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MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 2020, VOL. 26, NO. 2, 144–168 https://doi.org/10.1080/13873954.2019.1710715 ARTICLE On the combination of kernel principal component analysis and neural networks for process indirect control A. Errachdi, S. Slama and M. Benrejeb Automation Research Laboratory, Tunis El Manar University, Tunis, Tunisia ABSTRACT ARTICLE HISTORY Received 4 March 2019 A new adaptive kernel principal component analysis (KPCA) for Accepted 28 December 2019 non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-proces- KEYWORDS sing techniques. Indeed, the input vector of neural network con- Neural networks; modelling; troller is pre-processed by the KPCA method. Then, the obtained indirect control; KPCA; reduced neural network controller is applied in the indirect adap- reduction; non-linear system tive control. The inﬂuence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi- input multi-output system. It is concluded that, using the KPCA method, a signiﬁcant reduction in the control error and the identi- ﬁcation error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best. 1. Introduction We are involved in adaptive system control of the non-linear discrete system using neural network. In fact, the indirect adaptive control structure is based on two neural network blocks corresponding to the model identiﬁcation of the dynamic behaviour of the system and system controller [1–6]. However, the size of the neural network model or the neural network controller can accelerate or slow down their training phase. This problem of reduction of the higher dimension of neural network is well discussed by diﬀerent techniques [7–37]. The ﬁrst step in reduction method is feature selection (new features are selected from the original inputs) or feature extraction (new features are transformed from the original inputs). In the modelling, all available indicators can be used, but correlated features or irrelevant features could deteriorate the generalization performance of any model [7–18]. Many linear techniques of reduction dimensionality are proposed. For instance, Kohonen Self Organizing Feature Maps provide a way of representing multidimensional data in much lower dimensional spaces [19], curvilinear component analysis[20] and curvilinear distance analysis [21] are proposed to make smaller the original dimension of the face images, data and for classiﬁcation in medical imaging [22] and principal CONTACT A. Errachdi errachdi_ayachi@yahoo.fr Automation Research Laboratory, Tunis El Manar University, BP 37, le Belvédère, Tunis 1002, Tunisia © 2020 Informa UK Limited, trading as Taylor & Francis Group MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 145 component analysis (PCA) has been widely used for reducing high dimension in many applications [16–18,23–25]. PCA is a well-known method for feature extraction [23,24]. By calculating the eigenvectors of the covariance matrix of the original inputs, PCA linearly transforms the original high-dimensional input vector into new low-dimensional one whose com- ponents are uncorrelated. The basis function orders of PCA, as a typical approach, are the lowest in the sense of model dimension reduction [16–18,23–25]. In other applications, for instance, in the study by Zhang et al. [15], a hybrid modelling strategy consists of a decoupled non-linear radial basis function neural network model based on PCA and linear autoregressive exogenous model. PCA reduces the cross- validation time required to identify optimal model hyper-parameters [25]. In the study by Seerapu and Srinivas [26], it was combined with the linear discriminate analysis to ameliorate the reduction. Then, in the study by Peleato et al. [27], the use of ﬂuorescence data coupled with neural networks based on PCA for improved predictability of drinking water disinfection by-products was investigated. Second, in the study by Qinshu et al. [14], a PCA for feature selection and a grid searching and k-fold cross validation approach for parameter optimization in the support vector machine were developed. Finally, in other dimensionality reduction, linear techniques such as multidimensional scaling and probabilistic PCA are applied for user authentication using keystroke dynamics [28] and other methods [29]. However, PCA is a linear time/space separation method and cannot be directly applied to non-linear systems [30]. Non-linear PCA has also been developed by using diﬀerent algorithms. Kernel principal component analysis (KPCA) is a non-linear PCA developed by using the kernel method. Kernel method is originally used for Support Vector Machine (SVM). Later, it has been generalized into many algorithms having the term of dot products such as PCA. Speciﬁcally, KPCA ﬁrstly maps the original inputs into a high-dimensional feature space using the kernel method and then calculates PCA in the high-dimensional feature space. The linear PCA in the high-dimensional feature space corresponds to a non-linear PCA in the original input space. Recently, another linear transformation method called independent component analysis (ICA) is also developed. Instead of transforming uncorrelated components, ICA attempts to achieve statistically independent components in the transformed vectors. ICA is originally developed for blind source separation. Later, it has been generalized for feature extraction [7]. KPCA is used as an eﬀective method for tackling the problem of non-linear data [31]. Indeed, in the study by Chakour et al. [32], an algorithm for adaptive KPCA is proposed for dynamic process monitoring. This algorithm combined two existing algorithms: the recursive weighted PCA and the moving window KPCA algorithms. Even better, the fault detection of the non-linear system using KPCA method for extracting the reduced number of measurements from the training data [33] is studied. In the study by Xiao and He [34], a neural-network-based fault diagnosis approach of analog circuits is developed, using maximal class separability-based KPCA as a preprocessor to reduce the dimensionality of candidate features so as to obtain the optimal features with maximal class separability as inputs to the neural networks. In the study by Reddy and Ravi [36], diﬀerential evolution (DE)-trained kernel principal component wavelet neural network and DE-trained kernel binary quantile regression are proposed for classiﬁcation. 146 A. ERRACHDI ET AL. In the proposed DE-KPCWNN technique, KPCA is applied to input data to get KPC, on which WNN is employed. In the study by Klevecka and Lelis [37], a functional algorithm of preprocessing of input data taking into account the speciﬁc aspects of teletraﬃc and properties of neural networks is created. The practical application for forecasting telecommunication data sequences shows that the procedure of data preprocessing decreases the time of learning and increases the plausibility and accuracy of the forecasts. In this paper, the scheme of indirect adaptive control is used based on a neural network. First, the used neural network is based on an adaptive learning rate and a reduced derivative of the activation function. Even better, the weights of the neural network model and neural network controller are updated based on the identiﬁcation error and the control error and used to generate the appropriate control. In the ﬁrst hand, in various studies [1,2,5,6,15,38,39], the authors developed many algorithms for the adaptive indirect control without any preprocessing and they did not take into account the high dimension of the neural network. On the other hand, in the study by Errachdi and Benrejeb [4], the authors developed an algorithm to accelerate the speed of training phase in the adaptive indirect control based on neural network controller using a variable learning rate and a development of Taylor of the derivative of the activation function but they did not focus on the big dimension. That is why, in this paper, we propose a new algorithm of a reduction of the input vector of the neural controller in the control system based on the KPCA. The procedure of the data preprocessing scheme decreases the time of learning and increases the accuracy of the system control. The present paper is organized as follows. After this introduction, Section 2 reviews the proposed KPCA method for system control. In fact, the proposed neural network controller based on the KPCA method is developed. Furthermore, in Section 3, the proposed algorithm is detailed. In Section 4, an example of a non-linear system is presented to illustrate the proposed eﬃciency of the method. Section 5 gives the conclu- sion of this paper. 2. The proposed KPCA neural network controller approach On the basis of the input and output relations of a system, the above discrete non-linear system can be expressed by a NARMA (Non-linear Autoregressive Moving Average) model [4,35] given by yðk þ 1Þ¼ f ðyðkÞ; :::; yðk n Þ; uðkÞ; :::; uðk n ÞÞ (1) y u f ð:Þ is the non-linear function mapping speciﬁed by the model, yðkÞ and uðkÞ are the outputs and the inputs of the system, respectively, k is the discrete time, n and n are the y u number of past output and input samples, respectively, required for prediction. The aim of this paper is to ﬁnd a control law uðkÞ to the non-linear system, given by Equation (1), based on the KPCA approach in order that the system output yðkÞ tracks, where possible, the desired value rðkÞ. The indirect control architecture is shown in Figure 1, and the weights of the neural network model and the neural network controller are trained by diﬀerent errors where MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 147 Figure 1. The architecture of indirect neural control. eðkÞ is the identiﬁcation error, be ðkÞ is the estimated tracking error and e ðkÞ is the c c tracking error [4]. The architecture shown in Figure 1 assumes the role of two neural blocks. Indeed, the weights of the neural model are adjusted by the identiﬁcation error eðkÞ; however, the weights of the neural controller are trained by the tracking error e ðkÞ [4]. The multi-layer perceptron is used in the neural model and in the neural controller. Each block consists of three layers. The sigmoid activation function sð:Þ is used for all neurons [4]. 2.1. The neural network model The principle of neural network model is given by the Figure 2. th The j output layer of the hidden layer is described as follows: h ¼ w x j ¼ 1; 2; :::; n (2) j ji i 2 i¼1 where n is the number of nodes of the input layer, n is the number of nodes of the 1 2 hidden layer and w is the hidden weight. ji The input vector of the neural network model is x ¼½ uðkÞ; uðk 1Þ; uðk 2Þ; ::: (3) where uðkÞ is the neural network controller output. The output of the neural network model is given by the following equation: yrðk þ 1Þ¼ λsð w sðh ÞÞ (4) 1j j j¼1 where λ is a scaling coeﬃcient and w is the output weight. 1j The compact form of the output is given by the following equation: yrðk þ 1Þ¼ λsðh Þ¼ λs½w SðWxÞ (5) 1 148 A. ERRACHDI ET AL. Figure 2. The principle of neural network model. with,. x ¼½x ; i ¼ 1; .. . ; n i 1 W ¼½w ; i ¼ 1; ... ;n ; j ¼ 1; .. . ; n ji 1 2 SðWxÞ¼½sðh Þ ; j ¼ 1; ... ; n j 2 w ¼½w ; j ¼ 1; ... ; n 1 1j 2 The identiﬁcation error eðkÞ is given by eðkÞ¼ yðkÞ yrðkÞ (6) The function cost is given by the following equation: E ¼ ðeðkÞÞ (7) where N is the number of observations. The output weights are updated by the following equation: w ðk þ 1Þ¼ w ðkÞþ Δw ðkÞ (8) 1j 1j 1j MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 149 where Δw , j ¼ 1; :::; n is given by minimizing the cost function deﬁned as follows: 1j 2 @EðkÞ Δw ¼ηðkÞ 1j @w 1j @EðkÞ @eðkÞ @h (9) ¼ηðkÞ @eðkÞ @h @w 1 1j ¼ ληðkÞeðkÞs ðh ÞSðWxÞ ηðkÞ is the variable learning rate for the weights of the neural network model, 0 ηðkÞ 1, given by ηðkÞ¼ 2 2 (10) 0 T T 0 0 T λ s ðh Þ S ðWxÞSðWxÞþw S ðWxÞS ðWxÞw x x 1 1j 1j s ðh Þ is the derivative of sðh Þ deﬁned as follows: 1 1 s ðh Þ¼ sðh Þð1 sðh ÞÞ 1 1 1 (11) ð1 þ e Þ 1 1 þ h þ Oðh Þ 4 2 The hidden weights are updated by the following equation: w ðk þ 1Þ¼ w ðkÞþ Δw ðkÞ (12) ji ji ji where Δw is given by the following equation: ji @EðkÞ Δw ¼ηðkÞ ji @w ji @EðkÞ @eðkÞ @h @h j (13) ¼ηðkÞ @eðkÞ @h @h @w 1 j ji 0 0 T ¼ ληðkÞs ðh ÞS ðWxÞw x eðkÞ 1 1j 0 0 with S ðWxÞ¼ diag½s ðh Þ ; j ¼ 1; :::; n j 2 For the stability of the neural network model, the Lyapunov function is detailed. Indeed, let us deﬁne a discrete Lyapunov function as (14) VðkÞ¼ EðkÞ¼ ðeðkÞÞ where eðkÞ is the identiﬁcation error given by Equation (6). The change in the Lyapunov function is obtained by 2 2 ΔVðkÞ¼ Vðk þ 1Þ VðkÞ¼ ððeðk þ 1ÞÞ ðeðkÞÞ Þ (15) The identiﬁcation error diﬀerence can be represented by @yrðkÞ ΔeðkÞ¼ eðk þ 1Þ eðkÞ ηðkÞ eðkÞ (16) @w ðkÞ where w ðkÞ is the synaptic weights of the neural network identiﬁer (w ðkÞ, w ðkÞ). Using i 1j ji Equation (16), the identiﬁcation error is going to be 150 A. ERRACHDI ET AL. eðk þ 1Þ¼ eðkÞ ηðkÞðkÞeðkÞ (17) with hi 2 2 0 T T 0 0 T ðkÞ¼ð Þ s ðh Þ S ðWxÞSðWxÞþ w S ðWxÞS ðWxÞw x x (18) 1 1j 1j From Equations (17) and (18), the convergence of the identiﬁcation error eðkÞ is guaranteed if lim eðkÞ¼ 0or0< ηðkÞ< 2 ðkÞ with VðkÞ > 0 from Equation (14). k!þ1 The suitable online algorithm may be applied if the variable learning rate ηðkÞ is ðkÞ. 2.2. The KPCA neural network controller The PCA technique is a lower-dimensional projection method that can use with multi- variate data mining [25,30–32,40]. The main idea behind the PCA is to represent multi- dimensional data with fewer numbers of variables retaining the main features of the data. It is inevitable that by reducing dimensionality, some features of the data will be lost. The method PCA tries to project multidimensional data into a lower-dimensional space, retaining as much as possible variability of the data [4,25,30–32,40]. However, the presented PCA method is a linear technique and cannot capture the non-linear structure in a data set. For this reason, non-linear generalization has been proposed using the kernel method, introduced for computing the principal components of the data set mapped non-linearly into some high-dimensional feature space. Because sample data are implicitly mapped from an input space to a higher-dimensional feature space ζ, KPCA is implemented eﬃciently by virtue of kernel tricks and it can be solved as an eigenvalue problem of its kernel matrix. In this section, we propose to reduce the input vector of the neural network controller of the adaptive indirect control structure. Indeed, before the reduction of the input vector, the new architecture of the adaptive indirect KPCA neural network control is given Figure 3. We recall the input vector of the neural network controller is z ¼½ rðkÞ; rðk 1Þ; rðk 2Þ; ::: (19) where rðkÞ is the desired value. For the input data fz g , ϕ represents the non-linear mapped data in ζ. The k¼1 covariance matrix of the projected features C is l l,deﬁned as C ¼ ϕðz Þϕðz Þ (20) j j j¼1 Its eigenvalues and eigenvectors are given by Cp ¼ λ p k ¼ 1; :::; l (21) k k k From Equation (20), Equation (21) may be MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 151 Figure 3. The new architecture of indirect neural control. ϕðz Þðϕðz Þ p Þ¼ λ p (22) j j k k k j¼1 p can be rewritten as p ¼ α ϕðz Þ (23) k j j j¼1 with α , j ¼ 1; :::; l as the expansion coeﬃcients. Equation (21) can be rewritten as l l l X X X ϕðz Þðϕðz Þ α ϕðz ÞÞ ¼ λ α ϕðz Þ (24) j j i i k i i j¼1 i¼1 i¼1 The kernel function krðz ; z Þ is deﬁned as i j krðz ; z Þ¼ ϕðz Þ ϕðz Þ (25) i j i j is multiplied to the left and to the right by ϕðz Þ , Equation (23) becomes l l l X X X T T T ϕðz Þ ϕðz Þðϕðz Þ α ϕðz ÞÞ ¼ λ α ϕðz Þ ϕðz Þ (26) d j j i i k i d i j¼1 i¼1 i¼1 Equation (25) is l l l X X X krðz ; z Þ α krðz ; z Þ¼ λ α krðz ; z Þ (27) d i j i j k i d i i¼1 j¼1 i¼1 with krðz ; z Þ¼ ϕðz Þ ϕðz Þ d i d i 152 A. ERRACHDI ET AL. The resulting kernel principal components can be calculated using x ðkÞ¼ ϕðzÞ p ¼ α krðz; z Þ (28) r k i i i¼1 The reduced space of the signal given by Equation (28) constitutes the input vector of the neural network controller. We propose a dimensionality reduction technique that should be employed to reduce the dimensionality of the feature vectors before they are fed as input x ¼½ x ðkÞ; x ðk 1Þ; x ðk 2Þ; ::: (29) 1 r r r The primary purpose of data pre-processing is to modify the input variables so they can better match the predicted output. The main purpose of neural network data transfor- mation is to modify the distribution of the network input parameters without losing much information. th Using the reduced input vector x , the j output layer of the hidden layer is described as follows: h ¼ v x j ¼ 1; :::; n (30) cj ji 1i 4 i¼1 where n is the number of nodes of the input layer, v is the hidden weight. 3 ji Similarly, the output of the neural controller is given by the following equation: uðkÞ¼ λ s v sh c 1j cj j¼1 (31) n n 4 3 P P ¼ λ s v s v x c 1j ji 1i j¼1 i¼1 where n is the number of nodes of the hidden layer, λ is a scaling coeﬃcient and v is 4 c 1j the output weight. The compact form of the control input to the system is given by the following equation: uðkÞ¼ λ sðh Þ¼ λ s½v SðVx Þ (32) c c1 c 1 with,. x ¼½x ; i ¼ 1; ... ; n 1 1i 3 V ¼½v ; i ¼ 1; ... ;n ; j ¼ 1; .. . ; n ji 3 4 SðVx Þ¼½sðh Þ ; j ¼ 1; ... ; n 1 j 4 v ¼½v ; j ¼ 1; ... ; n 1 1j 4 The tracking error e ðkÞ is given by the following equation: c MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 153 e ðkÞ¼ yðkÞ rðkÞ (33) where rðkÞ is the desired output. The updated weights of the neural controller are obtained by minimizing the cost function deﬁned as follows: E ¼ ðe ðkÞÞ (34) c c where N is the number of observations. The output weights are updated by v ðk þ 1Þ¼ v ðkÞþ Δv ðkÞ (35) 1j 1j 1j with Δv , j ¼ 1::n , is the incremental change of the output weights: 1j 4 @E ðkÞ Δv ¼η ðkÞ 1j c @v 1j @sðh Þ @h @e ðkÞ @yrðkÞ j j @uðkÞ @E c @h @h c 1 c1 (36) ¼η ðkÞ @e ðkÞ @yðkÞ @h @sðh Þ @h @uðkÞ @h @v c 1 j j c1 1j 0 0 0 ¼ η ðkÞλ e ðkÞs ðh Þw S ðWxÞw s ðh ÞSðVx Þ c c 1 1j ji c1 1 where η ðkÞ is the learning rate for the weights of the neural network controller, 0 η ðkÞ 1, given by 2 2 0 0 0 η ðkÞ¼ 1=ðλ s ðh Þs ðh Þw w S ðWxÞ c1 1 1j ji c c hi (37) T T 0 0 T S ðVx ÞSðVx Þþ v S ðVx ÞS ðVx Þv x x Þ 1 1 1 1 1j 1 1j 1 Concerning the hidden weights, they are updated by v ðk þ 1Þ¼ v ðkÞþ Δv ðkÞ (38) ji ji ji where Δv is given by ji @E ðkÞ Δv ¼η ðkÞ ji @v ji @yr @sðh Þ @h @h @E @e @h j j @h cj c c 1 @u c1 (39) ¼η ðkÞ @e @y @h @sðh Þ @h @u @h @h @v c 1 j j c1 cj ji 0 0 0 0 T ¼ η ðkÞλ e ðkÞs ðh Þw S ðWxÞw s ðh Þv S ðVx Þx c c 1 1j ji c1 1j 1 c 1 0 0 with S ðVx Þ¼ diag½s ðh Þ ; j ¼ 1; :::; n 1 j 4 Let Ψ ¼½ϕðz Þ; :::; ϕðz Þ,1 ¼ð Þ and Γ ¼ Ψ Ψ, Γ is the matrix which is deﬁned as 1 l l ll ~ ~ ~ ~ Γ ¼ Γ 1 Γ Γ1 þ 1 Γ1 (40) l l l l with Γ ¼ ϕðz Þ 1 ϕðz Þ¼ krðz ; z Þ. In this paper, diﬀerent kernel functions are used and ij i l j i j deﬁned in Table 1. Table 1. The usual kernel functions. Function Kernel kz z k i j Radial basis function kernel 2σ krðz ; zÞ¼ e i j Polynomial kernel krðz ; zÞ¼ða:z :z þ bÞ i j i j Linear kernel krðz ; zÞ¼ z :z i j i j Sigmoid kernel krðz ; zÞ¼ tanhða:z :z þ bÞ i j i j 154 A. ERRACHDI ET AL. The principal components are the s ﬁrst vectors associated with the highest eigenva- lues and are often suﬃcient to describe the structure of the data. The number s satisﬁes the Inertia Percentage Criterion (IPC) [25] given by s ¼ argðIPC 99Þ (41) with i¼1 IPC ¼ 100 (42) i¼1 i We have developed a neural network controller based on a reduced input vector and a variable learning rate. Consequently, this approach increases the training speed. For the stability of the neural network controller, the Lyapunov function is detailed. Indeed, let us deﬁne a discrete Lyapunov function as (43) V ðkÞ¼ E ðkÞ¼ ðe ðkÞÞ c c c where e ðkÞ is the control error. The change in the Lyapunov function is obtained by 2 2 (44) ΔV ðkÞ¼ V ðk þ 1Þ V ðkÞ¼ ððe ðk þ 1ÞÞ ðe ðkÞÞ Þ c c c c c The control error diﬀerence can be represented by @e ðkÞ T @yðkÞ @u ðkÞ c c Δe ðkÞ¼ e ðk þ 1Þ e ðkÞ η ðkÞð Þ e ðkÞ (45) c c c c c @v ðkÞ @u ðkÞ @v ðkÞ c c c where v ðkÞ is the synaptic weights of the neural network controller (v ðkÞ and v ðkÞ). c 1j ji Using Equation (45), the control error is going to be e ðk þ 1Þ¼ e ðkÞ η ðkÞ ðkÞe ðkÞ (46) c c c c with hi 2 2 0 0 0 T T 0 0 T ðkÞ¼ λ s ðh Þs ðh Þw w S ðWxÞ S ðVx ÞSðVx Þþ v S ðVx ÞS ðVx Þv x x c c1 1 1j ji 1 1 1 1 1j 1 c 1j 1 (47) From Equations (46) and (47), the convergence of the control error e ðkÞ is guaranteed if lim e ðkÞ¼ 0or0< η ðkÞ< 2 ðkÞ with V ðkÞ > 0 from Equation (43). k!þ1 c c c c The suitable online algorithm for real-time applications may be applied if the variable learning rate η ðkÞ is ðkÞ. c c 3. The proposed algorithm In this section, a summary of the proposed algorithm of the online kernel principal component analysis neural network controller is presented. Oﬄine phase (1) Initialization of neural network parameters (v , v , w , w ) using M observa- 1j ji 1j ji tions, ðM NÞ, (2) Determine the matrix C, focus the data and decompose into eigenvalue λ, MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 155 (3) Determine the orthogonal eigenvalues and the eigenvectors of the covariance matrix, (4) Order the eigenvectors on the decreasing way respect to the corresponding eigenvalues, (5) (5) Choose x ðkÞ that satisfy Equation (28) using the s retained principal compo- nents given byEquations (41) and (42). Online phase (1) At time instant ðk þ 1Þ, we have a new data ðuðk þ 1Þ; yðk þ 1ÞÞ, using the obtained input vector x , if the condition eðk þ 1Þ< ε , where ε > 0 is a given 1 1 1 small constant, is satisﬁed then the neural network model, given by Equation (5), approaches suﬃciently the behaviour of the system. (2) If the condition e ðk þ 1Þ < ε , where ε > 0 is a given small constant, is satisﬁed, c 2 2 then the reduced neural network controller provides suﬃciently the control law uðkÞ. (3) If eðk þ 1Þ< ε is not satisﬁed, the update of the synaptic weights of the neural network model is necessary, using Equations (8) and (12), (4) If e ðk þ 1Þ< ε is not satisﬁed, the update of the synaptic weights of the neural c 2 network controller is necessary, using Equation (35) and (38), (5) (5) End. 4. Simulation results In this section, two non-linear discrete systems are used. Indeed, the ﬁrst is a single-input single-output nonlinear time-varying system and the second is a multi-input multi- output (MIMO) system. 4.1. Example of time-varying system The time-varying non-linear system is described by the input–output model in the following equation [41]. yðkÞyðk 1Þyðk 2Þuðk 1Þðyðk 2Þ 1Þþ uðkÞ yðk þ 1Þ¼ (48) 2 2 a ðkÞþ a ðkÞy ðk 1Þþ a ðkÞy ðk 2Þ 0 1 2 where yðkÞ and uðkÞ are, respectively, the output and the input of the time-varying non- linear system at instant k; a ðkÞ, a ðkÞ and a ðkÞ are given by 0 1 2 < a ðkÞ¼ 1 a ðkÞ¼ 1 þ 0:2cosðkÞ (49) a ðkÞ¼ 1 þ 0:2sinðkÞ The trajectory of a ðkÞ and a ðkÞ are given in Figure 4. 1 2 In this section, in order to examine the eﬀectiveness of the proposed algorithm of the dimensionality reduction, diﬀerent performance criteria are used. 156 A. ERRACHDI ET AL. 1.2 1.2 1.15 1.15 1.1 1.1 1.05 1.05 1 1 0.95 0.95 0.9 0.9 0.85 0.85 0.8 0.8 0 102030405060708090 100 0 102030405060708090 100 k k Figure 4. a ðkÞ and a ðkÞ trajectories. 1 2 Indeed, the mean squared identiﬁcation error (MSE ) and the mean absolute identi- ﬁcation error (MAE ) are, respectively, given by MSE ¼ ðyðkÞ yrðkÞÞ (50) k¼1 MAE ¼ ðyðkÞ yrðkÞÞ (51) k¼1 where yðkÞ is the time-varying system output, yrðkÞ is the neural network model output and the used number of observations N is 100. The mean squared tracking error (MSE ) and the mean absolute tracking error (MAE ) are, respectively, given by MSE ¼ ðyðkÞ rðkÞÞ (52) k¼1 where rðkÞ is the desired value. MAE ¼ ðyðkÞ rðkÞÞ (53) k¼1 In this section, we examine the eﬀectiveness of the proposed algorithm of the dimension- ality reduction of the neural network controller input vector in the adaptive indirect control system. Indeed, in oﬄine phase, using a reduced number of observations ðM ¼ 3Þ to ﬁnd, either, the parameter initialization of the neural network parameters (w , w , v , v ), and 1j ji 1j ji the KPCA parameters as the matrix C, the eigenvalues, the eigenvectors, and ﬁnally the reduced input vector x ðkÞ given by Equation (28) based on the s retained principal components given by the Equations (41)–(42) are obtained. In online phase, at instant ðk þ 1Þ, we use the input vector of the neural network controller x ¼½ x ðkÞ; x ðk 1Þ; x ðk 2Þ; x ðk 3Þ; x ðk 4Þ . 1 r r r r r a (k) a (k) 2 MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 157 Table 2. The comparison results of the used kernel function in the identiﬁcation error. Kernel function MSE RBF kernel, σ ¼ 6 4:625210 Polynomial kernel, a ¼ 1; b ¼ 1; n ¼ 3 4:685410 Linear kernel 4:682910 Sigmoid kernel, a ¼ 1; b ¼ 1 4:095510 In this case, both neural network model and pre-processing neural network controller consist of single input, 1 hidden layer with 8 nodes, and a single output node, identically, and a variable learning rate of neural network model ηðkÞ and of neural network controller η ðkÞ. The used scaling coeﬃcient is λ ¼ λ ¼ 1 and ε ¼ ε ¼ 10 . c 1 2 To use the suitable kernel function, the simulation results present that the used sigmoid function as a kernel, compared to other kernel functions deﬁned in Table 2, which gives the lowest value obtained with the calculation of the MSE indicating which sigmoid kernel function is the most reliable. However, the features are directly fed to multilayer perceptron neural network as inputs without any preprocessing by KPCA. The obtained online MLP neural network model and the plant output are obtained. The used input vector of the MLP neural network is ½rðkÞ; rðk 1Þ; rðk 2Þ; rðk 3Þ; rðk 4Þ; rðk 5Þ when the number of the hidden layer is 1 with 23 nodes and the value of the learning rate is variable. From Figure 5, an excellent concordance between both plant output and the desired value is observed with a mean square error equal to 6:926910 . 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 The desired values r(k) 0.1 The plant output y(k) 0.05 0 102030405060708090 100 Figure 5. The pre-processing control system output and the desired values. 158 A. ERRACHDI ET AL. In Figure 5, the output of the reduced online MLP neural network controller and the desired values are presented. In this case, the KPCA method is combined with the multilayer perceptron neural network. The KPCA technique is used as a preprocessing method to reduce the dimension features. The obtained reduced vector is fed also to the online multilayer perceptron neural network. The number of the hidden layer is 1. The learning rates are variable. A concordance between both desired values and the plant output is noticed from Figure 6. To give more eﬃciency of this combination, several functions are tested and the result is presented in Table 2. As deﬁned in Table 2, we use the sigmoid function as a kernel function in the KPCA technique, and the tracking control aim of this system is to follow as possible the reference signal based on a proposed pre-processing neural network controller. In this simulation, the desired value, rðkÞ, is given in the following: 0:45 fork 25 0:20 for 26 k 50 rðkÞ¼ (54) > 0:45 for 51 k 75 0:20 for k > 75 We examine the inﬂuence of the dimensionality reduction of the neural network con- troller input vector in the identiﬁcation error in Table 3 and in the control error in Table 4. From Tables 3 and 4, we observe that using the KPCA as a pre-processing phase to reduce the input vector of the neural network controller, the neural network KPCA controller has the smallest performance criteria in the identiﬁcation error eðkÞ and in the control error e ðkÞ. These results are shown in Figures 5, 6 and 7. Indeed, Figure 5 presents the pre-processing control system output and the desired values. In this case, the KPCA method is combined with a multilayer perceptron neural network controller. The KPCA technique is used as a preprocessing method to reduce the dimension features. The obtained reduced vector is fed to the neural network controller. A concor- dance between the desired values and the control system output is noticed from Figure 7 although the parameters vary over time. However, Figures 6 and 7 present, respectively, the control law and the control error. Table 3. The inﬂuence of the dimensionality reduc- tion in the identiﬁcation error. NN model KPCA NN model η Variable Variable 7 7 MSE e 4; 6252:10 4; 0955:10 4 4 MAE 5; 4126:10 4:9423:10 4 4 maxðeÞ 9:9953:10 9; 9666:10 time (s) 16:2345 1:0987 Table 4. The inﬂuence of the dimensionality reduction in the control error. NN controller KPCA NN controller η Variable Variable MSE 0:0074 0:0027 MAE 0:0225 0:0166 maxðe Þ 0:5000 0:3000 time (s) 47:4502 3:1092 MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 159 0.8 Control law u(k) 0.7 0.6 0.5 0.4 0.3 0.2 0 102030405060708090 100 Figure 6. The control law. 0.35 The control error 0.3 0.25 0.2 0.15 0.1 0.05 -0.05 0 102030405060708090 100 Figure 7. The control error. 160 A. ERRACHDI ET AL. These ﬁgures reveal that the NN controller using the KPCA as a pre-processing technique has smaller errors than the other controller without pre-processing. Another desired value rðkÞ, given by Equation (55), is used to examine the eﬀective- ness of the proposed algorithm of the dimensionality reduction of the neural network controller input vector in the adaptive indirect control system for the time-varying non- linear system. Indeed, both neural network model and neural network controller consist of single input, 1 hidden layer with 23 nodes, and a single output node, identically. The used scaling coeﬃcient is λ ¼ λ ¼ 1 and ε ¼ ε ¼ 10 . c 1 2 In this simulation, the desired value, rðkÞ, is given in the following: 0:45 for k 25 < 0:20 for 26 k 30 rðkÞ¼ 0:40 for 31 k 35 (55) > 0:30 for 36 k 80 0:20 for k > 80 Figure 8 presents the pre-processing control system output and the desired values. In this case, the KPCA method is combined with a multilayer perceptron neural network controller. A concordance between the desired values and the control system output is noticed, although the time-varying parameters. However, Figures 9 and 10 present, respectively, the control law and the control error. These ﬁgures reveal that the NN controller using the KPCA as a pre-processing technique has smaller errors than the other controller without pre-processing. 0.5 The desired values r(k) 0.45 The plant output y(k) 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 102030405060708090 100 Figure 8. The pre-processing control system output and the desired values. MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 161 0.8 Control law u(k) 0.7 0.6 0.5 0.4 0.3 0.2 0 102030405060708090 100 Figure 9. The control law. Tables 5 and 6 present the inﬂuence of the dimensionality reduction in the identiﬁca- tion error and in the control error. From Tables 5 and 6 we observe that by using the KPCA as a pre-processing phase to reduce the input vector of the neural network controller, the neural network KPCA controller has the smallest performance criteria in the identiﬁcation error eðkÞ and in the control error e ðkÞ. These results are shown in Figures 8, 9 and 10. Table 5. The inﬂuence of the dimensionality reduc- tion in the identiﬁcation error. NN model KPCA NN model η Variable Variable 7 7 MSE 4; 4236:10 4; 2343:10 4 4 maxðeÞ 9; 9904:10 9; 9793:10 4 4 MAE 5; 1255:10 4; 9632:10 time (s) 41:3576 1:8007 Table 6. The inﬂuence of the dimensionality reduc- tion in the control error. NN model KPCA NN model η Variable Variable MSE 0:0073 0:0028 MAE 0:0221 0:0166 ec maxðe Þ 0:5000 0:3073 time (s) 22:3142 3:1241 162 A. ERRACHDI ET AL. 0.35 The control error 0.3 0.25 0.2 0.15 0.1 0.05 -0.05 0 102030405060708090 100 Figure 10. The control error. 4.2. Eﬀect of disturbances An added noise vðkÞ is injected to the output of the time-varying non-linear system, given by Equation (48), in order to test the eﬀectiveness of the pre-processing neural network controller. To measure the correspondence between the system output and the desired value, a signal noise ratio ðSNRÞ is taken from the following equation: ðyðkÞ yÞ k¼0 SNR ¼ (56) ðvðkÞ vÞ k¼0 with vðkÞ is a noise of the measurement of symmetric terminal δ, vðkÞ2½δ; δ, y and v are an output average value and a noise average value, respectively. In this paper, the taken SNR is 5%. Using the ﬁrst desired value rðkÞ, the sensitivity of the proposed pre-processing neural network controller is examined in Tables 7 and 8, respectively. From these tables, we observe that by using the KPCA as a pre-processing phase to reduce the input vector of the neural network controller, the neural network KPCA controller has the smallest performance criteria in the identiﬁcation error and in the control error. Using the second desired value, the sensitivity of the proposed pre-processing neural network controller is examined in Tables 9 and 10, respectively. MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 163 Table 7. The inﬂuence of the dimensionality reduc- tion in the identiﬁcation error. NN model KPCA NN model η Variable Variable 8 8 MSE 4; 2044:10 3; 5275:10 4 4 maxðeÞ 9; 8520:10 9; 7021:10 4 4 MAE 1; 2436:10 1; 1753:10 time (s) 58:2112 3:2024 Table 8. The inﬂuence of the dimensionality reduction in the control error. NN Controller KPCA NN controller η Variable Variable MSE 0:0074 0:0027 MAE 0:0223 0:0165 maxðe Þ 0:5000 0:3000 time (s) 62:2231 2:3321 Table 9. The inﬂuence of the dimensionality reduc- tion in the identiﬁcation error. NN model KPCA NN model η Variable Variable 8 8 MSE 2; 8501:10 1; 6541:10 4 4 maxðeÞ 9; 4522:10 8; 9819:10 4 4 MAE 1; 1140:10 1; 0016:10 time (s) 17:5536 2:0092 According to the obtained simulation results, despite the fact of the presence of disturbance in the system output and the time-varying parameters, the lowest MSE , MAE and maxðe Þ are obtained using a combination between the neural network e c controller and the KPCA technique. 4.3. Example of multi-input multi-output system In this section, in order to examine the eﬀectiveness of the proposed algorithm of the dimensionality reduction, a multi-input multi-output (MIMO non-linear system, given by the following equation, is used. y ðkÞ y ðk þ 1Þ¼ þ u ðkÞ 1 2 1 1þy ðkÞ (57) y ðkÞy ðkÞ 1 2 y ðk þ 1Þ¼ þ u ðkÞ 2 2 2 1þy ðkÞ where y ðkÞ and u ðkÞ, i ¼ 1; 2, are, respectively, the output and the input of the MIMO i i non-linear system at instant k; r ðkÞ and r ðkÞ are the reference signal given by 1 2 164 A. ERRACHDI ET AL. 2kπ r ðkÞ¼ sinð Þ > 8 < > 0:8fork 50 0:4for51 k 100 (58) r ðkÞ¼ > > 0:8for101 k 150 > > : : 0:4for151 k 200 The control system outputs, the desired values and the control errors are presented in Figure 11. However, Figure 12 presents the control law u1 and u2 trajectories. These ﬁgures reveal that using a NN controller combined with the KPCA as a pre-processing technique gives an excellent concordance between the system outputs and the desired outputs with smaller control errors. In this case, both neural network model and pre-processing neural network controller consist of single input, 1 hidden layer with 28 nodes, and two output nodes, identically, and variable learning rates of neural network model, η ðkÞ, and of neural network controller η ðkÞ. The used scaling coeﬃcient is λ ¼ λ ¼ 1 and ε ¼ 10 , i ¼ 1 : 2. i c i ic The input vector of the neural network controller is x ¼½ x ðkÞ; x ðk 1Þ; x ðk 2Þ; x ðkÞ; x ðk 1Þ; x ðk 2Þ .The inﬂuence of the 1 r1 r1 r1 r2 r2 r2 dimensionality reduction in the model error and in the control error is shown in Tables 11 and 12. 5. Conclusion In this paper, an online combination between the neural network controller and the KPCA method is proposed and is applied with success in indirect adaptive control. Diﬀerent kernel functions are tested. For instance, the lowest MSE , MAE , maxðeÞ, MSE , MAE e e e e c c and maxðe Þ are obtained, and it is proved that the sigmoid kernel function is the best. The eﬀectiveness of the proposed algorithm is successfully applied, ﬁrstly, to single-input single-output system, with and without disturbances, and it proved its robustness to reject disturbances and to accelerate the speed of the learning phase of the neural model and neural controller. Second, it is applied to MIMO system and it gives good results. 0.9 0.9 desired value r 0.8 0.8 system output y control error ec 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 desired value r system output y 0.2 0.2 control error ec 0.1 0.1 0 0 -0.1 -0.1 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 k k Figure 11. The control system output, the desired values and the control error. MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS 165 0.4 0.7 control law u control law u 1 2 0.35 0.6 0.3 0.5 0.25 0.2 0.4 0.15 0.3 0.1 0.2 0.05 0 0.1 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 k k Figure 12. The control law u and u trajectories. 1 2 Table 10. The inﬂuence of the dimensionality reduction in the control error. NN controller KPCA NN controller η Variable Variable 0:0074 0:0028 MSE MAE 0:0233 0:0175 maxðe Þ 0:5000 0:3073 time (s) 38:4532 3:00941 Table 11. The inﬂuence of the dimensionality reduction in the model error. 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Mathematical and Computer Modelling of Dynamical Systems – Taylor & Francis
Published: Mar 3, 2020
Keywords: Neural networks; modelling; indirect control; KPCA; reduction; non-linear system
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