Jianchuan Yin

Global Asymptotic Model-Free Trajectory-Independent Tracking Control of an Uncertain Marine Vehicle: An Adaptive Universe-Based Fuzzy Control Approach

Motivated by the challenging difficulty in tracking an uncertain marine vehicle (MV) with unknown dynamics and disturbances to any unmeasurable/unknown trajectory, which is unresolved, an adaptive universe-based fuzzy control (AUFC) scheme with retractable fuzzy partitioning (RFP) in global universe of discourse (UoD) is created to achieve global asymptotic model-free trajectory-independent tracking. By defining an error surface and intensively exploring the MV structure, tracking error dynamics are sufficiently trimmed via separating external unknowns including trajectory dynamics and disturbances from internal nonlinearities dependent on tracking errors. An innovative retractable fuzzy approximator (RFA) using the RFP is developed to estimate internal nonlinearities and does not require a priori knowledge on the UoD, thereby contributing to a globally adaptive approximation based control approach in conjunction with Lyapunov synthesis. Together with RFA residuals, external unknowns are globally dominated by adaptive universal compensators driven by tracking error surface. Eventually, tracking errors and their derivatives globally asymptotically converge to the origin and all other signals of the closed-loop system are bounded. Simulation studies demonstrate superior performance of the proposed AUFC scheme in terms of both tracking and approximation.

Improved Gath–Geva clustering for fuzzy segmentation of hydrometeorological time series

In this paper, an improved Gath–Geva clustering algorithm is proposed for automatic fuzzy segmentation of univariate and multivariate hydrometeorological time series. The algorithm considers time series segmentation problem as Gath–Geva clustering with the minimum message length criterion as segmentation order selection criterion. One characteristic of the improved Gath–Geva clustering algorithm is its unsupervised nature which can automatically determine the optimal segmentation order. Another characteristic is the application of the modified component-wise expectation maximization algorithm in Gath–Geva clustering which can avoid the drawbacks of the classical expectation maximization algorithm: the sensitivity to initialization and the need to avoid the boundary of the parameter space. The other characteristic is the improvement of numerical stability by integrating segmentation order selection into model parameter estimation procedure. The proposed algorithm has been experimentally tested on artificial and hydrometeorological time series. The obtained experimental results show the effectiveness of our proposed algorithm.

A Hybrid Model of Partial Least Squares and RBF Neural Networks for System Identification

A novel learning algorithm is presented to construct radial basis function (RBF) networks by incorporating partial least squares (PLS) regression method. The algorithm selects hidden units one by one with PLS regression method until an adequate network is achieved, and the resulting minimal RBF-PLS (MRBF-PLS) network exhibits satisfying generalization performance and noise toleration capability. The algorithm provides an efficient approach for system identification, and this is illustrated by modelling nonlinear function and chaotic time series.

Fuzzy Uncertainty Observer Based Filtered Sliding Mode Trajectory Tracking Control of the Quadrotor

In this paper, a filtered sliding mode control (FSMC) scheme based on fuzzy uncertainty observer (FUO) for trajectory tracking control of a quadrotor unmanned aerial vehicle (QUAV) is proposed. To be specific, the dynamics model of QUAV is decomposed into three subsystems. By virtue of the cascaded structure, sliding-mode-based virtual control laws can be recursively designed. In order to remove the smoothness requirements on intermediate signals, a series of first-order filters are employed to reconstruct sliding mode control signals together with their first derivatives. Moreover, fuzzy uncertainty observers are employed to indirectly estimate lumped unknown nonlinearities including system uncertainties and external disturbances and make compensation for the QUAV system. Stability analysis and uniformly ultimately bounded tracking errors and states can be guaranteed by the Lyapunov approach. Simulation studies demonstrate the effectiveness and superiority of the proposed tracking control scheme.

Enhanced extreme learning machine with modified gram-schmidt algorithm

Extreme learning machine (ELM) has shown to be extremely fast with better generalization performance However, the implementation of ELM encounters two problems First, ELM tends to require more hidden nodes than conventional tuning-based algorithms Second, subjectivity is involved in choosing hidden nodes number In this paper, we apply the modified Gram-Schmidt (MGS) method to select hidden nodes which maximize the increment to explained variance of the desired output The Akaikes final prediction error (FPE) criterion are used to automatically determine the number of hidden nodes In comparison with conventional ELM learning method on several commonly used regressor benchmark problems, our proposed algorithm can achieve compact network with much faster response and satisfactory accuracy.

On-Line Tuning of a Neural PID Controller Based on Variable Structure RBF Network

This paper presents the use of a variable structure radial basis function (RBF) network for identification in PID control scheme. The parameters of PID control are on-line tuned by a sequential learning RBF network, whose hidden units and connecting parameters are adapted on-line. The RBF-network-based PID controller simplifies modeling procedure by learning input-output samples while keep the advantages of traditional PID controller simultaneously. Simulation results of ship course control simulation demonstrate the applicability and effectiveness of the intelligent PID control strategy.

Modified Gram-Schmidt Algorithm for Extreme Learning Machine

Extreme learning machine (ELM) has shown to be extremely fast with better generalization performance. The basic idea of ELM algorithm is to randomly choose the parameters of hidden nodes and then use simple generalized inverse operation to solve for the output weights of the network. Such a procedure faces two problems. First, ELM tends to require more random hidden nodes than conventional tuning-based algorithms. Second, subjectivity is involved in choosing appropriate number of random hidden nodes. In this paper, we propose an enhanced-ELM(en-ELM) algorithm by applying the modified Gram-Schmidt (MGS) method to select hidden nodes in random hidden nodes pool. Furthermore, enhanced-ELM uses the Akaikes final prediction error (FPE) criterion to automatically determine the number of random hidden nodes. In comparison with conventional ELM learning method on several commonly used regressor benchmark problems, enhanced-ELM algorithm can achieve compact network with much faster response and satisfactory accuracy.

An On-Line Learning Radial Basis Function Network and Its Application

To improve the on-line predictive capability of radial basis function (RBF) networks, a novel sequential learning algorithm is developed referred to as sequential orthogonal model selection (SOMS) algorithm. The RBF network is adapted on-line for both network structure and connecting parameters. Based on SOMS algorithm, a multi-step predictive control strategy is introduced and applied to ship control. Simulation results of ship course control experiment demonstrate the applicability and effectiveness of the SOMS algorithm.

Nonsingular Terminal Sliding Mode Based Trajectory Tracking Control of an Autonomous Surface Vehicle with Finite-Time Convergence

In this paper, a nonsingular terminal sliding mode (NTSM) based tracking control (NTSMTC) scheme for an autonomous surface vehicle (ASV) subject to unmodelled dynamics and unknown disturbances is proposed. The salient features of the NTSMTC scheme are as follows: (1) The NTSMTC scheme is designed by combining the NTSM technique with an established finite-time unknown observer (FUO) which enhances the system robustness significantly and achieves accurate tracking performance; (2) By virtue of the NTSMTC scheme, not only that unknown estimation errors are controlled to zero but also tracking errors can be stabilized to zero in a finite time; (3) The finite-time convergence of the entire closed-loop control system can be ensured by the Lyapunov approach. Simulation studies are further provided to demonstrate the effectiveness and remarkable performance of the proposed NTSMTC scheme for trajectory tracking control of an ASV.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system (ANFIS) model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules: the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system (FIS) structure, three approaches which include grid partition (GP), fuzzy c-means (FCM) and sub-clustering (SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.