Transactions of the Institute of Measurement and Control

Liu, Ting-Rui; Gong, Ai-Ling

doi: 10.1177/0142331219888415pmid: N/A

Theoretical modeling and vibration control for divergent motion of thin-walled pre-twisted wind turbine blade have been investigated based on “linear quadratic Gaussian (LQG) controller using loop transfer recovery (LTR) at plant input” (LLI). The blade section is a single-celled composite structure with symmetric layup configuration of circumferentially uniform stiffness (CUS), exhibiting displacements of vertical/lateral bending coupling. Flutter suppression for divergent instability is investigated, with blade driven by nonlinear aerodynamic forces. Theoretical modeling of CUS-based structure is implemented based on Hamilton variational principle of elasticity theory. The discretization of aeroelastic equations is solved by Galerkin method, with blade tip responses demonstrated. The LLI controller is characterized by LTR at the plant input. The effects of LLI controller are achieved and illustrated by displacement responses, controller responses and frequency spectrum analysis, respectively.

Ahmadi, Mahdi; Rikhtehgar, Pouya; Haeri, Mohammad

doi: 10.1177/0142331219888368pmid: N/A

Recently, the multi-model controllers design was proposed in the literature based on integrating of the stability and performance criteria. Although these methods overcome the redundancy problem, the decomposition step is very complex and time consuming. In this paper, a cascade design of multi-model control is presented that is made from two sequential steps. In the first step, the nonlinear system is decomposed into a set of linear subsystems by just considering the stability criterion. In this step, the gap metric is used as a smart tool to measure the distance between linear subsystems. While the closed-loop stability is gained through the first step, the performance is improved in the second step by adding internal model controllers in a cascade structure. Therefore, the proposed idea supports designing a multi-model controller in a simple way by integrating the stability and performance criteria in two independent cascade steps. As a result, the proposed method avoids the model redundancy problem, has a simple structure, guarantees the robust stability, and improves the performance. Two nonlinear chemical processes are simulated to evaluate the proposed multi-model controller approach.

Han, Ying; Jing, Yuanwei; Dimirovski, Georgi M

doi: 10.1177/0142331219888366pmid: N/A

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.

Liu, Weiqi; Feng, Zhengping; Bi, Anyuan

doi: 10.1177/0142331219889172pmid: N/A

This paper proposes a novel nonsingular terminal sliding mode control combined with global sliding surface for a class of uncertain nonlinear second-order systems. The suggested control approach is developed based on the Lyapunov theory. The sliding mode reaching the sliding surface in finite time can be guaranteed. Furthermore, chattering phenomenon caused by the switching control action can be eliminated theoretically, and desirable control performance is realized. The simulation and tank test results for heave control of a remotely operated vehicle are presented to verify the effectiveness of the proposed method.

Wu, Wen; Faisal, Shah

doi: 10.1177/0142331219889221pmid: N/A

In recent years, with the development of artificial intelligence, data-driven methodologies have been widely studied in fault diagnosis and detection, since an increasing number of complexities of modern complex systems make the mechanism model information difficult to obtain. Especially in people’s health monitoring, it is very difficult to achieve the mechanism model. The existing challenges, such as huge amount of data, high data dimension, large noise interference, and so forth, make the applications of data-driven approaches more suitable. For the sake of solving the problems above, we present principal component analysis-support vector machine (PCA-SVM) method with different kernels to reduce data dimension, and two sets of breast-cancer data are utilized to verify the method. Additionally, support vector machine-recursive feature elimination (SVM-RFE), the original SVM with different kernels, PCA and modified PCA (MPCA) methods are also applied to diagnose malignant cancer in comparison with PCA-SVM. In experiments, PCA-SVM via radial basis function (RBF) kernel shows better performance than other methods, with the two breast cancer datasets obtained from the University of Wisconsin Hospital. Finally, PCA-SVM in this study uses only six principal components and obtains better accuracy (97.19%) than most of the previous studies.

Xu, Nuo; Sun, Liankun

doi: 10.1177/0142331219889190pmid: N/A

In this paper, based on time-varying delay Markovian jump system (MJS), Bessel-Legendre inequality method and improved positive definite condition of Lyapunov function are used, and parallel distributed compensation (PDC) state feedback controller is also introduced. The conservatism of the system is reduced by using this inequality method and positive definite conditions, and further decreases with the increase of the Legendre parameter N. The PDC controller considers two control parameters simultaneously, which can represent the actual system more truthfully. Finally, Example 1 proves the effectiveness of the improved method in this paper. Example 2 considers time-varying probability transition perturbations. Example 3 obtains the parameters of PDC controller with different parameters N. Example 4 illustrates the practical significance of methods in this paper by introducing a time-delay inverted pendulum system with Markovian parameters.

Liang, Shuang; Liu, Zhongxin; Chen, Zengqiang

doi: 10.1177/0142331219889555pmid: N/A

In this paper, the leader-following H∞ consensus problem for discrete-time nonlinear multi-agent systems with delay and parameter uncertainty is investigated, with the objective of designing an output feedback protocol such that the multi-agent system achieves leader-following consensus and has a prescribed H∞ performance level. By model transforming, the leader-following consensus control problem is converted into robust H∞ control problem. Based on the Lyapunov function technology and the linear matrix inequality method, some new sufficient conditions are derived to guarantee the consensus of discrete-time nonlinear multi-agent systems. The feedback gain matrix and the optimal H∞ performance index are obtained in terms of linear matrix inequalities. Finally, numerical examples are provided to illustrate the effectiveness of the theoretical results.

Vichitkunakorn, Panupong; Vo, Thieu N; Razzaghi, Mohsen

doi: 10.1177/0142331219890171pmid: N/A

We present an efficient numerical method for solving fractional pantograph differential equations by applying Taylor wavelets. We give an exact formula for the Riemann-Liouville fractional integral of the Taylor wavelets. We then apply the given formula to reduce our problem to the problem of solving a system of algebraic equations. Several examples are included to show the the effectiveness of our numerical method and in comparison with previous methods.

Wang, Chunyan; Zhang, Mengqi; Li, Huan

doi: 10.1177/0142331219890730pmid: N/A

This paper investigates an adaptive fuzzy tracking control problem under arbitrary switching for a class of switched non-affine systems with completely unknown nonlinear functions and unmeasurable states. Combining with dynamic surface control (DSC) method and fuzzy approximation technique, an adaptive output-feedback common control approach is presented based on a new fuzzy reduced-order observer which is independent of any switching signal. The given design method does not rely on the boundness assumption about the control gain functions raised by the mean value theorem for non-affine systems, which contributes to the less conservation of the common controller. Meanwhile, the algebraic loop problem caused by the nonstrict-feedback structure and the repeated approximation problem in existing results are also circumvented in the given common control design process. Based on Lyapunov stability theory, the designed common controller can guarantee all the signals in the resulting closed-loop switched systems are uniformly bounded and the tracking error can converge to a small neighborhood of the origin. Two examples are provided to verify the feasibility and practicability of the proposed method.

Chen, Tim; Chen, CYJ

doi: 10.1177/0142331219891383pmid: N/A

This paper is concerned with the stability analysis and the synthesis of model-based fuzzy controllers for a nonlinear large-scale system. In evolved fuzzy NN (neural network) modeling, the NN model and LDI (linear differential inclusion) representation are established for the arbitrary nonlinear dynamics. The evolved bat algorithm (EBA) is first incorporated in the controlled algorithm of stability conditions, which could rapidly find the optimal solution and raise the control performance. This representation is constructed by taking advantage of sector nonlinearity that converts the nonlinear model to a multiple rule base linear model. A new sufficient condition guaranteeing asymptotic stability is implemented via the Lyapunov function in terms of linear matrix inequalities. Subsequently, based on this criterion and the decentralized control scheme, an evolved model-based fuzzy H infinity set is synthesized to stabilize the nonlinear large-scale system. Finally, a numerical example and simulation is given to illustrate the results.