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.

Resonance Issues and Damping Techniques for Grid-Connected Inverters With Long Transmission Cable

Abstract: An infinite source with series inductance is usually employed as a grid emulator in grid-connected distributed generation systems. Thus, high capacitance of a transmission cable (i.e., underground cable) is too significant to be neglected. As a result, the capacitance and inductance may cause system resonance, which, in turn, challenges system stability. This paper takes offshore wind farm as an example to investigate the resonance issues caused by the submarine transmission cable of the grid-connected generation system. Based on the submarine cable model, a series of considerable resonant peaks is found in the open-loop transfer function of the grid-connected system because of the high-order LC configuration. The resonant peaks are sensitive to the system setup, which is clearly investigated. To overcome the resonances, this paper proposes a cascaded notch-filter-based active damping method to guarantee a good system stability and robustness. Furthermore, the proposed controller employs a proportional-resonant component to reduce the steady-state error of the output current. The simulation and experimental results have validated the findings of resonances and the effectiveness of the proposed controller.

Remote control system for a locomotive using voice commands

Abstract: A remote control system for a locomotive. The remote control system includes a lead controller and follower controller connected to one another over a wireless communication link. The lead controller issues commands over the wireless communication link and those commands are implemented by the follower controller mounted on-board the locomotive. The lead controller is responsive to voice commands uttered by the operator.

Anti-Windup PID Controller With Integral State Predictor for Variable-Speed Motor Drives

Abstract: The windup phenomenon appears and results in performance degradation when the proportional-integral-derivative (PID) controller output is saturated. Integral windup is analyzed on the PI plane, and a new anti-windup PID controller is proposed to improve control performance of variable-speed motor drives and is experimentally applied to the speed control of a vector-controlled induction motor driven by a pulse width-modulated voltage source inverter. The steady-state value of the integral state is predicted while the PID controller output is saturated, and this value is utilized as an initial value of the integral state when the PID controller begins to operate in a linear range. Simulation and experiments result in more similar speed responses against load conditions and step reference change over conventional anti-windup schemes. Control performance, such as overshoot and settling time, is very similar to that determined by PID gain in the linear range.

A new technique for robust control of servo systems

Abstract: The robust controller has very simple structures and can be divided into two separate parts: a servo controller and an auxiliary controller. The two controllers are designed independently. The function of the auxiliary controller is to cancel out the plant uncertainties directly without the use of the high loop gain principle. Interpretation of robot controller as a signal-synthesis adaptive controller is given. Practical implementation issues of the auxiliary controller are discussed. Simulations of a design example with large parameter uncertainty, nonlinearity, and external disturbance are presented to demonstrate the effectiveness of the design technique. This technique is further tested with success in an experimental study of joint position control of a PUMA 560 robot arm. >

PID-Like Neural Network Nonlinear Adaptive Control for Uncertain Multivariable Motion Control Systems

Abstract: A mix locally recurrent neural network was used to create a proportional-integral-derivative (PID)-like neural network nonlinear adaptive controller for uncertain multivariable single-input/multi-output system. It is composed of a neural network with no more than three neural nodes in hidden layer, and there are included an activation feedback and an output feedback, respectively, in a hidden layer. Such a special structure makes the exterior feature of the neural network controller able to become a P, PI, PD, or PID controller as needed. The closed-loop error between directly measured output and expected value of the system is chosen to be the input of the controller. Only a group of initial weights values, which can run the controlled closed-loop system stably, are required to be determined. The proposed controller can update weights of the neural network online according to errors caused by uncertain factors of system such as modeling error and external disturbance, based on stable learning rate. The resilient back-propagation algorithm with sign instead of the gradient is used to update the network weights. The basic ideas, techniques, and system stability proof were presented in detail. Finally, actual experiments both of single and double inverted pendulums were implemented, and the comparison of effectiveness between the proposed controller and the linear optimal regulator were given.