Time-and event-driven communication process for networked control systems: A survey

Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Feedback control of unsupported standing in paraplegia. Part I: optimal control approach

This is the first of a pair of papers which describe an investigation into the feasibility of providing artificial balance to paraplegics using electrical stimulation of the paralyzed muscles. By bracing the body above the shanks, only stimulation of the plantarflexors is necessary. This arrangement prevents any influence from the intact neuromuscular system above the spinal cord lesion. Here, the authors extend the design of the controllers to a nested-loop LQG (linear quadratic Gaussian) stimulation controller which has ankle moment feedback (inner loops) and inverted pendulum angle feedback (outer loop). Each control loop is tuned by two parameters, the control weighting and an observer rise-time, which together determine the behavior. The nested structure was chosen because it is robust, despite changes in the muscle properties (fatigue) and interference from spasticity

Application of multirate digital filter banks to wideband all-digital phase-locked loops design

A new class of architecture for all-digital phase-locked loops (DPLL's) is presented in this article. These architectures, referred to as parallel DPLL (PDPLL), employ multirate digital filter banks (DFB's) to track signals with a lower processing rate than the Nyquist rate, without reducing the input (Nyquist) bandwidth. The PDPLL basically trades complexity for hardware-processing speed by introducing parallel processing in the receiver. It is demonstrated here that the DPLL performance is identical to that of a PDPLL for both steady-state and transient behavior. A test signal with a time-varying Doppler characteristic is used to compare the performance of both the DPLL and the PDPLL

A new alternating predictive observer approach for higher bandwidth control of dual-rate dynamic systems

Dual-rate dynamic systems consisting of a sensor with a relatively slow sampling rate and a controller/actuator with a fast updating rate widely exist in control systems. The control bandwidth of these dual-rate dynamic systems is severely restricted by the slow sampling rate of the sensors, resulting in various issues like sluggish dynamics of the closed-loop systems, poor robustness performance. A novel alternating predictive observer (APO) is proposed to significantly enhance the control bandwidth of a generic dual-rate dynamic systems. Specifically, at each fast controller/actuator updating period, we will first implement the prediction step by using the system model to predict the system output, generating a so-called virtual measurement, when there is no output measurement during the slow sampling period. Subsequently, the observation step is carried out by exploiting this virtual measurement as informative update. An APO-based output feedback controller with a fast updating rate is developed and rigorous stability of the closed-loop system is established. The superiority of the proposed method is demonstrated by applying it to control a permanent magnet synchronous motor system.</p

Multirate input based quasi-sliding mode control for permanent magnet synchronous motor

Permanent magnet synchronous motor field oriented control system often uses dual-loop (speed and current) cascade structure, and the dynamics speeds of the two loops mismatch. The motor’s mechanical and electrical subsystems have the typical multirate characteristics. Based on the multirate control theory, this paper proposes multirate input quasi-sliding mode algorithm for the speed control loop. Under the situation of the output data loss, the proposed algorithm builds the extended input vector with the output prediction information. Due to the extended input vector, the proposed algorithm reduces the system steady state chatterring, and then improves the performance of the whole system. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part