An error compensation controller for milling robots

Simple conventional control methods, such as PD and PID controllers, are widely used in industrial applications. Such controllers exhibit poor performance when applied to systems containing nonlinearities arising from unknown deadzones. In this article, we propose a novel fuzzy logic-based precompensation approach for controlling systems with deadzones. The control structure consists of a fuzzy logic-based precompensator followed by a conventional PD controller. Our proposed control scheme shows superior transient and steady-state performance compared to conventional PD and PID controllers. In addition, the scheme is robust to variations in deadzone nonlinearities, as well as the steady-state gain of the plant. We illustrate the effectiveness of our scheme using computer simulation examples.

In this study, pitch angle control is employed to maintain the output power and the rotor speed of the wind turbine, whenever the wind speed is higher than the base wind speed. Blade pitch is a key part of a wind turbine and it is a complex nonlinear system interrupted by many uncertainties. The proposed controller linear active disturbance rejection control is utilized to control the output power and rotor speed of the wind turbine. LADRC is based on the extended state observer and a proportional derivative control law. The distinguished feature of the LADRC is, that it can estimate the internal and external disturbances and to compensate for it in real time. The other advantage of the LADRC controller is that only two parameters need to be tuned: observer bandwidth and controller bandwidth. The purpose of the proposed pitch angle Control is to regulate the pitch angle of the wind turbine with respect to wind speed to improve the efficiency of the wind turbine and overcome the safety issues. To validate the effectiveness and superiority of the proposed controller, the results are compared with the conventional PD and Fuzzy logic controller. The simulation results show that the LADRC controller is better in performance as compared to the conventional PD and FLPI controller.

Power stabilization is a critical issue in nuclear reactors. A conventional PD controller is currently used in Egypt's second testing research reactor (ETRR-2). This controller is designed neglecting the nonlinear nature of the plant. In this paper, two fuzzy controllers are used instead of the conventional PD. The first fuzzy controller has a static rule set, while the second one has an adaptive rule set. Simulation results show that the adaptive fuzzy controller gives the best integral square error (ISE) index, while the static fuzzy controller still provides a better ISE than the conventional PD controller.

In this paper, an adaptive robust PD controller for Unmanned Underwater Vehicle (UUV) is proposed. Though conventional PD control is simple and effective for UUV heading control, in many circumstances, a large initial torque is required, while the maximum torque of the rudder is limited, thus making it unavailable to further improve the heading control effects. The adaptability and robustness of this control system is investigated by computer simulation, lake and sea trials. Experimental results indicate the effectiveness of the proposed controller in dealing with model uncertainties, nonlinearities of UUV, and eliminating the excessive initial output torque of the rudder and environmental disturbance caused by sea currents and waves.

Mechatronic systems are conventionally modeled by integer order state equations for simplicity. Based on these integer-order models, integer-order PID or other classical model-based controllers are designed. However, often complex mechatronic systems require fractional-order state equations for a more adequate description. In this paper, a Steward platform (ball and plate system) with different balls is used to demonstrate this. A fractional-order PD controller is designed and tuned by a genetic algorithm based global optimizer to obtain the suitable parameter values in time domain. The experimental results show that the fractional-order PD controllers can provide better performances than conventional integer-order PD controllers.

In this paper, we propose the LQ servo control method for the automatic train control of urban rail vehicle. Structures of the conventional PID control and LQ servo controller are compared in order to demonstrate the simplicity of the proposed controller which doesn't have zeros of the closed loop systems. The energy consumption is dealt with as an object function to be minimized, which consists of the quadratic performance indices for optimal control with the input of the feedback linearization. The effectiveness of the proposed method is shown by the practical example, compared with the conventional PD controller.

This paper deals with coordinated control problem of relative position and attitude of spacecraft with actuator saturation limit. Relative coupled dynamic model based on dual quaternion is introduced to describe their translational and rotational motions. In order to solve the actuator saturation problem, a conventional PD controller is modified by augmenting anti-windup control and intelligent integrator to increase the level of saturation allowed as well as the disturbance amplitude. The validity of the presented method and the superiority to conventional PD controller are demonstrated by numerical simulations.

Accurate estimation of inverse kinematics and dynamics is essential for precise robotic manipulator control. Traditional analytical models, while effective for nominal conditions, often struggle to capture complex nonlinearities and unmodeled dynamics, necessitating compensatory controllers. However, these combined approaches can become computationally intensive and prone to redundancy. Conventional machine learning methods have shown promise in error compensation but typically demand large datasets and incur high computational costs, particularly when addressing high-dimensional state spaces. To overcome these limitations, this paper proposes a novel data-efficient framework based on Liquid Neural Networks (LNNs) for learning inverse kinematics and dynamics directly from simulation platform measurements. The inherent capability of LNN to model temporal dependencies and adapt continuously to evolving system dynamics enables accurate approximation of nonlinear manipulator behavior without reliance on extensive offline training. The proposed method is validated on two distinct robotic platforms—a UR5e manipulator with 6 DoF and a WAM manipulator with 7 DoF using both simulation and real-world trajectory data acquired through a targeted sampling strategy. Both simulation and experimental evaluations with previously unseen trajectories demonstrate that the ILNN-based approach achieves high-fidelity approximation of both inverse kinematics and inverse dynamics, delivering superior generalization and robustness compared to conventional analytical and data-driven methods. These results highlight the potential of LNNs as an efficient and scalable solution for real-time robot control in dynamic and uncertain environments.

Neural network-based control of balanced robotic manipulators with joint flexibility

For two-wheeled self-balancing robot nonlinear model uncertainties and other characteristics, the design of fuzzy immune PD controller and a simulation study. The simulation results show that the control system than the conventional fuzzy PD controller with a small overshoot, adjust the time is short, anti-interference ability, etc., applicable to the mathematical model and the parameter change is difficult to determine two-wheeled self-balancing robot control system.

Composite Disturbance Rejection Control for Ball Balancer System

Autonomous capture of an unknown non-cooperative aerial target is a complex task requiring real-time target localization, trajectory prediction and control. The current work presents an estimation and control system for a quadrotor in order to track and grab a dynamic aerial target. A Kalman filter is used to estimate and predict the target’s pose in real time, which is further used as reference by a Model Predictive Controller for tracking and grabbing the target while chasing it. The efficacy of the proposed controller is demonstrated through repeated trials with varying initial conditions and target maneuvers. The controller is compared with a conventional PD controller. The accuracy of estimation algorithms and the ability of the proposed controller to neutralize the target is demonstrated by means of simulations in gazebo.

This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking.

This paper develops with a stabilizing method for hunting of the 2-link pneumatic manipulator. In the pneumatic manipulator, the time-delay exists due to the air tube when air pressure signal is transmitted, and the effect of the pneumatic motor's nonlinear friction is easy to be received. The hunting of each arm is not prevented by the conventional PD control, and it is generally difficult to realize a good stability control. For above problems, the new control method using neural network (NN) is proposed. Through simulation and experiment, the validity and practicability are clarified by this control method.

The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.