Network Based Control System Design Research Articles

Analysis and Design of Network-Based Control Systems with Binary Modulation

Network-based control systems have been emerging technologies in control and computer communication fields over the past decade. This paper focuses on the analysis and design of network-based control systems with binary modulation. First, it is shown that different modulations can result in different delays which are inevitable in network-based control systems. The delay can be seen as constant delay when the transmission time is the main consideration. Second, channel noise can result in bit error while bit error is seen as active packet loss in this paper, in this context, the conditions of signal-to-noise ratio (SNR) for binary modulation that can guarantee the stability of systems are obtained according to the proposed algorithm. Third, the system with delay and noisy communication can be modeled as an asynchronous dynamic system (ADS); in addition, the stability is analyzed and controller is designed in terms of Lyapunov function and linear matrix inequality (LMI) scheme. Finally, without loss of generality, numerical simulation demonstrates the effectiveness of the proposed scheme and designed controller based on binary amplitude shift keying (2ASK) modulation.

Variable Selective Control Method for Networked Control Systems

A new method for the design of network-based control systems, together with mathematical tools for closed-loop stability assessment, is proposed. In order to preserve the closed-loop stability under random network time delays, the variable selective control (VSC) methodology is introduced, which combines the idea of variable sampling control with the packet-based control methodologies. In the proposed method, event-driven sensors are employed, which sample the plant output just after a new control input signal is received by the actuator. The advantages are as follows: 1) the latest actual sampling period becomes equal to the most recent time delay, which is known by direct measurement; 2) better estimation of the actual plant states can be performed in the predictive controller; 3) less frequent and shorter packets need to be transferred through the network; and 4) the stability analysis becomes simpler and does not depend on variable-size matrices. In order to deal with packet dropout issue, a simple, yet effective, algorithm is adopted in the controller, where packet losses are considered as variable time delays. In light of this formulation, the networked control system can be considered as a switched linear system and, therefore, previously existing theoretical results are adopted for stability analysis of the VSC method. Simulation studies on a well-known benchmark problem demonstrate the effectiveness of the proposed method.

Robust Fuzzy Neural Network Based Control System for Mechatronic Servo Systems with High Nonlinearity (2nd Report, Analysis on the Stability and Robustness of System)

This paper proposes a novel robust fuzzy-neural network based control system design for the mechatoronic positioning servo systems that have nonlinear characteristics such as friction, backlash, variations of load and system parameters, and unknown disturbances. The proposed fuzzy neural network based control scheme consists of three elements : a) a feedback compensation of velocity and position : b) a feedforward compensation which has inverse dynamics of the PD controlled plant based on neural networks ; and c) a nonlinear deviation compensator based on fuzzy neural networks. This paper formulates the characteristics of the controller design, and defines the stability and the robustness which are the analysis of the system characteristics.

NNSYSID and NNCTRL tools for system identification and control with neural networks

Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview of the design of NNSYSID and NNCTRL.

NNSYSID and NNCTRL - MATLAB Tools for System Identification and Control with Neural Networks

Two MATLAB packages have been implemented: the Neural Network Based System Identification toolbox (NNSYSID) and the Neural Network Based Control System Design Toolkit (NNCTRL). The NNSYSID toolbox has been developed to assist identification of nonlinear dynamic systems and it offers the possibility to work with a number of different nonlinear model structures based on neural networks. The NNCTRL toolkit is an add-on to the NNSYSID toolbox and contains tools for design and simulation of control systems based on neural networks. This paper gives an overview of the contents of NNSYSID and NNCTRL.