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The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.

The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.

This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.

The conventional real time issues such as nonlinearity characteristics, poor controllability and stability.

It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

This study aims to minimize the risk of costly failures of flight tests during the path tracking control design, and a noble approach has been proposed in this study to put the whole vehicle-in-the simulation loop. Working with the real system is essential for developing intelligent and data-driven controllers for multirotor drones which needs learning the drones' nonlinear complicated dynamics. The vehicle-in-the-loop (VIL) platform developed in this paper is a safe and effective solution to deal with this problem.

To avoid risky flight test during controller design, the multirotor is hinged to a shaft that allows the multirotor's angular motion but restricts translational motion. The test-bed includes the real system attitude dynamics and the simulation of the position dynamics to model the complete flight based on real-time reactions of the vehicle. For the authors' case study, a hexacopter angular motion provides the real-time attitude data in translational motion simulation loop. To test the set-up, a proportional-integral-derivative (PID) and a brain emotional learning-based intelligent controller (BELBIC) is implemented for tracking of circle and 8-shape flight trajectories.

The results show that the platform helps the intelligent controller to learn the system dynamics without worrying about the failure in the early stages of the design and in the real-world flight test. Although the hexacopter translational dynamics is modeled in simulation, the authors still have highly accurate attitude dynamics matching the requirement of the control loop design. The comparison of the two controllers also shows that the performance of BELBIC is better than PID in this test.

The research background is reviewed in the introduction section. The other sections are originally developed in this paper.

The purpose of this paper is to propose a buoyancy-gravity adjustment device and a fuzzy intelligent controller for the depth control of a storage tank in-service inspection robot.

The structure of the robot is first designed based on the construction of the bottom of a crude oil tank and explosion-proof requirements. The buoyancy-gravity adjustment system is used to control the vertical movement of the robot. The motion analysis of the robot indicates that the diving or rising process is influenced by hydrodynamic force and umbilical cord tension. Considering the nonlinear model in-depth control, a fuzzy intelligent controller is proposed to address the depth control problem. The primary fuzzy controller is used to compensate for initial error with fast response. The secondary fuzzy controller is activated by an intelligent switch to eliminate the steady error.

The proposed fuzzy controller can better solve the complicated hydrodynamic problem of the coupling of umbilical cord and the robot during depth control by classifying the error values of depth, velocity and acceleration.

The buoyancy-gravity adjustment device and the depth control system of the robot can move through the heating coils by safe and accurate diving or rising.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

This paper aims to design a global controller that is operational throughout all flight modes and less dependent on an accurate model.

By adopting the interconnection and damping assignment passivity-based control (IDA-PBC) technology and compensating extra inputs for handling the unknown dynamics and time-varying disturbances, a model-free control (MFC)-based global controller is proposed.

Test results indicate that the designed controllers are more suitable for actual flight as they have smaller position tracking errors and energy consumption in all flight phases than the excellent model-free controller intelligent-PID.

The designed global controller, which works in all flight modes without adjusting its structure and parameters, can realize a stable and accurate tracking control of a tail-sitter and improve the resistance to unknown disturbances and model uncertainties.

The newly-designed controller is considered as an enhanced version of the traditional MFC. It further improves the control effect by using the poorly known dynamics of the system and choosing the IDA-PBC as the control auxiliary input. This method eliminates the unnecessary dynamics to continuously stabilize the vehicle with suitable energy consumption covering its entire flight envelope.

In this work, generation control of an isolated small hydro plant (SHP) is demonstrated by applying optimal controllers in speed governor and hydraulic turbine system. A comparative analysis of application of fuzzy PI (FPI) and PID controller is conferred for generation control (both power and terminal voltage) of an SHP. The controllers are designed optimally by using crow search algorithm (CSA) and novel hybrid differential evolution crow search algorithm (DECSA). The purpose of this paper is to settle the voltage and real power to improve the quality of the power.

In this work, the controllers (PID and FPI) are implemented in speed governor and excitation system of SHP to regulate power and terminal voltage. Differential evolution and CSA are hybridized to enhance the performance of controller to refurbish the power and terminal voltage of SHP.

The proposed DECSA algorithm is applied to solve ten benchmark functions, and the effectiveness of DECSA algorithm over CSA and DE is demonstrated in terms of best value, mean and standard deviation. CSA and DECSA algorithms optimized controllers (PID and FPI) are used to design SHP with the capability to contribute power and voltage of better quality. The comparative analysis to substantiate the competence of DECSA algorithm and FPI controller is demonstrated in terms of statistical measures of power and voltage of SHP. Robustness analysis is performed by varying all system parameters to prove the effectiveness of the proposed controller.

The proposed algorithm and FPI controller are applied individually to improve the quality of the power of SHP. DE, CSA and DECSA algorithms are implemented to solve benchmark equations. The solutions of all benchmark equations contributed by DECSA algorithm is converged rapidly and having minimum statistical measures as compared to DE and CSA algorithms. The DECSA algorithm and FPI controller are proposed with superior competence to enhance the generator performances by conceding undershoot, overshoot and settling time of power and terminal voltage. DECSA-based FPI controller contributes a noticeable improvement of the performances over other approaches.

The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor.

A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints.

The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller.

This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance.

First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, Gravitational Search Algorithm has been used.

The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects caused by external disturbances. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for automatic take-off, hovering and landing mission of quadrotor UAV even in the presence of unknown perturbations.

This paper propose a new robust control design approach which incorporates the backstepping control with fuzzy system for quadrotor UAV with inherent time-varying disturbance. The originality of this work relies on the technique to compensate the disturbances acting on the quadrotor UAV. In this new approach, the fuzzy system is introduced as an auxiliary control effort to compensate the effect of disturbances. Because the proposed control technique has the capability of robustness against disturbance, thus, it is also suitable to be applied for a broad class of uncertain nonlinear systems.

The purpose of this paper is to develop a novel autonomous in‐pipe robot to perform the preventive point reparation for long‐distance offshore oil pipelines.

The autonomous in‐pipe robot performs online ultrasonic inspection for pipe wall thickness, and the original inspection data are stored in large capacity hard disk. Through the offline data analysis by the data analysts and the software tool, the pipeline health status is known. If server defects lie there, the in‐pipe robot is introduced into the pipeline once more to indicate the defect's location to the maintenance ship.

The laboratory tests and the field tests prove the feasibility and validity of the developed autonomous in‐pipe robot. Furthermore, the application of intelligent control techniques ensures the mission completion by the autonomous in‐pipe robot, which worked in the awful pipeline environment.

The developed autonomous in‐pipe robot helps eliminate lost production costs and pipeline downtime caused by leakages and guarantees the safe run of offshore oil pipelines.

For the application of the autonomous in‐pipe robot, there are no special requirements for maintained pipelines themselves, so it is applicable to the point reparation for most long‐distance welded offshore pipelines.

The two-tank level control system is one of the real-world's second-order system (SOS) widely used as the process control in industries. It is normally operated under the Proportional integral and derivative (PID) feedback control loop. The conventional PID controller performance degrades significantly in the existence of modeling uncertainty, faults and process disturbances. To overcome these limitations, the paper suggests an interval type-2 fuzzy logic based Tilt-Integral-Derivative Controller (IT2TID) which is modified structure of PID controller.

In this paper, an optimization IT2TID controller design for the conical, noninteracting level control system is presented. Regarding to modern optimization context, the flower pollination algorithm (FPA), among the most coherent population-based metaheuristic optimization techniques is applied to search for the appropriate IT2FTID's and IT2FPID's parameters. The proposed FPA-based IT2FTID/IT2FPID design framework is considered as the constrained optimization problem. System responses obtained by the IT2FTID controller designed by the FPA will be differentiated with those acquired by the IT2FPID controller also designed by the FPA.

As the results, it was found that the IT2FTID can provide the very satisfactory tracking and regulating responses of the conical two-tank noninteracting level control system superior as compared to IT2FPID significantly under the actuator and system component faults. Additionally, statistical Z-test carried out for both the controllers and an effectiveness of the proposed IT2FTID controller is proven as compared to IT2FPID and existing passive fault tolerant controller in recent literature.

Application of new metaheuristic algorithm to optimize interval type-2 fractional order TID controller for nonlinear level control system with two type of faults. Also, proposed method will compare with other method and statistical analysis will be presented.