Proportional Derivative Controller Research Articles

Optimized Proportional-derivative Feedback-assisted Iterative Learning Control for Manipulator Trajectory Tracking

Optimized Proportional-derivative Feedback-assisted Iterative Learning Control for Manipulator Trajectory Tracking

Towards autonomous inland shipping: a manoeuvring model in confined waterways

ABSTRACT Autonomous inland water vessels are essential for promoting intelligent and sustainable waterborne transport. An accurate ship manoeuvring model ensures reliable control strategies and enhances navigation safety. Although ship manoeuvrability models have existed for decades, few studies address shallow and restricted waters. This study introduces a manoeuvring model for inland water vessels, accounting for confinement effects on ship motion. The Manoeuvring Modelling Group (MMG) model in open water serves as the baseline, incorporating empirical methods for shallow water and bank effects. This approach aims to provide a fast and accurate prediction of vessel motion response. The model was validated with free-running experimental data of a pusher-barge model from turning tests at three water depths. Additional case studies highlight shallow water impact compared to infinite water performance under bank effects. Finally, course-keeping case studies are presented, integrating a Proportional-Derivative controller with combined river current and bank-induced forces..

PEMODELAN SOLAR TRACKER DENGAN PENGENDALI PI, PD, DAN PID MENGGUNAKAN MATLAB-SIMULINK DENGAN BEBAN BATERAI

A solar tracker is a photovoltaic (PV) system support tool aimed at optimizing solar energy absorption by directing solar cells to follow the movement of the sun.In this research, a solar tracker for PV with battery load model was created using MATLAB-Simulink with each controller, namely Proportional Integral (PI), Proportional Derivative (PD) and Proportional Integral Derivative (PID) and the simulation results were compared. The simulation results show that the PID controller is the controller with the most optimal results because all set point values ​​entered in the output remain the same and the overshoot value is also low, the PD controller is the controller that produces high errors and also overshot reaching 120 V, and the PI controller is the controller that the results are closest to a PID controller where the resulting overshoot value is only around 20%.

Speed Control of Induction Motors using Neuro Fuzzy Expert Systems

Induction motors and their speed control is critical aspect in several real life applications. Often, the proportional integral derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this work, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been proposed for the speed control of induction motors. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink. Key Words: Induction Motors, Speed Control, PID Controllers, Fuzzy Logic, Neuro Fuzzy Expert Systems.

Experimentally validated predictive PI-PD control strategy for delay-dominant chemical processes

Controlling delay dominant chemical processes is a challenging control problem. Hence, a new method for designing a Smith predictor (SP)-based proportional-integral proportional-derivative (PI-PD) control strategy is proposed in this study. The inner-loop PD parameters are determined using the phase margin (PM) and maximum sensitivity (Ms) specifications. To determine the outer-loop PI parameters, the moment matching technique is augmented with maximum sensitivity considerations. The performance and robustness of this design is compared with that of its contemporaries using four benchmark chemical process models including that of a stirred tank reactor and a heat exchanger. The resilience of this control strategy amid uncertainties in process parameters is studied and performance improvement achieved over contemporary works is quantified. Finally, the proposed design is also experimentally validated on a two-tank level control loop.

Adaptive iterative learning control of soft robot for beating heart tracking

Purpose Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart. Design/methodology/approach In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system. Findings The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics. Originality/value Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

A Novel Fractional Order Proportional Integral-Fractional Order Proportional Derivative Controller Design Based on Symbiotic Organisms Search Algorithm for Speed Control of a Direct Current Motor

A Novel Fractional Order Proportional Integral-Fractional Order Proportional Derivative Controller Design Based on Symbiotic Organisms Search Algorithm for Speed Control of a Direct Current Motor

Soft Robotic Bilateral Rehabilitation System for Hand and Wrist Joints

Upper limb functionality is essential to perform activities of daily living. It is critical to investigate neurorehabilitation therapies in order to improve upper limb functionality in post-stroke patients. This paper presents a soft-robotic bilateral system to provide rehabilitation therapy for hand and wrist joints. A sensorized glove that tracks finger and wrist joint movements is worn on the healthy limb, which guides the movement of the paretic limb. The input of sensors from the healthy limb is provided to the soft robotic exoskeleton attached to the paretic limb to mimic the motion. A proportional derivative flow-based control algorithm is used to perform bilateral therapy. To test the feasibility of the developed system, two different applications are performed experimentally: (1) Wrist exercise with a dumbbell, and (2) Object pick-and-place task. The initial tests of the developed system verified its capability to perform bilateral therapy.

Robust Fractional-Order PI/PD Controllers for a Cascade Control Structure of Servo Systems

In this paper, a cascade control structure is suggested to control servo systems that normally include a servo motor in coupling with two kinds of mechanism elements, a translational or rotational movement. These kinds of systems have high demands for performance in terms of fastest response and no overshoot/oscillation to a ramp function input. The fractional-order proportional integral (FOPI) and proportional derivative (FOPD) controllers are addressed to deal with those control problems due to their flexibility in tuning rules and robustness. The tuning rules are designed in the frequency domain based on the concept of the direct synthesis method and also ensure the robust stability of controlled systems by using the maximum sensitivity function. The M-Δ structure, using multiplicative output uncertainties for both control loops simultaneously, is addressed to justify the robustness of the controlled systems. Simulation studies are considered for two kinds of plants that prove the effectiveness of the proposed method, with good tracking of the ramp function input under the effects of the disturbances. In addition, the robustness of the controlled system is illustrated by a structured singular value (µ) plot in which its value is less than 1 over the frequency range.

A deep learning optimized LQR method for enhanced formation control with embedded systems

To achieve higher accuracy throughout the formation control processes and enhance precision in dynamic environments, particularly for the formation control of follower vehicles with embedded systems, this paper proposes a method and framework for vehicle formation control. An Ackermann-model based Linear Quadratic Regulator (LQR) controller is developed for lateral distance control and a Proportional-Derivative (PD) controller for longitudinal distance control. To enhance the efficacy of the LQR controller, the Deep Deterministic Policy Gradient Derivative (DDPG) method is introduced into the control system. The DDPG networks are trained in a simulation environment and can subsequently predict LQR parameters in real-time experiments. The practical application of the method is showcased, and the concluding remarks emphasize the potential and superior performance of our proposed formation control approach by experimental comparison with other controllers. This method can be implemented in small vehicles that possess limited computational resources and is also suitable for scenarios requiring dynamic motion control with higher tracking accuracy and stability.

A convolutional neural network based on an evolutionary algorithm and its application

PM2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability.

A vibration analysis of the permanent magnet synchronous motor under the effect of proportional derivative control

In industrial environments, excessive vibration can pose a safety risk since it can weaken the structural integrity of PMSMs and adjacent equipment. Vibration levels can be understood and controlled to help assure safe operation and avoid mishaps or equipment breakdowns. Friction and mechanical resistance are examples of vibration-induced losses that can lower PMSMs’ overall efficiency. Through the optimization of motor design and operation, engineers may minimize vibration and increase energy efficiency while lowering operating costs. Since an electric motor’s tendency to overheat after a brief period of use is linked to oscillating vibration, this study employs a proportional derivative control (PD control) to demonstrate the potential effectiveness of a permanent magnet synchronous motor (PMSM). The external force is a component of the non-linear dynamical system. The equation of the (PMSM) system is explained with two-degree-of-freedom (2dof) differential coupled equations, consisting of the major body (motor current) and cutting drive system. The present study uses the approximate method of multiple scales perturbation technique (MSPT) to get an approximate solution, showing the response equation before using (PD) and to achieve the highest effect on the control system so as to ensure the highest efficiency at the lowest possible cost. Different numerical tests have been conducted (NPDCVF, NVC, PPF, PD, delay velocity) and showed that (PD) has the best effect and is able to control the vibrations with more accuracy than others. Then, the vibration value of the system has been studied numerically before and after applying the control method of (MSPT). By combining the analysis of the resonance situation via both the phase plane techniques and the frequency response equation ω1≅ω2,ω≅ω2, within the utilization of Runge–Kutta of the fourth-order, the stability of the numerical solution has been investigated. Then, by employing the MATLAB tool, the impact of various parameters on model performance has been examined numerically. Lastly, a comparison is made with earlier research and other techniques used in prior work using other types of control mechanisms for other systems.

Efficient and accurate flexible multibody dynamics modeling for complex spacecraft with integrated control applications

This study presents a novel flexible multibody dynamics modeling method tailored to spacecraft with intricate motion features such as multi-axis antenna actuation, robotic manipulations, and space station module rotation. Using a floating base and a tree-topology structure, the model effectively simulates the kinematics and dynamics of large-angle joint rotations and elastic deformations, leveraging the Lagrangian framework and finite element analysis. An advanced augmented proportional–derivative (APD) control scheme is also introduced, incorporating system dynamics for enhanced predictive control, accommodating complex interactions and nonlinear system behaviors. Our modeling approach, compared with the commercial Adams software, shows similar system responses while reducing computation time by about 70%, highlighting its efficiency and accuracy. Moreover, incorporating the APD strategy into our system enhances tracking precision, achieving a 90% reduction in error compared with conventional proportional–derivative controls, underscoring the benefits of combining dynamic modeling with control improvements.

Direct synthesis approach to design PDμ${\text{PD}}^\mu$ based smith predictor for integrating plants: Studied on a quadrotor UAV

AbstractThis article presents a simple yet novel method of designing a fractional‐order proportional derivative (namely, ) controller for all types of integrating plants. Considering the importance of the direct synthesis approach, the method obtains robust closed‐loop performance. The setpoint parameter is obtained using the multi‐optimization Pareto solution, considering the optimal values in control efforts and performance index together. The numerical investigations have shown improved servo and regulatory responses compared to recently published strategies with fractional orders. It is also known that classical PIDs cannot accurately follow a ramp setpoint. A real‐world situation also demands that the reference inputs become an acceleration ramp type. The proposed technique can handle rising ramp setpoints with plant uncertainty and measurement noise. Hardware verification is also performed on a quadrotor minidrone to check for real‐time issues.

Precision Landing of Unmanned Aerial Vehicle under Wind Disturbance Using Derivative Sliding Mode Nonlinear Disturbance Observer-Based Control Method

Unmanned aerial vehicles (UAVs) are extensively employed in civilian and military applications because of their excellent maneuverability. Achieving fully autonomous quadrotor flight and precision landing on a wireless charging station in the presence of wind disturbance has become a crucial research topic. This paper presents a composite control technique for UAV altitude and attitude tracking in harsh environments, i.e., wind disturbance. A composite controller was developed based on nonlinear disturbance observer (NDOB) control theory to allow the UAV to land in the presence of random external wind disturbances and ground effects. The NDOB estimated the unknown wind disturbance, and the estimation was fed into the derivative sliding mode nonlinear disturbance observer-based control (DSMNDOBC), allowing the UAV to perform autonomous precision landing. Two loop designs were applied: the inner loop for stabilization and the outer loop for altitude tracking. The quadrotor model dynamics and the proposed controller, DSMNDOBC, were simulated employing MATLAB/Simulink®, and the results were compared with the one obtained by the proportional derivative (PD) controller and the sliding mode controller (SMC). The simulation results indicated that the DSMNDOBC has superior altitude and attitude control compared to the PD and SMC controllers and better disturbance estimation and attenuation performance.

Dynamic Response of an Overhead Crane with Anti-Swing Controller in Driving Force and Energy Consumption

Cargo sway caused by overhead crane operation not only reduces crane efficiency and safety but also affects the driving force and energy consumption of the operating mechanism. In this paper, a control scheme based on proportional derivative (PD) control and an input shaping technique is proposed to suppress cargo sway. First, the dynamics model of the crane system is established, and the mathematical expressions of the cargo sway, driving force, and energy consumption are derived. The accuracy of the dynamics model is verified through a comparison between the simulations and experiments. Then, a PD-IS controller is designed based on the dynamics model. Finally, the performance of the designed controller is simulated under four typical excitation signals and compared to that of the standard PD controller.

Anti-Swaying Control Strategy of Ship-Mounted 3-RCU Parallel Platform Based on Dynamic Gravity Compensation

It is essential to ensure stability during marine transportation or the installation of high center of gravity loads. The heavy loads increase gravity disturbance, affecting the steady-state-error control of the multiple degrees of freedom (DOFs) motion compensation platform. In this paper, we propose a proportional derivative (PD) controller with dynamic gravity compensation (PDGC) for a 3-RCU (revolute–cylindrical–universal) parallel platform to improve the control effect of marine motion compensation for high center of gravity loads. We introduce an evaluation parameter of load stability and a weighting coefficient of anti-swaying control to tune the controller performance. The controller can set its control target between the two, keeping the load contact surface level and allowing the load center of gravity with the least movement. By deriving the Jacobian matrix, the gravity disturbance in the joint space is calculated and is compensated in the controller. First, we verify the control superiority of this controller over the PD controller under sinusoidal excitation in simulation and validate the effectiveness of the proposed anti-swing strategy. Then, the experiments are conducted with random excitation. The root mean square (RMS) value of the load’s residual angle with the proposed controller is reduced to 32.2% and 17.6% in two directions, respectively, compared with the PD controller under class 4 sea state excitation. The proposed method is effective for the anti-swaying control of ship-mounted 3-RCU parallel platforms.

Pitch angle control of wind turbines using model-free auto-tuned fractional order proportional derivative ATFOPD controller

The nonlinear dynamics of wind power plants, in addition to uncertainties such as unknown nonlinear disturbances and parameter variations make the design of a robust controller a difficult task. This paper proposes a robust fractional-order controller for the pitch angle control of a variable-speed wind turbine system. An auto-tuned fractional order proportional-derivative controller ATFOPD is designed taking the objectives of reducing pitch actuator fatigue, limiting output mechanical power and speed at their rated values above the rated wind speed. The phase, phase slope and modulus of the pitch actuator process at the fixed gain crossover frequency are needed in order to tune the fractional order controller parameters. These values are usually derived using a mathematical model of the system, such as a transfer function. A model-free auto-tuning technique that can calculate the controller's parameters from measured variables is used in this work in order to design a robust FOPD controller. Simulation results of the ATFOPD controlled system are compared to integer order proportional-derivative IOPD and IOPID controllers, which show that the ATFOPD controller is more effective than the other controllers and its robustness is verified in the presence of disturbances in addition to system parameters and wind speed variations.

The condition for dynamic stability in humans walking with feedback control.

The walking human body is mechanically unstable. Loss of stability and falling is more likely in certain groups of people, such as older adults or people with neuromotor impairments, as well as in certain situations, such as when experiencing conflicting or distracting sensory inputs. Stability during walking is often characterized biomechanically, by measures based on body dynamics and the base of support. Neural control of upright stability, on the other hand, does not factor into commonly used stability measures. Here we analyze stability of human walking accounting for both biomechanics and neural control, using a modeling approach. We define a walking system as a combination of biomechanics, using the well known inverted pendulum model, and neural control, using a proportional-derivative controller for foot placement based on the state of the center of mass at midstance. We analyze this system formally and show that for any choice of system parameters there is always one periodic orbit. We then determine when this periodic orbit is stable, i.e. how the neural control gain values have to be chosen for stable walking. Following the formal analysis, we use this model to make predictions about neural control gains and compare these predictions with the literature and existing experimental data. The model predicts that control gains should increase with decreasing cadence. This finding appears in agreement with literature showing stronger effects of visual or vestibular manipulations at different walking speeds.

Autonomous Landing of Quadrotor Unmanned Aerial Vehicles Based on Multi-Level Marker and Linear Active Disturbance Reject Control.

Landing on unmanned surface vehicles (USV) autonomously is a critical task for unmanned aerial vehicles (UAV) due to complex environments. To solve this problem, an autonomous landing method is proposed based on a multi-level marker and linear active disturbance rejection control (LADRC) in this study. A specially designed landing board is placed on the USV, and ArUco codes with different scales are employed. Then, the landing marker is captured and processed by a camera mounted below the UAV body. Using the efficient perspective-n-point method, the position and attitude of the UAV are estimated and further fused by the Kalman filter, which improves the estimation accuracy and stability. On this basis, LADRC is used for UAV landing control, in which an extended state observer with adjustable bandwidth is employed to evaluate disturbance and proportional-derivative control is adopted to eliminate control error. The results of simulations and experiments demonstrate the feasibility and effectiveness of the proposed method, which provides an effective solution for the autonomous recovery of unmanned systems.