Open-loop controller

About: Open-loop controller is a research topic. Over the lifetime, 16148 publications have been published within this topic receiving 224014 citations. The topic is also known as: non-feedback controller & open-loop control law.

Hybrid fuzzy control of robotics systems

Abstract: This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.

A self-paced fuzzy tracking controller for two-dimensional motion control

Abstract: A heuristic controller is presented that takes the form of a set of fuzzy linguistic rules. Simulations show that the fuzzy logic controller (FLC) yields better results than the conventional PD controller. A self-paced fuzzy tracking controller (SPFTC) designed for two-dimensional path tracking is also presented. The SPFTC adjusts the tracking speed in accordance with contour conditions such as curvature; the fuzzy-rule-based adjustment of the tracking speed improves performance in terms of tracking precision and travel time. Advantages of the FLC and SPFTC are demonstrated by a simulation study. >

State-Variable Steady-State Analysis of a Controlled Current Induction Motor Drive

Abstract: The exact equations defining steady-state operation of a controlled current induction motor drive system are derived by solving the system state equations in the stationary reference frame. These equations, which assume ideal current filtering, eliminate the difficulties involved in taking derivatives of discontinuous currents by defining a pair of pseudocurrent variables. Effects of saturation are included by using the slope ratio method. Electromagnetic torque and current pulsations are computed for various load conditions, and experimental confirmation of the calculated results is made. Similarities and differences to voltage controlled characteristics are presented. It is shown that normal open-loop operation occurs on the unstable side of the torque-slip characteristic necessitating the use of feedback control for stable operation.

The Advantages of PID Fuzzy Controllers Over The Conventional Types

Abstract: Fuzzy logic controllers (FLC's) have the following advantages over the conventional controllers: they are cheaper to develop, they cover a wider range of operating conditions, and they are more readily customizable in natural language terms. A self-organizing fuzzy controller can automatically refine an initial approximate set of fuzzy rules. Application of PI-type fuzzy controller increases the quality factor. In this paper, the voltage raising type-pulse controller is considered. Two types of fuzzy controllers used for the control of boost converter are investigated; the simulation results confirm the above mentioned advantages. In order to prove the dynamic characteristics of the PID fuzzy controller being fast and robust, simulation studies using PSIM program are carried out and compared to the results of the conventional loop gain design method for which MATLAB program is used.

Fuzzy neural network sliding-mode position controller for induction servo motor drive

Abstract: A sliding-mode controller with an integral-operation switching surface is adopted to control the position of an induction servomotor drive. Moreover, to relax the requirement for the bound of uncertainties, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which the FNN is utilised to estimate the bound of uncertainties in real-time. The theoretical analyses for the proposed FNN sliding-mode controller are described in detail. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Simulation and experimental results show that the proposed FNN sliding-mode controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance. Furthermore, compared with the sliding-mode controller, smaller control effort results, and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller.