scholarly journals Fuzzy control with estimated variable sampling period for non-linear networked control systems: 2-DOF helicopter as case study

2011 ◽  
Vol 34 (7) ◽  
pp. 802-814 ◽  
Author(s):  
PE Méndez-Monroy ◽  
H Benítez-Pérez
Keyword(s):  
Control Systems ◽  
Fuzzy Controller ◽  
Networked Control Systems ◽  
Sampling Period ◽  
Networked Control ◽  
System Stability ◽  
Control Input ◽  
Variable Sampling ◽  
Wide Range ◽  
Non Linear

This paper presents a fuzzy controller for class of non-linear networked control systems; the varying time delays and packet loss are taken as a variable sampling period of the system. The variable sampling period is estimated using a time stamped and probability density function. A fuzzy model smoothly switches to estimate the system state; the antecedent input is the estimated sampling period and the consequent part is formed by linear models discretized with specific sampling periods. The fuzzy controller generates a control input using the estimated states to ensure system stability for a wide range of sampling periods. A two-degree-of-freedom helicopter is used to show the applicability and effectiveness of the controller with robustness to traffic.

scholarly journals Active-Varying Sampling-Based Fault Detection Filter Design for Networked Control Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Long Wang ◽  
Tian-Bao Wang ◽  
Wei-Wei Che
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Networked Control Systems ◽  
Filter Design ◽  
Sampling Period ◽  
Networked Control ◽  
Packet Dropouts ◽  
Fault Detection Filter ◽  
Simulation Results ◽  
Detection Filter

This paper is concerned with fault detection filter design for continuous-time networked control systems considering packet dropouts and network-induced delays. The active-varying sampling period method is introduced to establish a new discretized model for the considered networked control systems. The mutually exclusive distribution characteristic of packet dropouts and network-induced delays is made full use of to derive less conservative fault detection filter design criteria. Compared with the fault detection filter design adopting a constant sampling period, the proposed active-varying sampling-based fault detection filter design can improve the sensitivity of the residual signal to faults and shorten the needed time for fault detection. The simulation results illustrate the merits and effectiveness of the proposed fault detection filter design.

scholarly journals Comprehensive Control of Networked Control Systems with Multistep Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Jie Jiang ◽  
Changlin Ma
Keyword(s):  
Time Delay ◽  
Control Systems ◽  
Networked Control Systems ◽  
Sampling Period ◽  
Networked Control ◽  
Control Methods ◽  
Delay Compensation ◽  
Linear Quadratic ◽  
Long Time ◽  
Time Delay Compensation

In networked control systems with multi-step delay, long time-delay causes vacant sampling and controller design difficulty. In order to solve the above problems, comprehensive control methods are proposed in this paper. Time-delay compensation control and linear-quadratic-Guassian (LQG) optimal control are adopted and the systems switch different controllers between two different states. LQG optimal controller is used with probability1-αin normal state, which is shown to render the systems mean square exponentially stable. Time-delay compensation controller is used with probabilityαin abnormal state to compensate vacant sampling and long time-delay. In addition, a buffer window is established at the actuator of the systems to store some history control inputs which are used to estimate the control state of present sampling period under the vacant sampling cases. The comprehensive control methods simplify control design which is easier to be implemented in engineering. The performance of the systems is also improved. Simulation results verify the validity of the proposed theory.

scholarly journals Stability and Time Delay Tolerance Analysis Approach for Networked Control Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ashraf F. Khalil ◽  
Jihong Wang
Keyword(s):  
Control System ◽  
Time Delay ◽  
Control Systems ◽  
Networked Control Systems ◽  
Networked Control System ◽  
Networked Control ◽  
Finite Difference Approximation ◽  
System Stability ◽  
Delay Tolerance ◽  
Maximum Time

Networked control system is a research area where the theory is behind practice. Closing the feedback loop through shared network induces time delay and some of the data could be lost. So the network induced time delay and data loss are inevitable in networked control Systems. The time delay may degrade the performance of control systems or even worse lead to system instability. Once the structure of a networked control system is confirmed, it is essential to identify the maximum time delay allowed for maintaining the system stability which, in turn, is also associated with the process of controller design. Some studies reported methods for estimating the maximum time delay allowed for maintaining system stability; however, most of the reported methods are normally overcomplicated for practical applications. A method based on the finite difference approximation is proposed in this paper for estimating the maximum time delay tolerance, which has a simple structure and is easy to apply.

scholarly journals Stabilization for Networked Control Systems with Random Sampling Periods

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Yuan Li ◽  
Qingling Zhang ◽  
Shuanghong Zhang ◽  
Min Cai
Keyword(s):  
Markov Chains ◽  
Control Systems ◽  
Random Sampling ◽  
Networked Control Systems ◽  
Transition Probabilities ◽  
Sufficient Conditions ◽  
Sampling Period ◽  
Networked Control ◽  
Linear Matrix ◽  
Random Delays

This paper investigates the stabilization of networked control systems (NCSs) with random delays and random sampling periods. Sampling periods can randomly switch between three cases according to the high, low, and medium types of network load. The sensor-to-controller (S-C) random delays and random sampling periods are modeled as Markov chains. The transition probabilities of Markov chains do not need to be completely known. A state feedback controller is designed via the iterative linear matrix inequality (LMI) approach. It is shown that the designed controller is two-mode dependent and depends on not only the currentS-Cdelay but also the most recent available sampling period at the controller node. The resulting closed-loop systems are special discrete-time jump linear systems with two modes. The sufficient conditions for the stochastic stability are established. An example of the cart and inverted pendulum is given to illustrate the effectiveness of the theoretical result.

scholarly journals Design of Multi-Rate Multi-Zone Wireless Fuzzy Temperature Control System for Greenhouse Application

2020 ◽  
Vol 26 (7) ◽  
pp. 97-114
Author(s):  
Qays Jebur Sabr
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Networked Control Systems ◽  
Sampling Rate ◽  
Sampling Period ◽  
Networked Control System ◽  
Networked Control ◽  
Temperature Control System ◽  
Temperature Controller ◽  
Input Gain

sensor sampling rate (SSR) may be an effective and crucial field in networked control systems.  Changing sensor sampling period after designing the networked control system is a critical matter for the stability of the system. In this article, a wireless networked control system with multi-rate sensor sampling is proposed to control the temperature of a multi-zone greenhouse. Here, a behavior based Mamdany fuzzy system is used in three approaches, first is to design the fuzzy temperature controller, second is to design a fuzzy gain selector and third is to design a fuzzy error handler. The main approach of the control system design is to control the input gain of the fuzzy temperature controller depending on the current zone and current sensor rate for each zone. Due to the low input gain of the fuzzy controller, the steady state output error of the greenhouse temperature is in the range (0.55 – 11.22) % when the system using five sensors of different sampling rates and in the range (2.43 - 16.74) % when the system using five sensors with the same sampling rates. Next, after designing the fuzzy error handler, this error doesn’t exceed 1.6%, but in most cases it is less than 0.15%.The work is Simulink designed and implemented using Matlab R2012b. The Zigbee wireless network is proposed for the system, it is implemented in Matlab using True time 2.0 library.

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