Grey Wolf Optimization Based Tuning of Terminal Sliding Mode Controllers for a Quadrotor

The research on Unmanned Aerial Vehicles (UAV) has intensified considerably thanks to the recent growth in the fields of advanced automatic control, artificial intelligence, and miniaturization. In this paper, a Grey Wolf Optimization (GWO) algorithm is proposed and successfully applied to tune all effective parameters of Fast Terminal Sliding Mode (FTSM) controllers for a quadrotor UAV. A full control scheme is first established to deal with the coupled and underactuated dynamics of the drone. Controllers for altitude, attitude, and position dynamics become separately designed and tuned. To work around the repetitive and time-consuming trial-error-based procedures, all FTSM controllers’ parameters for only altitude and attitude dynamics are systematically tuned thanks to the proposed GWO metaheuristic. Such a hard and complex tuning task is formulated as a nonlinear optimization problem under operational constraints. The performance and robustness of the GWO-based control strategy are compared to those based on homologous metaheuristics and standard terminal sliding mode approaches. Numerical simulations are carried out to show the effectiveness and superiority of the proposed GWO-tuned FTSM controllers for the altitude and attitude dynamics’ stabilization and tracking. Nonparametric statistical analyses revealed that the GWO algorithm is more competitive with high performance in terms of fastness, non-premature convergence, and research exploration/ exploitation capabilities.     

Robust RST control design based on Multi-Objective Particle Swarm Optimization approach

In this paper, a novel method for the digital two-Degrees-Of-Freedom (2DOF) controller design, called canonical RST structure, is proposed and successfully implemented based on a Multi-Objective Particle Swarm Optimization (MOPSO) approach. This is a polynomial control structure allowing independently the regulation and the tracking of discrete-time systems. An application to the variable speed control of an electrical DC Drive is investigated. The RST design and tuning problem is formulated as a multi-objective optimization problem. The proposed MOPSO algorithm which is based on the Pareto dominance is used to identify the non-dominated solutions. This approach used the leader selection strategy that is called a geographically-based system. In addition, the adaptive grid method is used to produce well-distributed Pareto fronts in the multi-objective formalism. The well known NSGA-II and the proposed MOPSO algorithms are evaluated and compared with each other in terms of several performance metrics in order to show the superiority and the effectiveness of the proposed method. Simulation results demonstrate the advantages of the MOPSO-tuned RST control structure in terms of performance and robustness.

     

An efficient mesh router nodes placement in wireless mesh networks based on moth-flame optimization algorithm

One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi-objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.     

Particle swarm optimization-based fixed-structure <Emphasis Type="Italic">ℋ</Emphasis><Subscript>∞</Subscript> control design

In this paper, a new robust fixed-structure ℋ _∞ controller design based on the Particle Swarm Optimization (PSO) technique is proposed. The optimization-based structured synthesis problem is formulated and solved by a constrained PSO algorithm. In the proposed approach, the ℋ _∞ controller’s structure is selectable. PI and PID controller structures are especially adopted. The case study of an electrical DC drive benchmark is adopted to illustrate the efficiency and viability of the proposed control approach. A comparison to another similar evolutionary algorithm, such as Genetic Algorithm Optimization (GAO), shows the superiority of the PSO-based method to solve the formulated optimization problem. Simulations and experimental results show the advantages of simple structure, lower order and robustness of the proposed controller.     

Fractional-Order Control of a Wind Turbine Using Manta Ray Foraging Optimization

In this research paper, an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator (DFIG) based wind energy system has been proposed. The proposed strategy used the robust Fractional-Order (FO) Proportional-Integral (PI) control technique. The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits. It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness. The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization (MRFO) algorithm. During the optimization process, the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized. To prove the superiority of the MRFO algorithm, an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved. The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.     

Drying characteristics and sorption isotherms of silverside fish (Atherina)

The aim of this work was to report drying characteristics and sorption isotherms of silverside fish. The sorption isotherm was determined at four temperature levels 40, 50, 60 and 70 °C and at water activities, ranging from 0.058 to 0.89, using the static gravimetric method. Five sorption models were fitted with the desorption data generated from the gravimetric method. It was found that the Peleg model suitably represents the sorption experimental data in the mentioned investigated ranges of temperature and water activities. Sorption isosteric heat was determined from sorption data using the Clausius–Clapeyron equation. The relationship between the net isosteric heat of sorption and the moisture content was best expressed by the Tsami’s equation. The drying characteristic curve and the drying rate expression of silverside fish have been established from experimental convective drying kinetics.