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This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor.

The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations.

In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller’s performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor.

The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor.

This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online.

This paper aims to present a new approach, called hybrid model reference adaptive controller or H-MRAC, for the hybrid controller (proportional-integral-derivative [PID + MRAC]) that will be used to control the position of a pneumatic manipulator.

It was developed a McKibben muscle using nautical mesh, latex and high-density polyethene connectors and it was constructed an elbow manipulator with two degrees of freedom, driven by these muscles. Then it was presented the H-MRAC control law based on the phenomenological characteristics of the plant, aiming at fast response and low damping. Lyapunov's theory was used as the project methodology, which ensures asymptotic stability for the control system.

It was developed a precise control system for a pneumatic manipulator and the results were compared to previous research.

In collaborative robotics, human and machine occupy the same workspace. This research promotes the development of safer and more complacent mechatronic systems in the event of collisions.

As a practical implication, the research allows the substitution of electric motors by McKibben muscles in industrial robots with high accuracy.

The pneumatic manipulator will make the human-robot physical interaction safer as it can prevent catastrophic collisions causing victims or equipment breakdown.

When compared to results in the literature, the present research showed a 37.51% and 36.74% lower global error in position tracking than MRAC and Adaptive proportional-integral-derivative (A-PID), respectively, validating its effectiveness.

In the high-altitude, high-voltage electromagnetic interference operation environment, due to the parameters perturbation for robot control model caused by uncertainties and disturbances, and with the poor effective of the conventional proportional–integral–derivative (PID) control to parameters perturbation system, the mathematical model of power cable live operation robot joint PID closed-loop control system is established.

The corresponding joint motion robust PID control method is also proposed based on Kharitonov theory, the system robust stability conditions including the sufficient and necessary conditions are deduced and obtained and the solving process of robust PID control parameters stability region is provided.

Finally, the simulation research on robot joint motion PID control system is also launched in MATLAB environment based on Kharitonov theory. The results show that the conventional PID control obtains better control effect only to nominal model but is ineffective to parameter perturbation system, while robust PID obtains sound control effect to parameter perturbation system. Compared with H8 robust PID, the Kharitonov robust PID has better control effect which meet the system design requirements of joint motor quickly response, high tracking accuracy and sound stability. Finally, the validity and engineering practicability are verified by 220-kV living replacing damper operation experiment.

This paper has described the development of a damper replacement power cable live maintenance robot experimental prototype, which greatly improves operation efficiency and deals with the safety problem of operation in a high-voltage environment. A general manipulator motion control model of the power cable robot is established; the Kharitonov theory-based parameter perturbation robust motion control method of damper replacement robot is also obtained. Through the simulation comparison, it is verified that the Kharitonov control has more superiority for dealing with the parameter perturbation systems under the premise of ensuring the stability motion. The field experiment has further confirmed the engineering practicability.

The purpose of this paper is to investigate an intelligent control for water hydraulic position servo system which is intent to be used in remote control robot for fusion reactor. The dynamic model of water hydraulic servo control system is built and proportional–integral–derivative (PID) controller is used.

PID control is the most common control algorithm used in industry and has been a conventional tool used to operate closed-loop control system; however, it is very difficult to achieve high accuracy and fast response by using the traditional way to tune its perimeters. To improve the control performance, optimization algorithm can be applied to search the best parameters of PID. This paper presents a search algorithm using particle swarm with H₂ control standards objective function to optimize PID parameters.

By comparing simulation and mock-up experiments’ results from different control methods, the particle swarm optimization algorithm presents better performance and is more effective for tuning PID parameters.

This paper presents an effective way to ensure safety and efficiency for remote handling maintenances of China Fusion Engineering Test Reactor.

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1.

The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

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.

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 purpose of this study is to design and manufacture a new remote center of motion (RCM) mechanism for use in laparoscopic surgical operations. In addition, obtaining the forward and inverse kinematic equations of the RCM mechanism and performing real-time position control with the Proportional–Integral–Derivative (PID) control method.

At the design stage, it is benefited from similar triangle rule. To obtain the kinematic equations in a simple way and facilitate control, two-fold displacement ratio is provided between the limbs where linear motion occurs. The rotation and displacement amounts required to move at the RCM point have been calculated by using the kinematic equations of the mechanism. Limb dimensions and motion limits are determined in the manner to avoid singularities and collisions. The x, y and z coordinates of the end effector have been defined as the reference point. Control of the mechanism was provided by PID control. To generate the user interface and control algorithm, MATLAB/Simulink real-time toolbox has been used. Four reference points were determined, control was performed and position error values were examined. MF634 Humusoft data acquisition card has been preferred to collect data from encoders.

A novel RCM mechanism has been designed and manufactured. Kinematic equations of this mechanism have been obtained. Position control of the cannula tip has been performed using PID control method for four different reference points. After settlement, maximum position error has been observed as 0.45 mm.

Structure of the designed mechanism is quite simple. Thus, costs are quite low. The operation area of the operator is widened by hanging the mechanism from the ceiling, so operational capability of health personnel is increasing. It helps to decrease the operation time and increase the success of the operation.

With this study, it is aimed to contribute to the literature by designing a new RCM mechanism. The rotation of the mechanism around the RCM point is provided by only one rotary motor, and the displacement of the RCM point in the vertical axis is provided by only one linear motor. The mechanism is also a surgical robot. The designed system is suitable for use in robot-assisted laparoscopic surgery in terms of maneuverability.

This paper aims to present a nonlinear mathematical model of a small-scale turbojet aeroengine and also a speed controller design that is conducted for the constructed nonlinear mathematical model.

In the nonlinear mathematical model of the turbojet engine, temperature, rotational speed, mass flow, pressure and other parameters are generated using thermodynamic equations (e.g. mass, energy and momentum conservation laws) and some algebraic equations. In calculation of the performance parameters, adaptive neuro fuzzy inference system (ANFIS) method is preferred in related components. All calculated values from the mathematical model are then compared with the cycle data of the turbojet engine. Because of the single variable control need and effect of noise factor, modified proportional–integral–derivative (PID) controller is treated for speed control. For whole operation envelope, various PID structures are designed individually, according to the operating points. These controller structures are then combined via gain-scheduling approach and integrated to the nonlinear engine model. Simulations are performed on MATLAB/Simulink environment for design and off-design operating points between idle to maximum thrust levels.

The cascade structure (proposed nonlinear engine aero-thermal model and speed controller) is simulated and tested at various operating points of the engine and for different transient conditions. Simulation results show that the transitions between the operating points are found successfully. Furthermore, the controller is effective for steady-state load changes. It is suggested to be used in real-time engine applications.

Because of limited data, only speed control is treated and simulated.

It can be used as an application in the industry easily.

First point of novelty in the paper is in calculation of the performance parameters of compressor and turbine components. ANFIS method is preferred to predict performance parameters in related components. Second novelty in the paper can be seen in speed controller design part. Because of the single variable control need and effect of noise factor, modified PID is treated.

The position/force control of the robot needs the parameters of the impedance model and generates the desired position from the contact force in the environment. When the environment is unknown, learning algorithms are needed to estimate both the desired force and the parameters of the impedance model.

In this paper, the authors use reinforcement learning to learn only the desired force, then they use proportional-integral-derivative admittance control to generate the desired position. The results of the experiment are presented to verify their approach.

The position error is minimized without knowing the environment or the impedance parameters. Another advantage of this simplified position/force control is that the transformation of the Cartesian space to the joint space by inverse kinematics is avoided by the feedback control mechanism. The stability of the closed-loop system is proven.

The position error is minimized without knowing the environment or the impedance parameters. The stability of the closed-loop system is proven.