MULTIVARIABLE MODEL REFERENCE ADAPTIVE CONTROL FOR A TURBOFAN ENGINE

A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.

Multivariable PI Type Generalized Predictive Control

This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder

In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.

A New Approach to Diagnosing Signals from Multivariate EWMA Control Chart

Since Lowry et al. [1992] proposed a multivariate version of theexponentially weighted moving average (EWMA) control chart, the multivariate EWMA control chart hasbecome more and more popular in monitoring production processes, especially in chemical processes.A major advantage of multivariate EWMA statistics is that it is sensitive to small and moderateshifts in the mean vector. However, when a multivariate EWMA chart issues a signal, it is difficultto identify which variable or set of variables is out of control. In this paper, we introduce anew approach to diagnosing signals from a multivariate EWMA control chart. The implementationprocedure is that when the multivariate EWMA control chart issues a signal, we adopt a univariatediagnostic procedure to identify the variables or/and the principal components that caused thesignal.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

Optimal decoupling control system using kernel method

A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the application of kernel method in decoupling multivariable output feedback controllers. Simulation results are presented to show the feasibility of the proposed technique.

Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

Because of coupling,nonlinearity,and uncertainty in a municipal solid waste incineration(MSWI)process,a suitable multivariable controller is difficult to establish under strong disturbance.Additionally,the problems of reducing mechanical wear and energy consumption in the control process should also be considered.To solve these problems,an event-triggered fuzzy neural multivariable controller is proposed in this paper.First,the MSWI object model based on the multiinput multioutput TakagiSugeno fuzzy neural network is established using a data-driven method.Second,a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external disturbance.Third,an event-triggered mechanism is constructed to update the control rate online while ensuring control effects.Then,the stability of the proposed control strategy is proven through the LyapunovⅡtheorem to guide its practical application.Finally,the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing,China.The experimental results show that the proposed control strategy greatly improves the control efficiency,reduces energy consumption by 66.23%,and improves the multivariable tracking control accuracy.

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

A novel model-based multivariable framework for aircraft gas turbine engine limit protection control

Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines.In terms of limit protection control,a novel model-based multivariable limit protection control method,which is achieved by adaptive command reconstruction and multiplecontrol loop selection and switch logic,is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic.Five different combination modes of control loops,which represent the online control loop of last time instant and that of current time instant,is analyzed.Different command reconstructions are designed for these modes,which is based on static gain conversion of amplitude beyond limits by using an onboard model.The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits.The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control,and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated,which demonstrates the effectiveness of the proposed method.

Flatness-Based Control in Successive Loops for Unmanned Aerial Vehicles and Micro-Satellites

The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.The state-space model of(i)unmanned aerial vehicles and(ii)micro-satellites is separated into two subsystems,which are connected between them in cascading loops.Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems.The state variables of the second subsystem become virtual control inputs for the first subsystem.In turn,exogenous control inputs are applied to the first subsystem.The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis.The validity of the control method is confirmed in two case studies:(a)control and trajectories tracking for the autonomous octocopter,(ii)control of the attitude dynamics of micro-satellites.

Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process

A fuzzy super-twisting algorithm sliding mode controller is developed for the dissolved oxygen concentration in municipal wastewater nitrification process. First, a fuzzy neural network(FNN) model is designed to approach the oxygen dynamics with unmeasurable disturbances, then the established model consists of the nominal system model and the modelling error. Second,based on the FNN model, a super-twisting sliding mode controller is employed to stabilize the nominal system and to suppress the modelling error. Moreover, the stability of the system is investigated and an adaption law is applied to ensure the robustness of the closed-loop system. Finally, the comparison experiments on benchmark simulation model no. 2(BSM2) of wastewater treatment show the advantages of the proposed method in multiple-units oxygen concentration control.

Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

Pulsed spray is a useful tool forgranule size control in fluid bed granulation.To improve the quality control of pulsed-spray fluid bed granulation,a combination of in-line near-infrared(NIR)spectroscopy and p「incipal component analysis was used to develop multivariate statistical process control(MSPC)charts.Different types of MSPC charts were developed,including principal component score charts,Hotelling's T2 control charts,and distance to model X control charts,to monitor the batch evolution throughout the granulation process.Correlation optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC modeling.The control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault analysis.The results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external variability.This research demonstrated the application of synchronized NIR spectra in conjunction w让h multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Optimal decentralized control of large scale systems

This paper presents a new optimized decentralized controller design method for solving the tracking and disturbance rejection problems for large-scale linear time-invariant systems, using only low-order decentralized controllers. To illustrate the type of results which can be obtained using the new optimized decentralized control design method, the control of a large flexible space structure is studied and compared with the standard centralized LQR-observer controller. The order of the resultant decentralized controller is much smaller than that of the standard centralized LQR-observer controller. The proposed controller also has certain fail-safe properties and, in addition, it can be five orders of magnitude more robust than the standard LQR-observer controller based on their real stability radii. The new decentralized controller design method is applied to a large flexible space structure system with 5 inputs and 5 outputs and of order 24.

CARMA-model-based j-step-ahead prediction for MIMO systems

The single-input single-output(SISO)j-step-ahead predictor for generalized predictive control(GPC)controllers was traditionally derived using the polynomial approach through the Diophantine equations.An equivalent version of the predictor in a state-space form is available in the literature.In this paper,a z-domain analysis of the multiple input multiple output(MIMO)extension of the state-space predictor is carried out,and then an MIMO j-step-ahead predictor in polynomial form based on the controlled auto-regressive moving average model is derived.The predictor enables us to simplify the GPC algorithm design for multivariable systems.In the SISO case the predictor is just the traditional GPC predictor,therefore this paper gives rigorous proof of the equivalence between the traditional GPC predictor and the state-space predictor.