Subspace Predictor Research Articles

Research on the Optimal Output Power of Proton‐Exchange Membrane Fuel Cell Based on Improved Model Predictive Control Strategy

To achieve optimal power output of proton‐exchange membrane fuel cell (PEMFC) under load variation and noise disturbance, this article proposes a recursive subspace model predictive control based on Laguerre function (RSMPCL) strategy. First, a fractional PEMFC identification model is established, and an optimal power setter is introduced to provide an optimal power value corresponding to the current load. Second, during the predictive process, a subspace predictor is updated in real time where a Householder transformer is applied to decrease the computation time of the frequent QR decomposition, and a variable forgetting factor is introduced to enhance the sensitivity and accuracy of predictive control. In the control process, a Laguerre function is employed to transform the problem of computing the input increment at the next step into calculating the coefficients of the input variables. This transformation reduces the computation time of the control quantities. Finally, load and disturbance tests conducted on a dSPACE semiphysical simulation platform indicate that compared with constrained subspace model predictive control, the proposed RSMPCL strategy can help the fuel cells achieve the optimal power point with control accuracy, robustness, and calculation speed.

Generalized Data–Driven Predictive Control: Merging Subspace and Hankel Predictors

Data–driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems. Two main approaches exist within DPC: the subspace approach, which estimates prediction matrices (unbiased for large data) and the behavioral, data-enabled approach, which uses Hankel data matrices for prediction (allows for optimizing the bias/variance trade–off). In this paper we develop a novel, generalized DPC (GDPC) algorithm by merging subspace and Hankel predictors. The predicted input sequence is defined as the sum of a known, baseline input sequence, and an optimized input sequence. The corresponding baseline output sequence is computed using an unbiased, subspace predictor, while the optimized predicted output sequence is computed using a Hankel matrix predictor. By combining these two types of predictors, GDPC can achieve high performance for noisy data even when using a small Hankel matrix, which is computationally more efficient. Simulation results for a benchmark example from the literature show that GDPC with a reduced size Hankel matrix can match the performance of data–enabled predictive control with a larger Hankel matrix in the presence of noisy data.

An H∞ approach to data‐driven fault estimation, and isolation for Hammerstein‐Wiener systems

AbstractThis article considers the fault estimation problem in a data‐driven setup for a class of nonlinear systems called Hammerstein‐Wiener. The fault estimation problem is converted to a finite‐horizon minimax optimization problem. To estimate faults, a subspace predictor is utilized to predict the output sequence. Our proposed method is robust against disturbances. The proposed method is briefly expressed as an algorithm that can be implemented by MATLAB software. The suggested algorithm is applied to a numerical MIMO example to verify its validity.

Recursive Subspace-based Predictive Control and Its Application to Fault-tolerant Control

This paper investigates the data-driven realization of predictive controllers, and its application to fault-tolerant control. With the aid of subspace identification method, the subspace predictor is first derived in the closed-loop setup. To cope with the change in the process, a recursive algorithm is adopted to recursive update the subspace matrices. The data-driven predictive controller is designed by seeking the solution of the quadratic programming problem, without the information of the predesigned controller. Then, it is applied to fault-tolerant control by implementing the recursive subspace predictor. In the end, the effectiveness of the proposed method is verified by a case study on the three-tank system.

A data-driven bilinear predictive controller design based on subspace method

In this paper, a new data-driven model predictive control (MPC), based on bilinear subspace identification, is considered. The system's nonlinear behavior is described with a bilinear subspace predictor structure in an MPC framework. Thus, the MPC formulation results in a fixed structure objective function with constraints regardless of the underlying nonlinearity. For unconstrained systems, the identified subspace predictor matrices can be directly used as controller parameters. Therefore, we design optimization algorithms that exploit this feature. The open-loop optimization problem of MPC that is nonlinear in nature is solved with series quadratic programming (SQP) without any approximations. The computational efficiency already demonstrated with the current formulation presents further opportunities to enable online control of nonlinear systems. These improvements and close integration of modeling and control also eliminate the intermediate design step, which provides a means for data-driven controller design in generalized predictive controller (GPC) framework. Finally, the proposed control approach is illustrated with a verification study of a nonlinear continuously stirred tank reactor (CSTR) system. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Fault-Tolerant Subspace Predictive Control Applied to a Boeing 747 Model

This paper presents a fault-tolerant control system based on a combination of predictive control and subspace identification called subspace predictive control. In this control method, the mathematical model used by conventional predictive controllers to predict the future output is replaced by a subspace predictor. Because the subspace predictor is continuously updated in a closed-loop setting based on new input-output data, it can naturally adapt the controller after a fault has occurred. This property is very useful for fault-tolerant control because faults might be unanticipated. A novel feature of the presented subspace predictive control algorithm is that the predictor is recursively updated in a computationally efficient way. A multiple-model fault classification scheme is used to distinguish between anticipated and unanticipated faults. For anticipated faults the control system is configured such that it can accommodate these faults more quickly. The proposed fault-tolerant control system is evaluated on a detailed model of a Boeing 747.

Persistency of excitation in subspace predictive control

This paper presents a method that ensures persistency of excitation for subspace predictive control. This control method is characterized by the combination of a predictive control law with a subspace predictor. The subspace predictor is continuously being adapted to the controlled system by using input-output data from this system. For this purpose the input-output data should be persistently exciting. In this paper a method is proposed to ensure persistency of excitation by adding a term to the cost function used by the predictive control law. This term is designed such that only the least excited directions of the input space are additionally excited. An advantage of the method is that the optimization problem that needs to be solved for the predictive controller can still be solved by using quadratic programming. The proposed excitation method is evaluated in simulation on a detailed nonlinear model of a transport aircraft. The simulation results clearly show the usefulness of the proposed method.

The Selective Random Subspace Predictor for Traffic Flow Forecasting

Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow data may be incomplete (partially missing or substantially contaminated by noises), which will aggravate the difficulties for traffic flow forecasting. In this paper, a new approach, termed the selective random subspace predictor (SRSP), is developed, which is capable of implementing traffic flow forecasting effectively whether incomplete data exist or not. It integrates the entire spatial and temporal traffic flow information in a transportation network to carry out traffic flow forecasting. To forecast the traffic flow at an object road link, the Pearson correlation coefficient is adopted to select some candidate input variables that compose the selective input space. Then, a number of subsets of the input variables in the selective input space are randomly selected to, respectively, serve as specific inputs for prediction. The multiple outputs are combined through a fusion methodology to make final decisions. Both theoretical analysis and experimental results demonstrate the effectiveness and robustness of the SRSP for traffic flow forecasting, whether for complete data or for incomplete data

Subspace based direct adaptive ℋ︁∞ control

AbstractThis paper presents extensions of the model free subspace based ℋ︁∞ control approach. In particular, a fast algorithm for computing a subspace predictor from experimental data is introduced. The paper also describes a fast algorithm for updating the subspace predictor when new experimental data are available. These algorithms can be applied in conjunction with model free subspace based ℋ︁∞ control in order to create a novel adaptive control law. A defining feature of this control law is that an estimate of achievable performance is maintained throughout the operation of the closed‐loop system. These algorithms are used to demonstrate the closed‐loop control of a flexible structure. Copyright © 2001 John Wiley & Sons, Ltd.