GPC Algorithm and Queuing-selecting for Networked Level Control

The networked control systems (NCSs) have nowadays applications ranging from factory and home automation, to automotive and avionic control systems and to military and spatial applications. Their widely inclusion in the industry was mainly due to the inherent advantages, e.g. lower costs, simpler installation and maintenance, higher reliability and greater flexibility. Although the subject was extensively studied during the last decades, the effect of introducing a communicatio network in the closed-loop control system needs to be more carefully studied. The main disadvantage is considered to be the network-induced time-varying delay which can degrade the control systems performances and can even lead to the instability of the networked control system. This paper studies and analyses the effect of different modeling methods for the network-induced time-varying delays on the networked control systems performances. The modeling methods are used in the design phase of a generalized predictive control (GPC) strategy and the performance evaluation is performed on a typical networked application, i.e., direct current (DC) motor controlled through an Ethernet network.

The use of a communication network in a control loop has gained increasing attention in recent years, because it can offer many advantages. One of major challenges in this so-called networked control systems (NCS) is the network-induced delay effect in the control loop. In this paper, a networked control strategy based on generalized predictive control (GPC) is presented, which can compensate for the communication network delays. A stability analysis method for the control strategy is proposed. The simulation results illustrate effectiveness of this method.

This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping.

Robust generalised predictive control with terminal output weightings

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional (2D) general predictive iterative learning control (2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control (ILC) algorithm for a 2D system and designed in the generalized predictive control (GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

In this paper, the robust H∞ control problem is developed for a class of linear time-varying networked control system (NCS) with uncertainties and external disturbance. In network, the network-induced delay and packet dropouts are simultaneously considered. Firstly, a discrete-time switched system model is established based on the signals transmission delay in network. Then, a time-varying H∞ controller is designed. Such the controller can vary according to delay in network. Furthermore, by constructing delay-dependent Lyapunov-Krasovskii functional, a sufficient condition is given to guarantee that the closed-loop system is stochastically stable with an H∞ disturbance attenuation level in terms of nonlinear matrix inequalities. In order to solve the controller gain, the cone complementarity linearization (CCL) algorithm is employed. Finally, a networked DC motor control system is analyzed to illustrate the effectiveness of the proposed method.

In this paper, a new GPC (Generalized Predictive Control) algorithm which always guarantees the stability of a closed loop system is proposed. The feedback part of the presented predictive control is shown to be equivalent to that of RHTC (Receding Horizon Tracking Control) with a terminal state constraint. The stability of the presented control law is proved and the simplest form of the stabilizing GPC is presented, which can be widely applied to various areas. The proposed control is different from Clarke's GPC in the sense that the latter guarantees stability for an indefinitely large horizon, while the former guarantees the stability for any horizon greater than the system order.

Connecting a complex control system with various sensors, actuators, and controllers as a networked control system by a shared data network can effectively reduce complicated wiring connections. This system is also easy to install and maintain. The trend is to use networked control systems for time-sensitive applications, such as remote DC motor actuation control. The performance of a networked control system can be improved if the network can guarantee quality-of-service (QoS). Due to time-varying network traffic demands and disturbances, QoS requirements provided by a network may change. In this case, a network has to reallocate its resources and may not be able to provide QoS requirements to a networked control application as needed. Therefore, the application may have to gracefully degrade its performance and perform the task as best as possible with the provided network QoS. This paper proposes a novel approach for networked DC motor control systems using controller gain adaptation to compensate for the changes in QoS requirements. Numerical and experimental simulations, and prototyping, are presented to demonstrate the feasibility of the proposed adaptation scheme to handle network QoS variation in a control loop. The effective results show the promising future of the use of gain adaptation in networked control applications.

Design, Analysis and Real-time Implementation of Networked Predictive Control Systems

This paper is concerned with predictive control of discrete-time networked control systems (NCSs) based on a dual-rate sampling switched observer, where the multiple output signals of the controlled plant are sampled using a dual-rate sampling mechanism. To reduce the effect of the bilateral network-induced uncertainties on NCSs, a novel predictive control protocol is proposed to compensate for the bilateral network-induced delays and packet dropouts. First, a dual-rate sampled-data-based switched observer with a fixed switching law is designed to estimate the state of the controlled plant, where the switching law is determined by the dual-rate sampled instants. Second, an estimated-state-based predictive controller is designed by synchronizing the control inputs used by the state predictor and the controlled plant, and the networked predictive control system is formulated as a threshold-error-dependent delay system model, where two associated but different event-triggered transmission schemes are introduced to determine whether the estimated states or the control packet should be transmitted or not. Sufficient conditions for co-designing the event-triggered matrix and the predictive controller gain are derived by using the Lyapunov-Krasovskii functional method. Finally, it is shown through a numerical example that the proposed method in this paper is effective for ensuring better system performance.

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

GPC FOR NON-UNIFORMLY SAMPLED SYSTEMS BASED ON THE LIFTED MODELS

Networked Control System (NCS) is a class of feedback control system forming a closed loop through the communication network. Due to the nodes conflicts, poor network stability, limited data bandwidth and other uncontrollable factors in the transmission, network-induced delay is the main factor reducing system performance and stability. Studying the state feedback rules of Long Delay Network Control system, the author proposes Stability and stabilization method for Random Long Delay Network Control. The method models random long delay closed-loop network control system, compensate the delay and complete the network stability and stabilization. Finally, a numerical example is given to verify the validity of the conclusion.

A kind of soft-sensing method of gauge of strip based on fast generalized predictive control(GPC) algorithm is presented. Based on in-depth studying simplified calculating principle of fast GPC algorithm, and using fast algorithm’s characteristics of small calculation amount and fast calculating speed the method establishes the real-time online soft-sensing model of the strip gauge. Combining GPC’s advantages of multi-steps prediction, rolling horizon optimization and feedback compensation etc, it can better solves problems of measurement delay caused by gage installation location and measurement errors caused by tension time-varying and equipment tremor. The test results show that the method can real-time, on-line measures the exit thickness of rolling strip accurately, and provides reliable guarantee for optimized control of shape and gauge. It can also be used in the key parameters measurement of other complex industrial process.

Generalized predictive controller (GPC) has robust performance when faced to control non-minimum phase and open-loop unstable processes. GPC has been originally developed with linear predictor models which leads to a formulation that can be solved analytically. If a nonlinear model is used then nonlinear optimization and nonlinear modeling techniques are necessary. This paper presents a new method of multivariable GPC design for nonlinear systems. The proposed GPC design incorporates the neural network model based on radial basis function neural network and optimization methodology of cost function by Lamarckian hybrid method using evolutionary programming and Nelder-Mead’s simplex algorithm. Steps of the control law implementation and intelligent procedure are discussed. Simulation results on a nonlinear MIMO (multi-input multi-output) system are presented to show the effectiveness of the neuro-evolutionary GPC method. The new GPC design is applicable for improve the approximation of nonlinear systems with unknown interactor matrix using neural network model and an efficient control law optimization task by simplex-evolutionary hybrid methodology.