Position control for haptic device based on discrete-time proportional integral derivative controller

Haptic devices had known as advanced technology with the goal is creating the experiences of touch by applying forces and motions to the operator based on force feedback. Especially in unmanned aerial vehicle (UAV) applications, the position of the end-effector Falcon haptic sets the velocity command for the UAV. And the operator can feel the experience vibration of the vehicle as to the acceleration or collision with other objects through a forces feedback to the haptic device. In some emergency cases, the haptic can report to the user the dangerous situation of the UAV by changing the position of the end-effector which is be obtained by changing the angle of the motor using the inverse kinematic equation. But this solution may not accurate due to the disturbance of the system. Therefore, we proposed a position controller for the haptic based on a discrete-time proportional integral derivative (PID) controller. A Novint Falcon haptic is used to demonstrate our proposal. From hardware parameters, a Jacobian matrix is calculated, which combines with the force output from the PID controller to make the torque for the motors of the haptic. The experiment was shown that the PID has high accuracy and a small error position.

Cascade controllers design based on model matching in frequency domain for stable and integrating processes with time delay

Purpose This paper aims to present an efficient and simplified proportional-integral/proportional-integral and derivative controller design method for the higher-order stable and integrating processes with time delay in the cascade control structure (CCS). Design/methodology/approach Two approaches based on model matching in the frequency domain have been proposed for tuning the controllers of the CCS. The first approach is based on achieving the desired load disturbance rejection performance, whereas the second approach is proposed to achieve the desired setpoint performance. In both the approaches, matching between the desired model and the closed-loop system with the controller is done at a low-frequency point. Model matching at low-frequency points yields a linear algebraic equation and the solution to these equations yields the controller parameters. Findings Simulations have been conducted on several examples covering high order stable, integrating, double integrating processes with time delay and nonlinear continuous stirred tank reactor. The performance of the proposed scheme has been compared with recently reported work having modified cascade control configurations, sliding mode control, model predictive control and fractional order control. The performance of both the proposed schemes is either better or comparable with the recently reported methods. However, the proposed method based on desired load disturbance rejection performance outperforms among all these schemes. Originality/value The main advantages of the proposed approaches are that they are directly applicable to any order processes, as they are free from time delay approximation and plant order reduction. In addition to this, the proposed schemes are capable of handling a wide range of different dynamical processes in a unified way.

Optimal PID Tuning of PLL for PV Inverter Based on Aquila Optimizer

Phase-locked loop (PLL) is a fundamental and crucial component of a photovoltaic (PV) connected inverter, which plays a significant role in high-quality grid connection by fast and precise phase detection and lock. Several novel critical structure improvements and proportional-integral (PI) parameter optimization techniques of PLL were proposed to reduce shock current and promote the quality of grid connection at present. However, the present techniques ignored the differential element of PLL and did not acquire ideal results. Thus, this paper adopts Aquila optimizer algorithm to regulate the proportional-integral-differential (PID) parameters of PLL for smoothing power fluctuation and improving grid connection quality. Three regulation strategies (i.e., PLL regulation, global regulation, and step regulation) are carefully designed to systematically and comprehensively evaluate the performance of the proposed method based on a simulation model in MATLAB/Simulink, namely, “250-kW Grid-Connected PV Array”. Simulation results indicate that PLL regulation strategy can effectively decrease power fluctuation and overshoot with a short response time, low complexity, and time cost. Particularly, the Error(P) and the maximum deviation of output power under optimal parameters obtained by PLL strategy are decreased by 418 W and 12.5 kW compared with those under initial parameters, respectively.

Neural Network Algorithm for Stabilizing Mechanized Systems

The article describes an experimental model of stabilization of a mechanized system. The following are shown: a skate; an element of the program code; an algorithm for stabilizing a proportional-integral-differential controller (PID). The experimental model uses the calculation and adjustment of the regulator according to the Ziegler-Nichols method. For the case of applying the neural network approach to the search for equilibrium, the Hopfield neural network is used. The technology of calculating the balancing of the values of the coefficients: proportional, integral, differential components are described. The design of the rolling system is described. The experimental model is designed to identify the balancing range of the rolling system of small-diameter balls. The experimental module balances the ball at a distance of 4.5 to 7 cm (SW-range). The shortcomings of the experimental model of stabilization of the mechanized system are revealed. The analysis of experimental studies of spacecraft stabilization is carried out. It is determined that it is advisable to use the mathematical tools of the sixth-order Butterworth polynomial in the training of a neural network. Complex neural network calculations make it possible to calculate the stabilization coefficients of the spacecraft when the coordinate system does not coincide with the axes of inertia. An overview of the authors ' research on the use of intelligent quality control systems for the production of medicines is given. An overview of neural network solutions for stabilizing the turning angle of high-speed cars is given. The expediency of selecting the stabilization coefficients of a proportional-integral-differential regulator by a trained neural network for various rolling ranges is proved.

Pipeline Leak Detection and Estimation Using Fuzzy PID Observer

A pipe is a ubiquitous product in the industries that is used to convey liquids, gases, or solids suspended in a liquid, e.g., a slurry, from one location to another. Both internal and external cracking can result in structural failure of the industrial piping system and possibly decrease the service life of the equipment. The chaos and complexity associated with the uncertain behaviour inherent in pipeline systems lead to difficulty in detection and localisation of leaks in real time. The timely detection of leakage is important in order to reduce the loss rate and serious environmental consequences. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. The objective of this paper is to propose a new leak detection method based on an autoregressive with exogenous input (ARX) Laguerre fuzzy proportional-integral-derivative (PID) observation system. In this work, the ARX–Laguerre model has been used to generate better performance in the presence of uncertainty. According to the results, the proposed technique can detect leaks accurately and effectively.