Advanced Stochastic Control Systems with Engineering Applications
View this Special IssueResearch Article  Open Access
Xiaosuo Luo, Yongduan Song, "Adaptive Predictive Control: A DataDriven ClosedLoop Subspace Identification Approach", Abstract and Applied Analysis, vol. 2014, Article ID 869879, 11 pages, 2014. https://doi.org/10.1155/2014/869879
Adaptive Predictive Control: A DataDriven ClosedLoop Subspace Identification Approach
Abstract
This paper presents a datadriven adaptive predictive control method using closedloop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closedloop subspace identification algorithm driven by inputoutput data. Taking advantage of transformational system model, the closedloop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closedloop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.
1. Introduction
With the development of industrial technology, the industrial processes become more complex than before and it is more difficult to build the accurate mechanism models of these processes. Hence, the datadriven approach has obtained widespread attention since it emerged. Datadriven control also turns into focus of study. Simply, the datadriven control is a method from data to design controller directly [1, 2]. Model predictive control (MPC) has been attractive for decades in control theory field. It has become more established as the one of the choices for the control architecture in the industry, especially with the improvement of computational capabilities of processors [3–8]. But one drawback of the traditional industrial predictive control is based on inputoutput model, including parametric and nonparametric ones. In order to improve the control performance, a statespace model should be adopted, so the modern filter theory and the design method of controller developed in recent years can play a role [9]. Subspace identification is one of the system identification algorithms for statespace modeling. The control workers may relieve completely from the tedious mechanism modeling and the accurate statespace model can be obtained when there is enough process inputoutput data [10–12]. More attractively, the subspace matrices obtained through the subspace identification algorithm can be used to derive the predictor of predictive controllers, eliminating the intermediate step of process model identification and providing a method of datadriven predictive control [13]. This method has been applied in some industrial processes and achieved good results.
Most datadriven predictive controllers are designed based on openloop subspace identification, but in practice it is often necessary to perform identification experiments on systems operating in closedloop. This is especially true when openloop experiments are not allowed due to safety (unstable processes) or production (undesirable openloop behavior) reasons [14]. It is found that the regular openloop subspace identification algorithm yields a biased estimate when applied to closedloop data [15]. The closedloop datadriven predictive control methods in [16, 17] have been presented. But the predictor is derived with the estimated Markov parameters which lead to a complicated predictor. We get a simple predictor constructed by subspace matrices. Jansson [18] developed a subspace method that can perform well on data collected both in open and closedloop conditions.
It is a major problem to implement adaptive control in closedloop system. In this paper, based on the subspace prediction model derived from [18], we design a closedloop datadriven predictive controller to solve this problem that obtains subspace matrices simply from Hankel matrices for a better implementation of the following adaptive mechanism in closedloop system.
The control performance of predictive control is dependent on the model quality [19]. The linear fixed model is used to design the controller in conventional datadriven predictive control method. It is applied to a linear system showed good results in a short period. But there are nonlinear and timevarying characteristics of long period in industrial processes, resulting in a poor performance when using the fixed model. It is highly desirable to implement adaptive mechanism to adjust the system model online. The feature of subspace identification is suitable for designing adaptive predictive controller perfectly. The adaptive mechanism is realized by online updating subspace matrices. At present, there are two ways of online adaptive subspace identification [20]. One is recursive identification method; by using different weighting to the new and old data, the variation of the process is tracked. The size of modeling data set will become larger with the process operation which needs enough memory storage. The other one is receding window method; the size of modeling data set remains unchanged and the oldest data is removed at the arriving of the new data. It is unfavorable that the harmless (or useless) data will increase information missing in the whole window and the computation time is longer than recursive one [21]. The recursive adaptive predictive control method is shown in [22, 23]; in [22] an adaptive predictive control strategy based on recursive subspace identification has been presented, adopting the prediction model with the smallest matching error. Mardi and Wang [23] presented an approach to constrained subspacebased MPC of timevarying systems. The central ideas are to find the predictive control law recursively using a subspace identification technology and to update the control law once a plantmodel mismatch is detected. Although both of them consider the forgetting factor to weaken the negative impact of the old data on the identification model, the identification accuracy will be declined as the old data more or less. Accordingly, we can find the receding window method in [24, 25]. Yang and Li [24] designed a subspacebased predictive controller, using receding window method to update subspace matrices at each time step for adaptive mechanism. Wahab et al. [25] proposed a direct adaptive MPC method which requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process inputoutput data for the identification. These two methods require decomposition at every time instant which increase the computational load and have incapability of handling harmless (or useless) data that bring performance degradation. Only one way of online adaptive subspace identification is employed in the above adaptive predictive control methods. We have been trying to combine the two ways, in our previous work [26]; an adaptive mechanism through online updating of the matrix is proposed. By comparing the prediction error before and after updating, we consider whether or not to update the prediction model. This method employs a recursive strategy to derive matrix but it requires us to compute every element value of matrix that increases the computation time. The model inspection can bring a promotion in harmless (or useless) data suppression but it cannot eliminate the harmless (or useless) data. Kameyama et al. [27] derived a recursive subspacebased identification algorithm with fixed inputoutput data size. It only solves the identification problem. We get the online updated subspace matrices from partial results in [27] but stress the derivation of the key elements of matrix which can reduce the computation time compared to the method in [26] and extend it to design the predictive controller. Another major problem to implement adaptive control is the inaccuracy of online excitation data. When the model or system parameters change, it needs to be adequately excited. Otherwise, some of the obtained data become harmless (or useless) ones which have a negative impact on system performance. The data inspection strategy introduced is a good solution for this problem through comparing the prediction error.
The main contribution of the paper is the development of a new solution of datadriven adaptive predictive control ensuring adaptation of closedloop systems. The method can offer an attractive alternative for industrial nonlinear, timevarying systems of long period in closedloop condition and there is no need for obtaining the system explicit model which can reduce the complexity. Through transforming system model form, the closedloop subspace identification algorithm is developed and the subspace matrices are obtained from the closedloop data. The adaptive mechanism is implemented by combining the advantages of receding window and recursive identification methods. The subspace matrices are derived by recursive method using a fixed modest size of data set with receding window method. The proposed mechanism can sufficiently fade the influence of the old data better than only recursive method and bring less computation load than only receding window method. By comparing the prediction error before and after updating, we consider whether or not to add the new data in data inspection strategy. The purpose of the strategy is to eliminate the new arrival of harmful (or useless) data produced by the online insufficient excitation. The control performance is superior to adopt openloop identification and other methods of datadriven adaptive predictive control.
The paper is organized as follows. In Section 2 the openloop datadriven predictive control method is given. Section 3 provides the closedloop datadriven predictive control method. The adaptive mechanism is highlighted in Section 4. Some simulation results are presented and discussed in Section 5. Section 6 gives the conclusions.
2. OpenLoop DataDriven Predictive Control
Consider a discrete statespace system of order described by innovations form where , and are input, output, and state vectors, respectively. is the Kalman filter gain and is an innovation sequence where variance . are system matrices of appropriate dimensions and is the innovations covariance matrix.
Construct the inputs block Hankel matrices using the data of with at instant : where the subscripts and represent the “past” and “future” time. Similarly, the outputs and noise Hankel matrices , , , and can also be obtained in the same way. The system past and future state sequences are defined as
The subspace prediction expression of the outputs can be derived by recursive substitution of (1): where is the extended observability matrix and and are the low triangular Toeplitz matrices, respectively, denoted by
The optimal prediction of can be written as where denotes the past inputoutput data matrix as , is the subspace matrix that corresponds to the past inputoutput data, and is the subspace matrix that corresponds to the future input data.
In order to calculate the subspace matrices and from block Hankel matrices, by solving the following least squares problem: where represents the Frobenius norm, the solution can be found from the orthogonal projection of the row space of onto the row space of the matrix : where denotes the orthogonal projection. The solution for (8) can be done in an efficient way by performing a decomposition: where is a low triangular matrix and is an orthogonal matrix. By letting with where superscript represents the MoorePenrose pseudoinverse and , .
The model predictive control problem is realized by the minimization of a cost function. A typical form of cost function in MPC is given as follows: where is the reference setpoint signal at the current time , and are the weight matrices, and and are the prediction and control horizon, respectively. and are defined as being equal to , and (12) can be rewritten as
In MPC framework, only the leftmost column is used to predict output. And to avoid steadystate error, the predictor of predictive controllers can be written in terms of incremental and as follows: where Using (14) in the minimization of cost function of (13), the control sequence can be obtained as follows:
At each time instance, only the first element of is used for calculating the control input. Therefore the control input is drawn as
At the next instant, when the new inputoutput data arrive, the same optimization is repeated. The above results can also be seen in [28–32]. In the above objectives, subspace matrices are identified using the openloop data and applied to the openloop system suitably. But, in closedloop system, as the data correlations due to feedback, above identification algorithm will result in a less accurate model and it will lead to degradation in control performance. To overcome the drawback, a closedloop datadriven predictive control method is given in Section 3.
3. ClosedLoop DataDriven Predictive Control
The structure of closedloop datadriven predictive control method is shown in Figure 1.
In order to use the closedloop structure of the subspace identification technique, the necessary steps are presented. Firstly, transform the system model in (1); define
It is well known that we can rewrite system model form as follows:
The prediction model can be represented as the subspace expression: where
Next, it’s directly to obtain the system statespace model in previous paper [18]. But in this paper, we focus on the derivation of subspace matrices to implement datadriven predictive control. Equation (20) can be rewritten as where and is the appropriate identity matrix. can be denoted by constituting the subspace matrices as follows:
The intermediate subspace matrices and are provided by the least squares problem:
The solution procedure is similar to the derivation of and in Section 2. Therefore, the closedloop subspace matrices and can be calculated as
We use incremental form to denote the predictor:
So the control sequence becomes where is an identity matrix of size 1. The control input is
At the next time step, measuring the new inputoutput data and the new control input will be calculated using the above optimization.
The above method relies on transforming system model form for reducing the impact of the noise sequence on input sequence greatly. It can be applied in closedloop system but also is suitable for openloop system.
4. Adaptive Mechanism
The linear fixed model is used to design the controller in traditional datadriven predictive control. But, in industrial processes, in presence of nonlinear and timevarying characteristics, the control performance is difficult to achieve the desired control effect and it will cause great mismatch of the model. Therefore, the adaptive control methods, updating the model online according to the conditions, have been attractive for decades and gradually applied to industrial processes. The adaptive predictive control, one of the adaptive methods, also has achieved a number of applications [33]. In this paper, an adaptive predictive control method is presented. Drawing the advantages of the receding window approach, the size of window is maintained as modest a priori while the recursive approach is used for updating the model. Additionally, due to the system disturbance and noise, a larger match error will be produced between the test data with the real time data at some time when the model or system parameters change. Such data is referred to as the harmful (or useless) data. A data inspection strategy is suggested to use the 1step output prediction error for filtering the harmful (or useless) data and eliminating the negative impact on the system of the harmful (or useless) data. Then, updating the subspace matrices online and implementing the adaptive mechanism are done.
The subspace matrices are obtained from matrix, so we update the matrix online using recursive method; then the prediction model can be obtained to calculate the control input.
Let be the inputoutput Hankel matrix at instant as where , , and are the past inputoutput data matrix, future input data matrix, and future output data matrix, respectively, in closedloop system. The oldest column of is defined as , where Given a set of new inputoutput data at instant , where
The inputoutput Hankel matrix at instant is defined as where , , and are similar to the definitions of , , and .
In order to maintain the size of receding window constant, it is necessary to exclude from and add to . So we can get the relation as ; then the relation gives
The decomposition of is
The objective is to get the results from the decomposition of :
From (33), firstly, we can get the first element of : where chol is Cholesky factorization [34]. The subspace matrices are obtained from matrix as in (10), so we just calculate the elements required in :
Substituting (38) and (39) into (10), the subspace matrices at instant can be derived by
By this way, the subspace matrices can be obtained through the above method; then the predictor will be calculated using (14) in openloop system and (26) in closedloop system. So we can get the control input at instant . At the next time, repeat the above procedure to implement the online adaptive mechanism and it will result in a quicker response to process changes.
In presence of noise and online disturbance, it would result in an inaccurate identification precision and an unneglectable match error as the presence of the harmful (or useless) data in the online excitation. In our previous work [26], an inspection strategy of model precession was proposed, but it cannot eliminate the negative impact of harmful (or useless) data on system performance. In this paper, a data inspection strategy introduced is the use of prediction error to remove the harmful (or useless) data.
Calculate the following prediction error before adding new data: where is the process output at time and is the predictive output at time predicting time before adding new data.
Similarly, the prediction error after adding new data can be also introduced: where is the output at time predicting time after adding new data.
While , the new data is a harmful (or useless) one, so maintain matrix and the system model invariably. Inversely, while , use the new data to update the matrix and predictor. At the next sampling time, when the new data arrives, recycle the above progress.
For the sake of clarity, the proposed adaptive mechanism implemented in the closedloop datadriven predictive controller is summarized in Algorithm 1.

5. Simulation Examples
In this section, a SISO (single input single output) example and a MIMO (multiple input multiple output) example identified and controlled by the proposed method are presented and discussed as follows.
Remark 1. The data used were preprocessed with the methods in Section 14 of [35].
5.1. A Hair Dryer Example
This hair dryer system is a simple mechanical device. The input is the power of the heating device, which is a mesh of resistor wires. The output is the outlet air temperature, which can be measured by thermocouple. Air is fanned through a tube and heated at the inlet. The details can be seen in [35]. In this example, we operated in case of closedloop system. was chosen to be a binary random signal shifting between 35 W and 65 W. The length of samples and sampling time were set to 1000 and 0.2 s, respectively. Firstly, totally 100 samples were used to verify the identification accuracy. The comparisons in Figure 2 show the response of the identified model and process output using openloop datadriven predictive control (ODPC) in Section 1 and closedloop datadriven predictive control (CDPC) in Section 2, where “Rf” is process output, “openloop” is openloop identified model, and “closedloop” is closedloop identified model.
To test the crossvalidation in Figure 2, a form of prediction error in [10] is given as where and are the values at instant of process and model output, respectively. Table 1 illustrates the prediction errors of openloop and closedloop identified models.

The crossvalidation results indicate that the closedloop model is more accurate than openloop model. Then, the system is given a performance of desired output changes to track using ODPC and CDPC. The sample was set to 1000 and the sampling time used was 0.2 s. The tuning parameters used in this simulation were , , and . Figure 3 depicts the output tracking performance. It can be seen that CDPC shows the favorable control performance and has a better tracking ability compared to ODPC.
In order to verify the adaptive mechanism in Section 4, the model of closedloop identification was identified as a statespace model:
We changed the system model at as
For comparison, the adaptive methods in [25, 26] are given. The method in [25] is an original receding window method which is performed by only decomposition. In [26], recursive approach is presented to obtain every element value of matrix and the model inspection strategy is given. Figure 4 shows the response comparison in the presence of disturbance after the system model changes. We can get that, in performance of disturbance rejection, the method in this paper is better than the other two methods. The data inspection strategy makes the contribution for this result. The harmless (or useless) data are always produced when we implement online identification. The control performance depends on the better data preprocessing in this paper compared to the methods in [25, 26].
By comparing computation time of 1000 samples, the methods in [25, 26] and this paper take about 71 s, 62 s, and 52 s, respectively. The method in [25] requiring decomposition at every instant results in the most time of the three methods. The computation time of our proposed method is less than that taken by the method in [26] since it only requires calculating the key elements , , , , and of matrix of our method but every element value of matrix of the method in [26].
Additionally, to verify the usefulness of the data inspection strategy, the prediction error in (43) is used. When system model was changed, we introduced two identification ways, the data inspection strategy is used in one way and the other not. The prediction errors of these two ways are showed in Table 2 from 600 s to 1000 s. We can get that the data inspection strategy improves the accuracy of the method.

5.2. An Industrial 4Stage Evaporator Example
The evaporator is a nonlinear and timevarying industrial process control system, and considering the stability of system the evaporator is often necessary to work in the closedloop case. The conventional control methods, such as PID control, will result in poor control performance. The product quality will be also affected accordingly. The evaporator is used to reduce the water content of a product and is widely applied in chemical industry, food industry, pharmaceuticals, and others. Therefore, it is of an extremely important practical significance to use an effective control method to achieve fast and accurate control performance of the evaporator. A typical industrial 4stage evaporator system and the detailed principle of operation can be seen in [36]. The system has three inputs and three outputs. The three inputs are input product flow , vapour flow to the first evaporator, and cooling water flow to condenser, respectively. The three outputs are dry matter content TDS of output product, output product flow , and output product temperature , respectively [37].
The openloop and closedloop identification algorithms are applied in system. Using 1000 validation data for identification, the prediction errors in (43) are given in Table 3.

It is similar to the hair dryer example in Section 5.1; the closedloop identification computes a more accurate model. The target is set for the output TDS tracking the reference signal in the system. The parameters of proposed datadriven adaptive predictive control (DAPC) method were tuned as , , , , and . The initial value of TDS was . For comparison, the recursive adaptive subspace predictive control (RASPC) method in [23] and an adaptive fuzzyPID controller in [38] were selected as competitors to compare the tracking capability. Figure 5 depicts the tracking comparison of these three controllers in the first 2000 samples and Figure 6 showed the partial enlarged drawing between 1000 s and 1200 s of Figure 5. At 1600 s, we changed to increase by 10 percent; the response comparison after the parameters change is showed in Figure 7.
Through the simulation results, it may fairly be said that our proposed method is much better in output tracking and disturbance rejection than that performed by recursive method in [23] and fuzzyPID controller in [38]. It can be interpreted that the reduction of the influence of the old data plays an important role.
As for the computation time, our method takes about 76 s for 1000 samples, while their recursive method in [23] is about 64 s; the latter method is somewhat superior to ours because it needs to add new data and eliminate old data at every instant in ours but theirs only add new data.
Similar to Section 5.1, the prediction errors with and without the data inspection strategy from 1600 s to 2000 s are listed in Table 4. Corresponding to the conclusion in the hair dryer example, the superior performance is obtained as using the data inspection strategy.

6. Conclusion
In this paper, the design of a datadriven adaptive predictive controller based on closedloop subspace identification has been addressed. The predictor is identified through the closedloop subspace identification and used to design a datadriven predictive controller. The adaptive mechanism is presented that combines the merits of both receding window and recursive identification methods, keeping the size of inputoutput data matrix constant and using recursive identification to obtain the subspace matrices which can derive the predictor. Meanwhile, the data inspection strategy is used to eliminate the new harmless (or useless) data. By simulation studies for two examples its performance has been proved to be efficient by comparing with other methods.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work was supported in part by the Major State Basic Research Development Program 973 (no. 2012CB215202), the National Natural Science Foundation of China (no. 61134001), and Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education. The authors would like to thank Dr. Xiaojie Su and the reviewers for their helpful comments.
References
 T. Katayama, T. McKelvey, A. Sano, C. G. Cassandras, and M. C. Campi, “Trends in systems and signals. Status report prepared by the IFAC Coordinating Committee on Systems and Signals,” Annual Reviews in Control, vol. 30, no. 1, pp. 5–17, 2006. View at: Publisher Site  Google Scholar
 Z. S. Hou and J. X. Xu, “On datadriven control theory: the state of the art and perspective,” Acta Automatica Sinica, vol. 35, no. 6, pp. 650–667, 2009 (Chinese). View at: Publisher Site  Google Scholar
 S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003. View at: Publisher Site  Google Scholar
 Huyck, H. J. Ferreau, M. Diehl et al., “Towards online model predictive control on a programmable logic controller: practical considerations,” Mathematical Problems in Engineering, vol. 2012, Article ID 912603, 20 pages, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 R. Hedjar, “Adaptive neural network model predictive control,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 3, pp. 1245–1257, 2013. View at: Google Scholar
 S. Bououden, M. Chadli, F. Allouani, and S. Filali, “A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 9, pp. 3741–3758, 2013. View at: Google Scholar
 V. Vesely, D. Rosinova, and T. N. Quang, “Networked output feedback robust predictive controller design,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 10, pp. 3941–3953, 2013. View at: Google Scholar
 R. N. Yang, G. P. Liu, P. Shi, C. Thomas, and M. V. Basin, “Predictive output feedback control for networked control systems,” IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 512–520, 2014. View at: Publisher Site  Google Scholar
 B. C. Ding, Modern Predictive Control, CRC Press, Boca Raton, Fla, USA, 2010.
 W. Favoreel, B. de Moor, and P. van Overschee, “Subspace state space system identification for industrial processes,” Journal of Process Control, vol. 10, no. 23, pp. 149–155, 2000. View at: Publisher Site  Google Scholar
 S. J. Qin, “An overview of subspace identification,” Computers and Chemical Engineering, vol. 30, no. 10–12, pp. 1502–1513, 2006. View at: Publisher Site  Google Scholar
 S. Yin, S. Ding, A. Haghani, and H. Hao, “Datadriven monitoring for stochastic systems and its application on batch process,” International Journal of Systems Science, vol. 44, no. 7, pp. 1366–1376, 2013. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 R. Kadali, B. Huang, and A. Rossiter, “A data driven subspace approach to predictive controller design,” Control Engineering Practice, vol. 11, no. 3, pp. 261–278, 2003. View at: Publisher Site  Google Scholar
 P. van Overschee and B. de Moor, “Closed loop subspace system identification,” in Proceedings of the 36th IEEE Conference on Decision and Control, pp. 1848–1853, December 1997. View at: Google Scholar
 B. Huang, S. X. Ding, and S. J. Qin, “Closedloop subspace identification: an orthogonal projection approach,” Journal of Process Control, vol. 15, no. 1, pp. 53–66, 2005. View at: Publisher Site  Google Scholar
 B. Kulcsár, J. W. van Wingerden, J. Dong, and M. Verhaegen, “Closedloop subspace predictive control for Hammerstein systems,” in Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 28th Chinese Control Conference (CDC/CCC '09), pp. 2604–2609, December 2009. View at: Publisher Site  Google Scholar
 J. Dong, M. Verhaegen, and E. Holweg, “Closedloop subspace predictive control for fault tolerant MPC design,” in Proceedings of the 17th World Congress, International Federation of Automatic Control (IFAC '08), pp. 3216–3221, July 2008. View at: Publisher Site  Google Scholar
 M. Jansson, “A new subspace identification method for open and closed loop data,” in Proceedings of the 16th Triennial World Congress of International Federation of Automatic Control (IFAC '05), pp. 500–505, July 2005. View at: Google Scholar
 Q. Zhou, P. Shi, S. Xu, and H. Li, “Adaptive output feedback control for nonlinear timedelay systems by fuzzy approximation approach,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 2, pp. 301–313, 2013. View at: Publisher Site  Google Scholar
 L. Xie, W. Liang, Q. Zhang, J. Zhang, and S. Wang, “Adaptive subspace identification based on fast moving window QR decomposition,” Journal of Chemical Industry and Engineering, vol. 59, no. 6, pp. 1448–1453, 2008 (Chinese). View at: Google Scholar
 B. Baykal and A. G. Constantinides, “Sliding window adaptive fast QR and QRlattice algorithms,” IEEE Transactions on Signal Processing, vol. 46, no. 11, pp. 2877–2887, 1998. View at: Publisher Site  Google Scholar
 L. Sun and X. M. Jin, “Modelpredictivecontrol based on subspace identification and its application,” Control Theory and Applications, vol. 26, no. 3, pp. 313–315, 2009 (Chinese). View at: Google Scholar
 N. A. Mardi and L. Wang, “Subspacebased model predictive control of timevarying systems,” in Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 28th Chinese Control Conference (CDC/CCC '09), pp. 4005–4010, December 2009. View at: Publisher Site  Google Scholar
 H. Yang and S. Y. Li, “Subspacebased adaptive predictive control for a class of nonlinear systems,” International Journal of Innovative Computing, Information and Control, vol. 1, no. 4, pp. 743–753, 2005. View at: Google Scholar
 N. A. Wahab, M. R. Katebi, and J. Balderud, “Data driven direct adaptive model based predictive control,” in Proceedings of the UKACC International Conference Control, 2008. View at: Google Scholar
 X. S. Luo, B. C. Ding, and T. Zou, “Adaptive predictive control based on online subspace identification,” Control and Instruments in Chemical Industry, vol. 37, no. 10, pp. 6–9, 2010 (Chinese). View at: Google Scholar
 K. Kameyama, A. Ohsumi, Y. Matsuura, and K. Sawada, “Recursive 4sidbased identification algorithm with fixed inputoutput data size,” International Journal of Innovative Computing, Information and Control, vol. 1, no. 1, pp. 17–33, 2005. View at: Google Scholar
 W. Favorel, B. D. Moor, and M. Gevers, “SPC: subspace predictive control,” in Proceedings of the IFAC World Congress, pp. 235–240, 1999. View at: Google Scholar
 I. H. Song, S. B. Lee, H. K. Rhee, and M. Mazzotti, “Identification and predictive control of a simulated moving bed process: purity control,” Chemical Engineering Science, vol. 61, no. 6, pp. 1973–1986, 2006. View at: Publisher Site  Google Scholar
 X. Wang, B. Huang, and T. Chen, “Datadriven predictive control for solid oxide fuel cells,” Journal of Process Control, vol. 17, no. 2, pp. 103–114, 2007. View at: Publisher Site  Google Scholar
 J. S. Zeng, C. H. Gao, and H. Y. Su, “Datadriven predictive control for blast furnace ironmaking process,” Computers and Chemical Engineering, vol. 34, no. 11, pp. 1854–1862, 2010. View at: Publisher Site  Google Scholar
 X. Lu, H. Chen, P. Wang, and B. Gao, “Design of a datadriven predictive controller for startup process of AMT vehicles,” IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2201–2212, 2011. View at: Publisher Site  Google Scholar
 H. Fukushima, T. H. Kim, and T. Sugie, “Adaptive model predictive control for a class of constrained linear systems based on the comparison model,” Automatica, vol. 43, no. 2, pp. 301–308, 2007. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 B. R. Woodley, J. P. How, and R. L. Kosut, “Subspace based direct adaptive ${H}_{\infty}$ control,” International Journal of Adaptive Control and Signal Processing, vol. 15, no. 5, pp. 535–561, 2001. View at: Publisher Site  Google Scholar
 L. Ljung, System Identification, Theory for the User, Prentice Hall, Upper Saddle River, NJ, USA, 1999.
 Y. C. Zhu, P. V. Overschee, B. D. Moor, and L. Ljung, “Comparison of three classes of identification methods,” in Proceedings of the 10th IFAC Symposium on System Identification (SYSID '94), pp. 175–180, 1994. View at: Google Scholar
 P. V. Overschee and B. D. Moor, Subspace Identification for Linear Systems: Theory Implementation Applications, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1996.
 J. Carvajal, G. Chen, and H. Ogmen, “Fuzzy PID controller: design, performance evaluation, and stability analysis,” Information Sciences, vol. 123, no. 34, pp. 249–270, 2000. View at: Publisher Site  Google Scholar  Zentralblatt MATH
Copyright
Copyright © 2014 Xiaosuo Luo and Yongduan Song. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.