter changes are modelled as norm-bounded uncertainties. We apply the new extended version of the attractive (or invariant) ellipsoid for this tracking problem. Convex optimization is used to obtain the region’s minimal size where the tracking error between the state trajectories and the reference states converges. The sufficient conditions for stability are derived and solved based on linear matrix inequalities (LMIs). The proposed controller’s validity is shown via simulating the hybrid MG with various operational scenarios. In each scenario, the performance of the controller is compared with a recently proposed sliding mode controller. The comparison clearly illustrates the superiority of the developed controller in terms of transient and steady-state responses.

scholarly journals Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

2020 ◽  
Vol 10 (1) ◽  
pp. 396-407
Author(s):  
Fatiha Loucif ◽  
Sihem Kechida
Keyword(s):  
Sliding Mode ◽  
Robot Manipulator ◽  
Absolute Error ◽  
Grey Wolf Optimizer ◽  
Optimal Parameters ◽  
Error Criterion ◽  
Trajectory Tracking Control ◽  
Integral Time ◽  
Sine Cosine Algorithm ◽  
Antlion Optimization

AbstractIn this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robot manipulator system, minimizing the integral time absolute error criterion (ITAE) and the integral time square error criterion (ISTE). The robot manipulator is modeled in Simulink and the control is implemented using the MATLAB environment. The obtained simulation results prove the robustness of ALO in comparison with other algorithms.

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