PD control with gain-scheduling depending on the distance for a 3-dof network based haptic system

Both neural network and sliding mode can compensate the steady-state error of proportional-derivative (PD) control. PD control with neural compensation is smooth, but it is not asymptotically stable. PD control with sliding mode is asymptotically stable, but the chattering is big. This paper first analyzes the asymptotic stability of PD control with parallel neural networks and the first-order sliding mode compensation. Then a serial compensation structure is proposed. In the serial compensation, a dead-zone neural PD control assures that the regulation error is bounded. And a super-twisting second-order sliding-mode is used to guarantee finite time convergence of the sliding mode PD control.

Dynamic Analysis of Electro Hydrostatic Actuation System

For a multi-input multi-output (MIMO) system a set of stabilizing proportional derivative (PD) controllers was designed for each decoupled subsystem. The design approach is based on generalized result of the classical Hermite Biehler theorem. Stability analysis of the composite system with the designed set of decoupled controller parameter is studied via LMI framework. A genetic algorithm based search technique is adopted to select an optimal PD controller gain from a search space of PD stabilizing controllers. A MIMO system is considered to show the effectiveness of the design procedure.

The objective of this paper is to show the results of the practical implementation of a neural network (NN) tracking controller on a single flexible link and compare its performance to that of proportional derivative (PD) and proportional integral derivative (PID) standard controllers. The NN controller is composed of an outer PD tracking loop, a singular perturbation inner loop for stabilization of the fast flexible-mode dynamics, and an NN inner loop used to feedback linearize the slow pointing dynamics. No off-line training or learning is needed for the NN. It is shown that the tracking performance of the NN controller is far better than that of the PD or PID standard controllers. An extra friction term was added in the tests to demonstrate the ability of the NN to learn unmodeled nonlinear dynamics.

The linear quadratic regulator (LQR) is a classical optimal control approach that can regulate gait dynamics about target kinematic trajectories. Exoskeletons to restore gait function have conventionally utilized time-varying proportional-derivative (PD) control of leg joints. But, these PD parameters are not uniquely optimized for whole-body (full-state) performance. The objective of this study was to investigate the effectiveness of LQR full-state feedback compared to PD control to maintain bipedal walking of a sagittal-plane computational model against force disturbances. Several LQR controllers were uniquely solved with feedback gains optimized for different levels of tracking capability versus control effort. The main implications to future exoskeleton control systems include (1) which LQR controllers out-perform PD controllers in walking maintenance and effort, (2) verifying that LQR desirably produces joint torques that oppose rapidly growing joint state errors, and (3) potentially equipping accurate sensing systems for nonjoint states such as hip-position and torso orientation. The LQR controllers capable of longer walk times than respective PD controllers also required less control effort. During sudden leg collapse, LQR desirably behaved like PD by generating feedback torques that opposed the direction of leg-joint errors. Feedback from nonjoint states contributed to over 50% of the LQR joint torques and appear critical for whole-body LQR control. While LQR control poses implementation challenges, such as more sensors for full-state feedback and operation near the desired trajectories, it offers significant performance advantages over PD control.

Electro-Mechanical Dual Acting Pulley (EMDAP) Continuously Variable Transmission (CVT) is a transmission utilized by electro-mechanical actuated system. It has a potential to reduce energy consumption because it only needs power during changing CVT ratio and no power is needed to maintain CVT ratio due to self lock mechanism design. This paper proposed simple proportional derivative plus conditional integral (PDCI) controller to control EMDAP CVT ratio which can be simply implemented on a microcontroller. This proposed controller used Astrom-Hagglund method and Ziegler-Nichols formula to tune PDCI gain. The Proportional Derivative controller is directly activated from the start but Integral controller is only activated when the error value reaches error value setting point. Simulation using Matlab/Simulink software was conducted to evaluate PDCI system performance. The simulation results showed PDCI controller has ability to perform maximum overshoot 0.1%, 0.001 steady state error and 0.5s settling time. For clamping condition, settling time is about 11.46s during changing ratio from 2.0 to 0.7, while for release condition, settling time is about 8.33s during changing ratio from 0.7 to 2.0.

This paper studies the proportional-derivative (PD) control method in the control field, which can be applied in industrial automation, robot control, aerospace, process control, and other fields. This paper discusses the PD control to adjust the motor angle and the fuzzy proportional-integral (PI)-PD control of the brushless direct current (DC) motor. How to apply PD control theory to practice is a part worth exploring. In the traditional control field, proportional-integral-derivative (PID) control parameters cannot adjust the changes of controlled parameters, and the traditional PID control must cooperate with three parameters at the same time to work. Therefore, in this paper, PD control is applied to the adjustment of motor angle, and PI-PD fuzzy control is used to adjust the controller parameters in real time. This method can improve the response speed of the controller, apply the control theory to the actual motor control, and improve the anti-interference ability. This research can improve the application scope of PD control and combine it with a variety of control theories, to achieve a better effect of the control system, optimize the response speed of the motor, and achieve the target speed faster.

This paper presents a study on miniature continuum robots which can bend continuously along their length and can be constructed to be more than one section to increase their degrees of freedom. This study is concerned with providing the kinematics and the dynamics of the robot. The kinematic equations is provided by Piece-wise constant curvature (PCC) assumption which is based on curve geometry. The Dynamics of the robot are provided by the analysis of the Lagrangian equation. Next, three control strategies are applied on a two-section continuum robot model using MATLAB Simulink environment to demonstrate the effectiveness of each control loop. The three control strategies are Proportional Derivative (PD) control, Fuzzy Logic Control (FLC), and Sliding Mode Control (SMC). The simulation results show an advantage in performance for the intelligent controller (FLC) and the nonlinear robust control (SMC) over the PD control.

One of the objectives of electrical distribution networks is to provide customers with access to high‐quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter’s resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system’s frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers.

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter.

This paper presents a stabilization control method for “x” configuration quadcopter. The control method used the combination of PD (Proportional Derivative) controller and PIAFC (Proportional Integral Active Force Control). PD is used to stabilize quadcopter, and PIAFC is used to reject uncertainty disturbance (e.g. wind) by estimating disturbance torque value of quadcopter. The PD with PIAFC provided better result where PIAFC could minimize uncertain disturbance effect. The simulation has successfully give comparation about controller performance (PD, PD-AFC, PD-PIAFC) by calculate RMS (Root Mean Square) value. PD with AFC gives better result than PD. AFC optimization using PI (PD-PIAFC) give best result if compared with PD or PD-AFC. PD-PIAFC has lowest RMS value of result control signal, 0.0389 for constant disturbance and 0.1008 for fluctuated disturbance.Keywords:“x” configuration quadcopter, stability, PD, PIAFC.

This paper is concerned with developing a procedure for stabilizing a linear time-invariant plant of any order with time delay utilizing proportional-integral (PI) and proportional-derivative (PD) controllers. The method presented here is based on computing the stability boundary in terms of the proportional and integral gain for the PI case, and similarly, proportional and derivative gain for the PD case. The two variables are then plotted on the same coordinate system, thus obtaining the stability region for each controller used. The advantage of this procedure is the fact that it does not require the knowledge of the plant transfer function parameters, but only its frequency response. In addition, if the plant function is known, the procedure may also be used to analytically obtain the stabilizing controllers. We also present the tuning rules for user specified gain and phase margins.

In order to maintain upright stability, two-wheel self-balancing robots belong to a type of underactuated, intrinsically unstable systems that need constant feedback control. The design, modelling, and practical implementation of a two-wheel self-balancing robot employing a proportional-derivative (PD) control approach are presented in this study. An inverted pendulum supported by two driven wheels serves as the model for the robot. An inertial measuring device made up of an accelerometer and gyroscope provides real-time orientation feedback. To fuse sensor data and reduce noise and drift, a complementary filter is used. LabVIEW is used to build the control algorithm on an NI myRIO platform, where the PD controller produces pulse-width-modulated motor commands. According to experimental data, the PD controller can achieve stable balancing with low oscillations and a quick transient response when the gain is tuned appropriately. The paper demonstrates that classical PD control provides a dependable and computationally effective solution for self-balancing robotic platforms, making it appropriate for real-time embedded control, prototype-level, and instructional applications.

This paper proposes the use Fuzzy logic controller and a Particle Swarm Optimization (PSO) based Linear Quadratic Regulator (LQR) controller for the balance of an electric unicycle. It also examines dynamics of the electric unicycle's schematic design, which has a backing seat for the passenger and is analogous to the Segway device equipped with a handle shaft for direction. A proportional derivative controller incorporated with a Fuzzy logic controller was designed for the suggested model of the electric unicycle for speed and position control (Fuzzy controller), and a Proportional Derivative (PD) controller was utilized to control the angle rate and angle of the unicycle. Comparison of the Fuzzy logic controller with a Particle Swarm Optimization (PSO) Linear Quadratic Regulator (LQR) controller was carried out based on error deviation and time response simulations. Command tracking or regulation of some initial state condition were examined and demonstrated by time based simulations of the modelled system. The results evidently indicate that the Fuzzy Proportional Derivative (PD) controller can effectively attain attitude equilibrium for the two wheeled unicycle and preventing it from falling down.

As the character of brushless DC motor (BLDCM) is nonlinear, the classical control method can not obtain satisfied performance, so the fuzzy control is used to control BLDCM, but the conventional fuzzy controller has no integral part, which performance is similar to Proportional Derivative(PD) controller, so its static control precision cannot be guaranteed. In this paper, a fuzzy Proportional Integral and Derivative(PID) controller that can ensure static precision is proposed to improve the performance of conventional fuzzy controller and is applied to the speed control of brushless DC motor. The proposed fuzzy-PID method employs fuzzy control when the error is big, employs the PID control when the error is small, so it has both the rapid response of ordinary fuzzy controller and the steady precision of PID controller. The experiment results show the dynamic and static performance of the proposed fuzzy-PID controller are all excellent, the overshoot is zero, the speed respond is fast and the static precision is high, better than the conventional PID controller and the conventional fuzzy controller in speed control.