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The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm.

An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function.

Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials.

The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

The purpose of this paper is to present fault tolerant control of a quadrotor based on the enhanced proportional integral derivative (PID) structure in the presence of one or more actuator faults.

Mathematical model of the quadrotor is derived by parameter identification of the system for the simulation of the UAV dynamics and flight control in MATLAB/Simulink. An improved PID structure is used to provide the stability of the nonlinear quadcopter system both for attitude and path control of the system. The results of the healty system and the faulty system are given in simulations, together with motor dynamics.

In this study, actuator faults are considered to show that a robust controller design handles the loss of effectiveness in motors up to some extent. For the loss of control effectiveness of 20 per cent in first and third motors, psi state follows the reference with steady state error, and it does not go unstable. Motor 1 and Motor 3 respond to given motor fault quickly. When it comes to one actuator fault, steady state errors remain in some states, but the system does not become unstable.

In this paper, an enhanced PID controller is proposed to keep the quadrotor stable in case of actuator faults. Proposed method demonstrates the effectiveness of the control system against motor faults.

Nowadays, in order to improve current applications, industry incorporates to their solution approaches artificial intelligence techniques and methodologies like Fuzzy Logic, neural networks and/or genetic algorithms (GA). Artificial intelligence techniques complement classical methodologies and include concepts that simulate the way humans solve problems or how processes work in nature. In this work, the Fuzzy Logic system cancels the effects of load perturbances in an energy plant, by implementing a secondary controller which complements the main controller. The purpose of this paper is to use GA to tune this new secondary controller. The authors particularize the proposal for three specific applications: control the angular speed and position of a Direct Current (DC) motor and control the output voltage of a DC/DC buck converter.

The authors use GA for tuning a Proportional-Integral Fuzzy Controller (PI-Fuzzy). The proposal defines a new objective function in comparison with literature approaches. The main key in the new objective function is combining the best features of Integral Square Error (ISE) function and taking out the overshoot response.

In order to demonstrate the proposed methodology based on GA tuning a PI-Fuzzy, the authors apply the literature benchmark to the solution. The results are compared with the following techniques: Robust control, continuous PID control, discrete PID control, Optimal Control, Fuzzy Control and Artificial Neural Network based control. Comparisons are presented in terms of setting time and overshot.

Results demonstrate that ISE or integral of absolute value of error function do not provide the desired response. Achieved results demonstrate the usefulness of the proposal to eliminate the overshoot of the traditional behaviour without lost any of the main features of the literature methodologies.

Generally, humanoid robots usually suffer significant impact force when walking or running in a non-predefined environment that could easily damage the actuators due to high stiffness. In recent years, the utilization of passive compliant series elastic actuators (SEA) for driving humanoid's joints has proved the capability in many aspects so far. However, despite being widely applied in the biped robot research field, the stable control problem for a humanoid powered by the SEAs, especially in the walking process, is still a challenge. This paper proposes a model reference adaptive control (MRAC) combined with the back-stepping algorithm to deal with the parameter uncertainties in a humanoid's lower limb driven by the SEA system. This is an extension of our previous research (Lanh et al., 2021).

Firstly, a dynamic model of SEA is obtained. Secondly, since there are unknown and uncertain parameters in the SEA model, a Model Reference Adaptive Controller (MRAC) is employed to guarantee the robust performance of the humanoid's lower limb. Finally, an experiment is carried out to evaluate the effectiveness of the proposed controller and the SEA mechanism.

This paper proposes an effective control algorithm that can be widely applied for the humanoid-SEA system. Besides, the effect of the coefficients in the control law is analyzed to further improve the response's quality.

Even though the simulation shows good results with stable system response, the practical experiment has not been implemented to fully evaluate the quality of the controller.

The MRAC is applied to control the humanoid's lower limb and the back-stepping process is utilized to combine with an external SEA system but still maintain stabilization. The simplified model of the lower-limb system proposed in the paper is proven to be appropriate and can be taken for further research in the future.

The aim of this paper is to design a nonlinear disturbance observer-based control (DOBC) method obtained by patching a control method developed using a robust adaptive technique and a DO.

For designing a DOBC, initially a class of nonlinear system is considered with an external disturbance. First, a DO is designed to estimate the external disturbances. This estimate is combined with the controller to reject the disturbances and obtain the desired control objective. For designing a controller, the robust sliding mode control theory is used. Furthermore, instead of using a constant switching gain, an adaptive gain tuning criterion is designed using Lyapunov candidate function. To investigate the stability and effectiveness of the developed DOBC, stability analysis and simulation study are presented.

The major findings of this paper include the criteria of designing the robust adaptive control parameters and investigating the disturbance rejection when robust adaptive control based DOBC is developed.

In practice, the flight of quadrotor is affected by different kind of external disturbances, thus leading to the change in dynamics. Hence, it is necessary to design DOBCs based on robust adaptive controllers such that the quadrotor model adapts to the change in dynamics, as well as nullify the effect of disturbances.

Designing DOBCs based on robust control method is a common practice; however, the robust adaptive control method is rarely developed. This paper contributes in the domain of DOBC based on robust adaptive control methods such that the behavior of controller varies with the change in dynamics occurring due to external disturbances.

The purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.

Control of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.

Robustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.

To the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.

The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics.

Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter.

Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m²]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x-axis, 3.4 to 0.14 [m] for y-axis and 3.7 to 0.2 [m] for z-axis, respectively.

The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.

A novel proportional integral derivative-extended state disturbance observer-based control (PID-ESDOBC) algorithm is proposed to solve the nonlinear hydrodynamics, parameters perturbation and external disturbance in yaw control of remote operated vehicles (ROVs). The effectiveness of PID-ESDOBC is verified through the experiments and the results indicate that the proposed method can effectively track the desired attitude and attenuate the external disturbance.

This study fully investigates the hydrodynamic model of ROVs and proposes a control-oriented hydrodynamic state space model of ROVs in yaw direction. Based on this, this study designs the PID-ESDOBC controller, whose stability is also analyzed through Kharitonov theorem and Mikhailov criterion. The conventional proportional-integral-derivative (PID) and active disturbance rejection control (ADRC) are compared with our method in our experiment.

In this paper, the authors address the nonlinear hydrodynamics, parameters perturbation and external disturbance problems of ROVs with multi-vector propulsion by using PID-ESDOBC control scheme. The advantage is that the nonlinearities and external disturbance can be estimated accurately and attenuate promptly without requiring the precise model of ROVs. Compared to PID and ADRC, both in overshoot and settling time, the improvement is 2X on average compared to conventional PID and ADRC in the pool experiment.

The delays occurred in the control process can be solved in the future work.

The attitude control is a kernel problem for ROVs. A precise kinematic and dynamic model for ROVs and an advanced control system are the key factors to obtain the better maneuverability in attitude control. The PID-ESDOBC method proposed in this paper can effectively attenuate nonlinearities and external disturbance, which leads to a quick response and good tracking performance to baseline controller.

The PID-ESDOBC algorithm proposed in this paper can be ensure the precise and fast maneuverability in attitude control of ROVs or other underwater equipment operating in the complex underwater environment. In this way, the robot can better perform undersea work and tasks.

The dynamics of the ROV and the nominal control model are investigated. A novel control scheme PID-ESDOBC is proposed to achieve rapidly yaw attitude tracking and effectively reject the external disturbance. The robustness of the controller is also analyzed which provides parameters tuning guidelines. The effectiveness of the proposed controller is experimental verified with a comparison by conventional PID, ADRC.

This study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system.

This work was adopted by IChimp to optimize proportional integral derivative (PID) controller parameters used for the LFC of a two area interconnected thermal system.

The supremacy of proposed IChimp tuned PID controller over Chimp optimization, direct synthesis-based PID controller, internal model controller tuned PID controller and recent algorithm based PID controller was demonstrated.

IChimp has good convergence and better search ability. The IChimp optimized PID controller is the proposed controlling method, which ensured better performance in terms of converging behaviour, optimizing controller gains and steady-state response.

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.