Support Vector Regressions And Their Applications To Parameter Estimation For Intelligent Aeroengines

As an advanced control concept worthiest of developing in aeroengines, intelligent engine control possesses plentiful contents. Hence, in this dissertation, thrust estimator design and analytical redun-dacy technique for intelligent engine control attract more attentions. The thrust estimator plays a paramount role in direct thrust control and performance deterioration mitigation control, while ad-vanced analytical redundancy technique is always referred as a powerful and effective tool to cope with sensor fault in high reliability control emphasized in intelligent engine control. During the proc-ess of designing thrust estimator and developing analytical redundancy, based on support vector re-gression (SVR) owning statistical learning foundation and excellent generlization performance, to circumvent the shortcomings existing in the original algorithms, many valuable algorithms and view-points are proposed. To be more important, the proposed algorithms are applied to thrust estimator design and analytical redundancy technique, and the satisfactory results are obtained.The classical SVR does not oppress outliers in the system. To this end, the truncated support vector regression (TSVR) with truncatedε-insensitive loss function is proposed, which not only oppreses outliers in the system and enhances generalization performance but also reduces the number of sup-port vectors and improves the real time. To solve the non-convex optimization problem in TSVR, the concave-convex procedure (CCCP) is utilized to transform the non-convex optimization to a series of convex ones so that this problem is settled successfully. In the process of realizing TSVR, there are two different perspectives, one of which is the dual; another is the primal. Although perspectives are different, the similar results can be achieved.After analyzing the overfitting phenomenon in hard support vector regression (HSVR), the greedy stagewise strategy is employed to approximately train HSVR, viz. GS-HSVR. In effect, the presented GS-HSVR uses the early stopping rule, which is equivalent to an implicit regularization technique, to block the overfitting phenomenon. Compared with the classical SVR, GS-HSVR takes advantages to a certain extent in the training time and the number of support vectors.In comparison with classical SVR, the solution of least squares support vector regression (LSSVR) is lack of sparseness. To sidestep this problem, on the basis of fast sparse approximation for LSSVR (FSA-LSSVR), a novel algorithm, viz. LS2SVR, is proposed. By contrast with FSA-LSSVR and other pruning algorithms, LS2SVR takes advantages of the training time and the number of support vectors. Due to consideration of generating constraints for the target function from the ensemble training set, LS2SVR can gain the comparable generalization performance with FSA-LSSVR using less number of support vectors, which is proved. Furthermore, after combining reduced technique with iterative strat-egy, recursive reduced LSSVR (RR-LSSVR) is proposed. Compared with FSA-LSSVR, RLSSVR (random LSSVR), and LS2SVR, with the exception of the training cost, RR-LSSVR holds better sparseness.To improve many unkown systems owning different data trends in different regrions, i.e., some parts are steep variations while other parts are smooth variations, after combining semiparametric technique with multikernel learning, two multikernel semiparametric predictors, i.e., multikernel semiparametric linear programming support vector regression (MSLP-SVR) and sparse multikernel semiparametric least squares support vector regression (sparse MSLSSVR), are proposed. The com-mon characteristic of the two predictors above is that the classical predictors are their special cases, which signifies the learning effectiveness of the two proposed predictors not worse than the classical ones. In addition, compared with other multikernel learning algorithms, the two proposed multikernel learning algorithms take advantages in generalization performance or training time.The main purpose of researches on these algorithms above is to exploit them, which includes two aspects. One is that RR-LSSVR is utilized to design thrust estimator for intelligent aeroengine. An-other is that based on GS-HSVR, an online analytical redundancy technique for sensor fault in intel-ligent aeroengine is proposed.The design of thrust estimator is significant for direct thrust control and performance deterioration mitigation control in telligent engine control. Hence, firstly, the input parameters of thrust estimator are determined by leave-one-out strategy which is commonly-used to select model. In order to design thrust estimator with high accuracy and real time, according to the fight altitude, the full flight enve-lope is divided. Subsequently, a more reasonable method is presented to divide the flight envelope, i.e., the whole samples in the full flight envelope are grouped using the clustering method. In general, during each group, the thrust does not fluctuate steeply, which guarantees to avoid the phenomenon that the relative errors are obviously different with the almost same thrust errors due to the sharply fluctuant thrust. To mitigate the deterioration phenomenon, after adding deterioration samples to the training set, this problem is solved. Finally, to simulate the dynamic process in intelligent aeroengines, the modified RR-LSSVR, which uses the feedback thrust as the input parameter, is exploited to design dynamic thrust estimator.As a concept in intelligent engine control, high reliability control is to supply correct signals for aeroengine controller. To this problem, the improved GS-HSVR, viz. fast online approximation for hard support vector (FOAHSVR), can get the similar generalization performance to GS-HSVR. More importantly, FOAHSVR is an online learning algorithm. Hence, an online analytical redundancy scheme for sensor fault in telligent aeroengines is proposed, which can be used to detect, isolate and accommodate sensor failure fault. To deal with sensor drift fault, a correction strategy is proposed, and the experimental results demonstrate that the presented correction strategy is capable of detecting, isolating and accommodating the sensor drift fault effectively.