PD Compensator Research Articles

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m.

A modified Smith predictor based – Sliding mode control approach for integrating processes with dead time

This paper proposes a modified hybrid robust Smith predictor controller design for integrating processes with long dead time. The proposal results in a dynamic sliding mode controller for integrating systems on blending sliding mode control and Smith Predictor modification concepts. The design part of the controller corresponding to the sliding mode arises from an approximate model of the high-order nonlinear integrating process to characterize the non-self-regulating processes. The approximation to a First Order Integrating System Plus Dead Time is obtained from an experimental method that adopts identification procedures and avoids complex analytical resources. The model contributes to designing the dynamic sliding mode controller and tuning parameters. The resulting controller is a hybrid scheme that combines the sliding mode methodology, a smith predictor structure, and a PD compensator. The hybrid control topology is used for the first time for integrating systems with dead time. The proposed approach is applied to a high order linear integrating system with long-dead time and a coupled tanks system using hardware in the loop (HIL) simulation. The hybrid control topology provides enhanced controller performance, guarantees proven robustness to parameter modeling uncertainties, and attenuates high-frequency oscillations, counteracting the adverse effects of chattering. Integral of Squared Error (ISE), Total Variation of control effort (TVu), Maximum overshoot, and Settling Time are used for quantitative comparative performance analysis.

Quadruped Robot Control through Model Predictive Control with PD Compensator

Quadruped Robot Control through Model Predictive Control with PD Compensator

A method of synchronous control system for dual parallel motion stages

A synchronous control system for dual parallel stages is proposed in order that the synchronous error owing to the skew disturbances can be quickly and stably eliminated. The control system is composed of two disturbance observers and a synchronous controller. The two disturbance observers are designed to decrease the mismatched dynamics between a nominal model and a real plant with nonlinear properties and to reject instantly disturbances for each stage. The synchronous controller is designed from the view point of accurate synchronization and improvement of damping characteristics for the synchronous control system. The proposed synchronous control system is evaluated and compared experimentally with a normal cross-coupling control system without disturbance observer. The experiment results show that the new control system considerably reduces the synchronous error against skew disturbances. In addition, the synchronous error can be quickly and more stably eliminated through the improvement of the damping characteristics of the synchronous control system using a PD compensator.

온라인 피드백 에러 학습을 이용한 이동 로봇의 자율주행 알고리즘 개발

본 논문에서는, 신경망을 이용한 뉴로 인터페이스 설계를 통해 논홀로노믹 이동 로봇을 제어하는 방법을 제시하였다. 특히, 가상의 마스터-슬레이브 로봇 개념을 이용하여, 부분적으로 안정된 마스터 로봇의 역 동적모델이 피드백-에러 학습법을 적용한 신경망을 통해 온라인으로 획득되도록 하였다. 이 피드백 제어기는 PD 보상기에 기초를 두고 있다. 온라인 학습을 위한 신경망은 입력층이 6개의 입력세포들(<TEX>$x_i$</TEX>, i=1~6)로 구성되어있으며, 1개의 은닉층에는 2개의 은닉세포(<TEX>$o_j$</TEX>, j=1~2), 출력층은 2개의 출력세포(<TEX>${\tau}_k$</TEX>, k=1~2)로 구성되었고, 신경망의 온라인 학습을 위하여 최소자승법에 의한 오류역전파 알고리즘을 이용하였다. 본 연구에서 개발된 뉴로 인터페이스의 경로추적제어에 관한 성능은 2-wheel 독립구동이 가능한 논홀로노믹 이동 로봇의 시뮬레이션으로 증명하였다. In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs <TEX>$x_i$</TEX>, i=1~6, the hidden layer consists of two hidden units for hidden outputs <TEX>$o_j$</TEX>, j=1~2, and the output layer consists of two units for the outputs <TEX>${\tau}_k$</TEX>, k=1~2. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, <TEX>${\pi}$</TEX>].

Design of a memory‐based prefilter supplementing a robust PID control system

AbstractFor systems with uncertainties, lots of PID parameter tuning methods have been proposed from the view point of the robust stability theory. However, the control performance becomes conservative using robust PID controllers. In this paper, a new two‐degree‐of‐freedom (2DOF) controller, which can improve the tracking properties, is proposed for nonlinear systems. According to the proposed method, the prefilter is designed as the PD compensator whose control parameters are tuned by the idea of a memory‐based modeling (MBM) method. Since the MBM method is a type of local modeling methods for nonlinear systems, PD parameters can be tuned adequately in an online manner corresponding to nonlinear properties. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

A BMI-Based Design of Switched PID Controllers

This paper provides a design method for two-degrees-of-freedom PID controllers including switched PD compensator based on bilinear matrix inequalities (BMIs). Two design specifications based on H 2 norm are formulated in BMIs, and PID parameters can be exactly obtained by solving the BMI problems via branch and bound algorithms. A set of PD compensators can be obtained simultaneously by using proposed design method. The most effective parameter is selected out of the set of PD compensator based on the switching criterion which obtained from estimated system conditions using recursive least square algorithms. Numerical example is also shown.

LINEAR OPTIMAL FEEDBACK AND LOAD‐DECOUPLED CONTROL OF A BUCK DC‐DC CONVERTER

ABSTRACTThis paper describes a new controller design procedure and tuning method for a PWM buck dc‐dc converter. First, linear optimal feedback is designed using the LQR approach. Then the designed control law is implemented using a PID controller incorporated with a load‐decoupled PD compensator. The PID controller is tuned to achieve the optimal design based on the output error voltage directly, instead of using an estimator. When the proposed PD compensator is used, the converter is robust with respect to the input voltage and output current changes and the parameter perturbations. We also provide the conditions for the robust stability assurance of the closed‐loop system.

Positioning Control of Direct Drive Robot with Two-Degree-of-Freedom Compensator.

To achieve high performance control of robot manipulator, one has to overcome disturbances added on each joint and nonlinear forces such as a frictional force, gravity, interacting force. To meet these ends, recently a method based on disturbance observer was proposed which is characteristic of the fact that it does not need inverse dynamics calculation and thus can be implemented with a simple microprocessor. However, this method depends on the intutional approach in determining the disturbance estimation filter, so more systematic approach is desirable. In this paper, we propose a new design approach of a two-degree-of-freedom compensator taking into account the robust stability. First of all, we clarify the internal structure of the generalized two-degree-of-freedom compensator. By using this structure, we can make a bridge between a generalized controller and the disturbance observer based controller. Secondly, based on the clarified structure we derive a robust stability condition, and propose a design algorithm of free parameter taking into account it. The proposed design algorithm is ease to execute and, as the result, we obtain the low order free parameter compared with the conventional design algorithm. Thirdly, we show that the clarified structure can also be regarded as an extended version of the conventional PD compensator by adopting an appropriate coprime factorization. Finally, we apply the proposed algorithm to a three-degree-of freedom direct drive robot, and show some experimental results to verify the effectiveness of the proposed algorithm.