Fractional-Order Time Delay Compensation in Deadbeat Control for Power Converters

Deadbeat control scheme is widely implemented in the control of power electronics and electrical drives, which is of simplification, rapidity and flexibility. However, owing to its sensitive to model uncertainties and unmodeled dynamics, the practical control performance is severely degraded and sometimes even unstable. Uncertain time delay is a typical case of model uncertainties, which severely deteriorates the control accuracy and dramatically reduce the system stability margin of deadbeat control. In this paper, the time delay effects on the control performance and system stability are investigated. A fractional-order Smith Predictor based solution is proposed to compensate arbitrary time delay with high accuracy, simple structure, and good robustness. The composite control scheme offers accurate time delay compensations in digital implementation and considerably enhances the robustness of the control system, which will effectively promote widespread applications of the deadbeat scheme. An application example of three-phase inverter system is explored to comprehensively illustrate the feasibility and effectiveness of the proposed scheme

Adaptive-smith predictor for controlling an automotive electronic throttle over network

The paper presents a control strategy for an automotive electronic throttle, a device used to regulate the power produced by spark-ignition engines. Controlling the electronic throttle body is a difficult task because the throttle accounts strong nonlinearities. The difficulty increases when the control works through communication networks subject to random delay. In this paper, we revisit the Smith-predictor control, and show how to adapt it for controlling the electronic throttle body over a delay-driven network. Experiments were carried out in a laboratory, and the corresponding data indicate the benefits of our approach for applications.Peer ReviewedPostprint (published version

Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.Comment: Accepted for the conference -- The International Conference on Computational Science ICCS202

Predictive Input Delay Compensation with Grey Predictor for Networked Control System

The performance of networked control systems is affected strictly by time delay. Most of the literature in the area handle the problem from a stability perspective. However, stability optimized algorithms alone are not sufficient to reduce synchronization problems caused by time delay between the action and reaction in geographically distant places, and the effect and performance of other system components should also be taken into account. In teleoperation applications the reference is often provided by a human, known as the operator, and due to the nature of the human system, references provided by the human operator are of a much lower bandwidth when compared to common control reference inputs. This paper focuses on the operator, and proposes an approach to predict the manipulator’s motion (created by the operator) ahead of time with an aim to reduce the time delay between the master and slave manipulator trajectories. To highlight the improvement offered by the developed approach, hereby called Predictive Input Delay Compensator (PIDC), we compare the performance with the only other study in the literature that handles this problem using the Taylor Series approach. The performance of these two approaches is evaluated experimentally for the forward (control) path on a PUMA robot, manipulated by a human operator and it has been demonstrated that the efficient latency in the forward path is decreased by 100ms, on average, reducing the forward latency from 350ms to 250ms

Adaptive-Smith Predictor for Controlling an Automotive Electronic Throttle over Network

The paper presents a control strategy for an automotive electronic throttle, a device used to regulate the power produced by spark-ignition engines. Controlling the electronic throttle body is a difficult task because the throttle accounts strong nonlinearities. The difficulty increases when the control works through communication networks subject to random delay. In this paper, we revisit the Smith-predictor control, and show how to adapt it for controlling the electronic throttle body over a delay-driven network. Experiments were carried out in a laboratory, and the corresponding data indicate the benefits of our approach for applications

A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach

A real-time bilateral teleoperation control system over imperfect network

Functionality and performance of modern machines are directly affected by the implementation of real-time control systems. Especially in networked teleoperation applications, force feedback control and networked control are two of the most important factors and determine the performance of the whole system. In force feedback control, generally it is necessary but difficult and expensive to attach sensors (force/torque/pressure sensors) to detect the environment information in order to drive properly the feedback force. In networked control, there always exist inevitable random time-varying delays and packet losses, which may degrade the system performance and, even worse, cause the system instability. Therefore in this chapter, a study on a real-time bilateral teleoperation control system (BTCS) over an imperfect network is discussed. First, current technologies for teleoperation as well as bilateral teleoperation control systems are briefly reviewed. Second, an advanced concept for designing a bilateral teleoperation networked control (BTNCS) system is proposed and the working principle is clearly explained. Third, an approach to develop a force-sensorless feedback control (FSFC) is proposed to simplify the sensor requirement in designing the BTNCS while the correct sense of interaction between the slave and environment can be ensured. Forth, a robust adaptive networked control (RANC) -based master controller is introduced to deal with control of the slave over the network containing both time delays and information loss. Case studies are carried out to evaluate the applicability of the suggested methodology