CARIMA Model Research Articles

HNN-based generalized predictive control for turbofan engine direct performance optimization

The aero-engine performance optimization control is to find the appropriate control variables to maximize or minimize the comprehensive index of one or more performance parameters while ensuring its safe operation. Traditional performance seeking control is mainly based on indirect control, with complex structure and large error. This paper develops an HNN (Hopfield Neural Network)-based generalized predictive control strategy for turbofan engine direct performance optimization. With a Hopfield network as the optimization algorithm and a CARIMA model based on generalized prediction criterion as the optimization model, an optimal controller is designed. Together with the identification module and the self-adaptive module, an integrated performance optimization control system is formed. Namely, the adaptive model calculates the unmeasurable performance parameters of the engine for the CARIMA model identification. Then HNN performs the iterative operation to obtain the optimal control variables for a given objective. The main contribution of this paper is to propose a direct performance control structure to avoid the nonlinear conversion of control reference; meanwhile, the linear CARIMA model is combined with HNN algorithm to build a neural network for solving constrained nonlinear programming problems, and mutation operation is introduced to improve its global search capability, which is evident in the minimum turbine temperature optimization mode. The method is evaluated on a turbofan model from the aspects of model verification, performance optimization control simulation at multiple operating points and under degeneration conditions. The results confirm the satisfactory performance of the proposed strategy at the selected points and conditions. And compared with the traditional algorithms, the computer calculation time is greatly shortened.

Sticking Fault Detecting Method for CARIMA Model

This paper proposes a sticking fault detecting method for controlled auto-regressive integrated moving average model (CARIMA) which detect the sticking fault of control input and feedback signal. It consists of model estimation using recursive least square method with the forgetting factor and fault detection. In the fault detection, an evaluation function is introduced, and it generates a fault signal from the input and output data. Numerical simulations are performed, and it is shown that this method can detect the sticking fault.

Research and Simulation of Electromagnetic Voltage-Regulated Soft Starter Based on Predictive Control

Aiming at the problems of large starting current and unsmooth starting of asynchronous motor, an electromagnetic voltage-regulated soft start control method based on predictive control is proposed. The model of motor soft starter based on predictive control algorithm is established. The control principle of predictive control algorithm is analyzed. The CARIMA model is used to adjust the parameters of motor starting process. With the help of MATLAB, the motor direct start model, the electromagnetic control soft starter model based on PID control algorithm and predictive control algorithm are simulated. The results show that the starting current waveform of the electromagnetic voltage regulator soft starter based on the predictive control algorithm is relatively flat, and the control algorithm can achieve a smooth start of the motor.

Sticking Fault Detecting Method for CARIMA Model

Sticking Fault Detecting Method for CARIMA Model

Autonomous takeoff control system design for unmanned seaplanes

Unmanned seaplanes are special kinds of fixed-wing airplanes which can achieve autonomous takeoff and landing on water. Due to complicated hydrodynamic forces and unpredictable sea states, researches on modeling, dynamic analysis and controller design are still facing great challenges. In this paper, the dynamic characteristics and motion stability of the unmanned seaplane are firstly analyzed based on a nonlinear mathematical model. The proposed autonomous takeoff control system consists of two main parts: T-S fuzzy identification and generalized predictive control (GPC). A linear CARIMA model obtained through T-S fuzzy identification is used to represent the dynamic characteristics in different motion stages. Wave forecasting is considered in the GPC algorithm to improve the anti-waves capability and avoid unstable phenomena in high sea states. Simulations are performed in three different wave conditions, including calm water, regular wave and irregular wave. Moreover, GPC controller is compared with other disturbance rejection control solutions. The simulation results show that the proposed controller has good performances for autonomous takeoff of the unmanned seaplane.

Adaptive Generalized Predictive Control on Electro Hydraulic Servo Feeding System of Band Saw

In view of the shortcomings of band saw feeding system on position and micro-feeding speed tracking response characteristics, an electro-hydraulic servo feeding system of band saw is designed and its difference mathematical model is presented. Then, a new adaptive generalized predictive controller (AGPC) for the electro-hydraulic servo feeding system is provided. The controller identifies the parameters of the system model by system control input and output on line. Through minimizing performance evaluation function J which is based on j-step ahead predictive output of CARIMA model, system control variable can be got. The simulation and experiment application show that the proposed controller has better position precision and feeding tracking performances than the traditional PID controller and can well address feeding process of band saw.

T‐S fuzzy model based generalized predictive control of vehicle yaw stability

Purpose – The purpose of this paper is to design a steering control for vehicles to protect the vehicle from spin and to realize improved cornering performance.Design/methodology/approach – The improved cornering performance is realized based on Takagi‐Sugeno fuzzy model and generalized predictive control (GPC). A new approach to establish model of the vehicle is presented on the basis of fuzzy neural network. The network which inputs and outputs are composed of five layers of forward structure is utilized to build the structure and parameters of T‐S fuzzy model through learning from training data. In this way, the vehicle dynamic system is divided into many linear sub‐systems, and the system output is the weighted‐sum of these sub‐systems' outputs. A CARIMA model can be derived from the presented fuzzy model, and GPC is applied to deal with the control problem of vehicle stability.Findings – Vehicle model can be divided into local linear models, corresponding controller can be developed. Simulation resul...

Research on the Identification of Tubular-Furnace Based on CARIMA Model

Atmospheric and vacuum furnace is a complex control object, in particular, combustion system and multi-branch feeding the outlet temperature control system is interconnected multiple-input and multiple-output coupling multivariable time-varying systems. Using multivariable decoupling control methods will have better control effect, but in fact, because of the nonlinear time-varying systems, and other factors, it is difficult to obtain more accurate mathematical model of the system. Based on CARIMA model, a distributed lags model is constructed in this paper. In the numerical simulation, the proposed model has a excellent recognition result, can achieve an arbitrary precision according to actual requirements.

Generalized Predictive Control Design for a Nonlinear Hydraulic System

The paper deals with the design of optimization predictive control algorithms, which are based on the input-output ARX and CARIMA models of the dynamic systems. The paper also deals with the simulation verification of predictive control algorithms on a nonlinear hydraulic system with different settings of various control algorithms parameters, with a system subject to noise, or disturbance. There is mentioned a brief theoretical derivation and the particular formulas for concrete predictive control methods in the paper. The paper includes a flow chart diagram for predictive control methods programming and the control structure, where predictive control algorithms were used in nonlinear hydraulic system control. Nonlinear differential equations, which express hydraulic system’s dynamic are presented, too. The results of generalized predictive control algorithms comparison are expressed by the control action and the hydraulic system’s output in a graphs form.

Lean Limit and Emissions Improvement for a Spark-Ignited Natural Gas Engine Using a Generalized Predictive Control (GPC)-Based Air/Fuel Ratio Controller

The effects of the different air/fuel ratio (AFR) controller on lean limit and emission characteristics of a compressed natural gas (CNG) engine have been studied. The CARIMA model for the CNG engine was established, and a generalized predictive control (GPC)-based adaptive AFR controller was built by Matlab/Simulink. A simulation model of the engine was established by GT-Power. The experimental investigation was conducted on a six-cylinder multipoint injection lean-burn turbocharged spark-ignition (SI) CNG engine. The results of the simulation and experiment show that, in comparison to the proportional-integral-derivative (PID) algorithm, the GPC algorithm has not only better dynamic behavior but also better adaptability to the disturbances of the system, especially at transient conditions. Using the GPC controller, the lean misfire is improved because of the smaller overshoot of the excess air ratio and the operating range of the engine is extended. As a result, the emissions decrease slightly.

Generalized Predictive Control Scheme for the Internet-based Telerobot Control System

The variable time delay and the packet loss in the Internet degrade seriously the performance of Internet based teleoperation system, and even make the system unstable. To overcome this problem, a novel control architecture of the Internet-based teleoperation system is proposed. The reference information signed with time stamp at master site is transmitted to the slave site and then feeds back to the operator, so the operator can get the round-trip-timedelay(RTT) and then predict the next RTT making use of the multivariate linear regression algorithm to analyze the preceding RTT which has been got. A generalized predictive control(GPC) strategy using CARIMA model of the lineared slave robot is designed at slave site to control the telerobot. Consequently, the operation performance of the system is improved by using this method. Simultaneously, the GPC controller generates the redundant control information which can diminish the influence of the packet loss and the large time delay in the Internet to the system. Finally, stability condition is deduced according to the predictive stability theorem. Simulation results show that these strategies can solve efficiently the problem caused by the variable time delay and the packet loss.

Direct Algorithm for Multivariable Generalized Predictive Controller0s Coe±cients of Diagonal CARIMA Model

A direct way for getting controller 0 s coe-cents of multivariable generalized predictive control (MGPC) of diagonal CARIMA model is developed in order to simplify the design and implementation of MGPC. A very concise MGPC controller is obtained by directly manipulating the model predictor of a diagonal multivariable CARIMA model recursively, and the control moves are the product of the controller 0 s coe-cients and set-points, historical input/output data of the plant and predictive errors of the predictor. The controller's coe-cients are determined only by the model parameters and design parameters, and the number of coe-cients only depends on the orders of the model. This method avoids solving Diophantine equations, reduces the computational overhead on line, and simplifles the implementation of MGPC. Its validity is demonstrated by comparative experiment results obtained from a nonlinear liquid system controlled by DCS.

Applying a Generalized Predictive Control Theory to a Carding Autoleveler

Usually, a carding process is difficult to control with classic control algorithms (such as PID) for three main reasons: strong stochastic disturbance, time delays, and parameter variation. In this paper, generalized predictive control (GPC) is introduced to control such a process. First, a CARIMA model is built to describe the carding process, then the GPC control law is derived and used to design the controller for a carding autoleveler. The simulation results show that the GPC controller can greatly reduce a sliver's standard deviation and can reject measured and unmeasured step disturbance. Although this paper mainly involves the design of a feedforward-feedback GPC controller for a carding autoleveler, the methods of modeling and GPC controller design can easily be extended to a feedback case and used to design controllers for other preparatory machines' autolev elers.

Neuro-Fuzzy Generalized Predictive Control of Boiler Steam Temperature

A neuro-fuzzy network predictive approach is introduced to design a control system for boiler steam temperature process. While the nonlinear process is modeled by neuro-fuzzy technique containing local CARIMA model, traditional generalized predictive control method can be extended to nonlinear case in a neuro-fuzzy fashion. The proposed method was found to provide a satisfactory performance over the 200MW superheater steam temperature system.

PROPERTIES OF MGPC DESIGNED IN STATE SPACE

This paper presents some properties regarding the Multivariable Generalized Predictive Controller (MGPC) designed in state space. The authors proposed the design of such a controller under the condition that it gives the same results as the input/output (I/O) formulation of the GPC proposed originally by Clarke. The main reason for redesigning the controller in state space is the power of the analysis tools available. These tools allow some properties to be treated more easily than under (I/O) formulation. These properties include: stability, robustness, closed loop representation, specifications, etc. This paper presents results related to: observability and controllability of the CARIMA model used, closed loop representation, uniqueness and the existence of control law without constraints and closed loop stability without constraints.

State-space approach to stabilizing stochastic predictive control

The design of stabilizing predictive controllers for stochastic discrete-time linear plants is formulated in a state-space framework. The resulting approach, named SSPC (Stabilizing Stochastic Predictive Control), extends straightforwardly to MIMO plants wherein the controlled output is not directly measurable. It is demonstrated that SSPC leads to well known input-output solutions, such as SIORHC (Stabilizing Input/Output Receding Horizon Control) or SGPC (Stable Generalized Predictive Control), when particular state-space realizations of a CARIMA model are adopted. An efficient algorithm for computing the SSPC control law is also presented.

A pole placement adaptive control of a rapid thermal processor

Rapid thermal processing (RTP) techniques are often applied in microelectronic technology. The main characteristics of RTP techniques lie in single wafer treatments, fast thermal kinetics at low thermal cost and reduced contamination risks; moreover, many types of reaction can be realized with one single wafer treatment. Problems of measurement and control of space-time temperature profiles have not yet been solved in a satisfactory way; in particular, classical control algorithms (such as PID control) can give bad dynamics to the system if nominal temperature profiles must be modified. Modelling of the thermal behaviour of the process leads to very complex mathematical models. These are the reasons why adaptive control techniques are chosen. A CARIMA model is an interesting input-output model of the thermal processor, whose parameters can be estimated on-line, using an identification scheme which includes supervisory actions. The adaptive control algorithm is a full-state feedback pole-placement law with an adaptive observer. A partial-state reference model was included in the control law algorithm. This type of control law can be implemented even for non-minimum-phase systems, time-varying dynamics, a wrong assumption about the time delay of the system, and when disturbances on the load exist. Experimental results are presented which show a better performance when using an adaptive control strategy

Application of a gain scheduling generalized predictive controller to a solar power plant

This paper presents a gain-scheduling generalized predictive controller for the distributed collector field of a solar power plant. The controller has been developed by considering CARIMA models of the field, calculated taking into account the frequency response of the plant. Aspects of the modelling, problems encountered and performance of the control scheme are dealt with and illustrated by plant results. A proven non-linear computer model of the collector field has been used to check the controller behaviour before its real implementation, and some simulation results are shown too.

Implementation of self‐tuning generalized predictive controllers for the process industry

AbstractThis paper presents a set of simple Ziegler‐Nichols‐type functions for implementing generalized predictive controllers (GPCs). By using this set of functions, the implementation of GPCs is simplified considerably and the heavy computational requirements needed are avoided. The functions have been obtained for a CARIMA model that is valid for most processes in industry, as is the case of plants whose behaviour can be described in terms of a static gain, time constant and effective dead time.

Using the filtered CARMA and CARIMA models for state-space self-tuning control

Design procedures of state-space self-tuning controllers are described for both the filtered CARMA and CARIMA models. While closely resembling the generalized approach of Clarke-Kanjilal-Mohtadi (CKM), the proposed methods use the minimum possible dimension for the associated implicit-delay state-space model, and practical issues such us feedforward for linearization, regulation, servo-following, disturbance and offset elminution are considered. The resulting conipart-mental design allows the adjustment of tuning parameters to affecl different control characteristics. For the state-space self-tuners based on the filtered CARIMA model, two different forms can be distinguished: the normal form resembles the controller configuration based on the CARMA model, while the integrating form resembles the CKM self-tuner. Satisfactory results are observed in a simulation study of using the proposed self-tuners to control the steam temperature of a solar central receiver.