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This article introduces a robust H-infinity fuzzy static output feedback (RHF-SOF) control with norm-bounded strategy to enhance the path-following performance for the four-wheel-independent-driven electric vehicles (FWID-EVs). Firstly, taking the parameter uncertainties including vehicle mass, moment of inertia, longitudinal velocity, and tire cornering stiffness into account, a fuzzy optimization model is constructed to approximate the nonlinear characteristic of the vehicle system using both the parameter uncertainty reduction strategy and polytope reduction strategy. Secondly, with consideration of the difficulty of the online lateral velocity measurement, a Lyapunov stability-based RHF-SOF control with the gain scheduling method is proposed to control the variables of the path following. Thirdly, since the actuator saturation can deteriorate the control performances, a norm-bounded strategy is designed to address the nonlinear saturation problem. The proposed controller is verified and evaluated through the hardware-in-the-loop experimental test with different maneuvers. [ABSTRACT FROM AUTHOR]

In this paper, a robust control algorithm is designed to achieve a finite-time vehicle height and posture control through electronically controlled air suspension (ECAS) system subject to actuator faults, uncertainties under non-stationary condition. To achieve simultaneous position control of four corners of vehicle, synchronization errors between corners are taken to form a synchronization control strategy. Furthermore, to improve the system convergence speed and robustness, finite-time stability constrain is applied and H ∞ index is designed strategically in order to develop a novel robust finite-time controller. Since the solenoid valves in the ECAS system may degrade with the frequent switching, actuator fault and uncertain parameters are considered in this study to design the proposed fault-tolerant control methodology. Meanwhile, road disturbance is applied to the vehicle with the ECAS system to provide a non-stationary condition. Several software-in-the-loop tests and hardware-in-the-loop test are conducted to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has attracted considerable attention owing to its great contributions to the vertical dynamics of vehicle. This paper investigates the non-fragile output feedback control problem of the uncertain vehicle active suspension with stochastic network-induced delay. Firstly, taking the variation of sprung and unsprung masses into consideration, an interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is introduced to describe the nonlinear characteristics of active suspension systems (ASSs). Secondly, to ensure that the control strategy is practicable when some states are unmeasured, a novel output feedback method is proposed by employing a variable substitution approach. Meanwhile, in order to approximate the real physical conditions of the control system, the gain perturbations are taken into account. Thirdly, with regard to the complexity of signal transmission delay in network control process, a more generalized lumped delay form is employed to represent the network-induced delay. Moreover, to describe the stochasticity of lumped delay, a Markovian process is introduced. Finally, both numerical simulations and experimental tests are carried out to examine the effectiveness and practicability of the proposed controller. [ABSTRACT FROM AUTHOR]

This research proposes a novel hydro-pneumatic suspension structure based on high-speed on-off solenoid valves, which can realize two spring force modes and four damping force modes. The basic working principles of the HPS with multiple output force modes are analyzed to establish the damping and stiffness features mathematical models. Based on the parameters taken from a mine dump truck, the stiffness coefficient and damping coefficient of the proposed HPS are matched firstly, then the initial charging pressure of accumulators and the diameter of throttle valves are determined. Finally, the validity of the proposed model is demonstrated by comparing the force-displacement and the force-velocity responses of the model with the corresponding measured experimental data under a broad range of excitations. [ABSTRACT FROM AUTHOR]

Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect. Collecting engine vibration signals from an engine cylinder block is an effective way to detect engine knock. This paper proposes an intelligent engine knock detection system based on engine vibration signals. First, filtered signals are obtained by utilizing variational mode decomposition (VMD), which decomposes the original time domain signals into a series of intrinsic mode functions (IMFs). Moreover, the values of the balancing parameter and the number of IMF modes are optimized using genetic algorithm (GA). IMFs with sample entropy higher than the mean are then selected as sensitive subcomponents for signal reconstruction and subsequently removed. A multiple feature learning approach that considers time domain statistical analysis (TDSA), multi-fractal detrended fluctuation analysis (MFDFA) and alpha stable distribution (ASD) simultaneously, is utilized to extract features from the denoised signals. Finally, the extracted features are trained by sparse Bayesian extreme learning machine (SBELM) to overcome the sensitivity of hyperparameters in conventional machine learning algorithms. A test rig is designed to collect the raw engine data. Compared with other technology combinations, the optimal scheme from signal processing to feature classification is obtained, and the classification accuracy of the proposed integrated engine knock detection method can achieve 98.27%. We successfully propose and test a universal intelligence solution for the detection task. [ABSTRACT FROM AUTHOR]

As a key environment perception technology of autonomous driving or driver assistance systems, lane detection is to ensure vehicles to drive safely in corresponding lane. However, existing lane detection algorithms for two-lane detection focus on using various filtering methods to reduce the impact of useless information, resulting in low accuracy and low efficiency. In this paper, a novel Adaptive Region of Interest (A-ROI) extraction method is proposed to improve the accuracy and real-time performance of the two-lane detection algorithm. Three key technologies are introduced to solve the problems. First, A-ROI, which only focuses on the lane where the vehicle is located, is applied to the Bird's-Eye-View image obtained by using Inverse Perspective Mapping (IPM). Next, based on Bayesian framework and Likelihood models, a lane feature extraction method with a lane-like feature filter is used for edge detection. Finally, an improved Random Sample Consensus (RANSAC) algorithm is introduced by using a filter that can remove noisy lane data. The performance of the proposed A-ROI method together with the improved lane detection method is evaluated via simulation of various scenarios. Experimental results show the proposed method has better accuracy and real-time performance than the traditional lane detection methods. [ABSTRACT FROM AUTHOR]

Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model. [ABSTRACT FROM AUTHOR]

The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA model of the tire nonlinear cornering properties is introduced. In this approach, the highly nonlinear dynamic of the tire cornering properties is well approximated by a set of affine maps which relate inputs and outputs. These maps are defined in disjunctive regions in the regression space, itself composed of system inputs and outputs. The tire cornering properties tests are firstly carried out through a high-performance flat-plate test bench, thus the experimental data which can accurately reflect the tire cornering properties is obtained. On this basis, the PWA identification of the tire cornering properties is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices, which are respectively achieved by means of fuzzy c-means clustering, weighted least-squares and support vector machines. Finally, to verify the PWA model accuracy in approximating the tire nonlinear cornering properties, the comparison between the simulation results of the PWA identification model and the experimental data is conducted and the comparison results are analyzed. [ABSTRACT FROM AUTHOR]

In this research, a multi-objective frequency domain-constrained static output feedback control is developed for the delayed active suspension system with wheelbase preview information. Firstly, the model of the half-vehicle active suspension system with wheelbase preview information and time delay is constructed by employing the augmented method. Secondly, in vertical vibration, the human body senses are more intense when the frequency of the external disturbance is in the range of 4–8 Hz. Therefore, a finite frequency control method is proposed for the controller design with robust H ∞ and generalized H 2 performance. Thirdly, considering that a full online knowledge of states is not available in practical suspension systems, a static output feedback strategy is proposed to guarantee the feasibility of the controller by using a two-stage method. Finally, simulation study is given to explain the effectiveness and superiority of the presented controller. [ABSTRACT FROM AUTHOR]

The assessment of vehicle dynamic comfort to human body vibration varies from person to person. Even though the dynamic comfort rating can be obtained by placing measurement devices over the cabin, passengers may have dissimilar sensations. For many car manufacturers, both subject and objective measurements are used together at development of new cars. To effectively assess the dynamic comfort of vehicle, this research explores the correlation between the objective measurement of the whole body vibration, hand-transmitted vibration and subjective assessment. The whole body vibration is measured from the backrest, seat surface and vehicle floor, whereas the hand-transmitted vibration is measured from the steering wheel. Four different vehicles are driven at various speeds on five types of pavements. Both simple linear regression and polynomial regression models based on Stevens's power law from psychophysics are built to correlate the subjective and objective data. The regression results show that (1) the seat surface is more reliable than other positions for assessing the human body vibration; (2) the hand transmitted vibration on rough roads is more trustful for dynamic comfort evaluation than on smooth roads; (3) the overall dynamic comfort value is the most reliable index for dynamic comfort evaluation. [ABSTRACT FROM AUTHOR]

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load. [ABSTRACT FROM AUTHOR]

Tire cornering characteristics have significant influence on vehicle lateral dynamics control. Unlike traditional tire mechanics models which are established based on the research experience or the mechanics mechanism, in this study, a novel experimental data-driven modeling approach is presented to model the tire cornering characteristics based on piecewise affine (PWA) identification method. In this approach, the highly nonlinear dynamic of the tire cornering characteristics is well approximated by several affine submodels acting on different regions. To obtain the experimental data which can accurately reflect the tire cornering characteristics, the tire tests are firstly carried out through a high-performance flat-plate test bench. On this basis, the PWA identification of the tire cornering characteristics is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices. The simulation results of the PWA identification model are finally compared with the experimental data to illustrate that the identified model has high accuracy in approximating the tire nonlinear cornering characteristics under wide range driving conditions. [ABSTRACT FROM AUTHOR]

Maximum margin clustering (MMC) is a recent approach of applying margin maximization in supervised learning to unsupervised learning, aiming to partition the data into clusters with high discrimination. Recently, extreme learning machine (ELM) has been applied to MMC (called iterative ELM clustering or ELMCIter) which maximizes the data discrimination by iteratively training a weighted extreme learning machine (W-ELM). In this way, ELMCIter achieves a substantial reduction in training time and provides a unified model for both binary and multi-class clusterings. However, there exist two issues in ELMCIter: (1) random feature mappings adopted in ELMCIter are unable to well obtain high-quality discriminative features for clustering and (2) a large model is usually required in ELMCIter because its performance is affected by the number of hidden nodes, and training such model becomes relatively slow. In this paper, the hidden layer in ELMCIter is encoded by an approximate empirical kernel map (AEKM) rather than the random feature mappings, in order to solve these two issues. AEKM is generated from low-rank approximation of the kernel matrix, derived from the input data through a kernel function. Our proposed method is called iterative AEKM for clustering (AEKMCIter), whose contributions are: (1) AEKM can extract discriminative and robust features from the kernel matrix so that better performance is always achieved in AEKMCIter and (2) AEKMCIter produces an extremely small number of hidden nodes for low memory consumption and fast training. Detailed experiments verified the effectiveness and efficiency of our approach. As an illustration, on the MNIST10 dataset, our approach AEKMCIter improves the clustering accuracy over ELMCIter up to 5%, while significantly reducing the training time and the memory consumption (i.e., the number of hidden nodes) up to 1/7 and 1/20, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the tasks of height and posture motion control for an electronically controlled active air suspension (AAS) system. A mathematical model of a vehicle body with AAS system is established to describe the dynamic characteristics and then formulated into a multi-input multi-output nonlinear system by considering parametric uncertainties and unmodelled dynamics. Based on this mathematical model, a synchronization control strategy is proposed to adjust the heights of adjacent AASs simultaneously, driving the pitch and roll angles closely to an arbitrarily neighborhood of zero, achieving global uniform ultimate boundedness. The proposed controller is robust to parametric uncertainties and external disturbances. A projection operator is utilized to limit the estimated parameters to their corresponding prescribed bounds in finite time. A co-simulation is conducted by combining a virtual vehicle plant with ASS system in AMEsim with the proposed synchronization controller in MATLAB/Simulink. Simulation results demonstrate that the proposed synchronization controller is effective and robust. [ABSTRACT FROM AUTHOR]

In modern automotive engines, air–fuel ratio (AFR) strongly affects exhaust emissions, power, and brake-specific consumption. AFR control is therefore essential to engine performance. Most existing engine built-in AFR controllers, however, are lacking adaptive capability and cannot guarantee long-term control performance. Other popular AFR control approaches, like adaptive PID control or sliding mode control, are sensitive to noise or needs prior expert knowledge (such as the engine model of AFR). To address these issues, an initial-training-free online sequential extreme learning machine (ITF-OSELM) is proposed for the design of AFR controller, and hence a new adaptive AFR controller is developed. The core idea is to use ITF-OSELM for identifying the AFR dynamics in an online sequential manner based on the real-time engine data, and then use the ITF-OSELM model to calculate the necessary control signal, so that the AFR can be regulated. The contribution of the proposed approach is the integration of the initial-training-free online system identification algorithm in the controller design. Moreover, to guarantee the stability of the closed-loop control system, a stability analysis is also conducted. To verify the feasibility and evaluate the performance of the proposed AFR control approach, simulations on virtual engine and experiments on real engine have been carried out. Both results show that the proposed approach is effective for AFR regulation. [ABSTRACT FROM AUTHOR]

With the increasing installed power of the wind turbines, the necessity of condition monitoring for wind turbine drivetrain cannot be neglected any longer. A reliable and rapid response fault diagnosis is vital for the wind turbine drivetrain system. The existing manual inspection-based methods are difficult to accomplish the real-time compound-fault monitoring task. To solve this problem, this paper proposes a novel dual extreme learning machines (Dual-ELMs) based fault diagnostic framework for feature extraction and fault pattern recognition. At the stage of feature learning, this paper applies the local mean decomposition (LMD) method to extract the production functions from the raw vibration signals. Compared with the traditional empirical mode decomposition method, the LMD method has a stronger ability to restrain the mode mixing and endpoints effect. At the stage of compound-fault classification, unlike the other widely-used classifiers, the proposed Dual-ELM networks inherit the advantages of the original extreme learning machines (ELMs), that employs two basic ELM networks for the compound-fault classification, and it does not need iterative fine-tuning of parameters. Thus the learning speed is faster than the other combinations of classifiers. The experimental validity of the proposed algorithm was conducted on a test rig for vibration analysis, which demonstrated that the proposed Dual-ELMs based fault diagnostic method provides an effective measure for the observed machinery than the other available fault diagnostic methods in aspects of feature extraction and compound-fault recognition. [ABSTRACT FROM AUTHOR]

The semi-active suspension (SAS) system has been one of the most attractive topics due to its simplicity and effectiveness in the control of vehicle dynamics. This research proposes a cuckoo search optimized proportional-integral-derivative (CS-PID) strategy for the damping force control of the semi-acive suspension system in order to improve vehicle ride quality. Firstly, a quarter-car suspension model with air spring and variable hydraulic damper (VHD) is developed. By constructing the detailed analytical model and describing the working process, the regulating mechanism and external characteristics of the VHD are presented. Subsequently, the CS-PID strategy is designed to generate the desired damping force according to the vehicle states in real-time, followed with the evaluation of the proposed strategy. Finally, the experimental tests are carried out to verify the accuracy of the VHD model and examine the feasibility of the proposed strategy. The numerical simulation reveal that the proposed control strategy is effective in improving the vehicle performance and the experimental results show that the CS-PID strategy can be successfully implemented in the suspension system for practical use. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a kind of random projection-based neural networks, whose advantages are fast training speed and high generalization. However, three issues can be improved in ELM: (1) the calculation of output weights takes OL2N time (with N training samples and L hidden nodes), which is relatively slow to train a model for large N and L; (2) the manual tuning of L is tedious, exhaustive and time-consuming; (3) the redundant or irrelevant information in the hidden layer may cause overfitting and may hinder high generalization. Inspired from compressive sensing theory, we propose an efficient ELM via very sparse random projection (VSRP) called VSRP-ELM for training with large N and L. The proposed VSRP-ELM adds a novel compression layer between the hidden layer and output layer, which compresses the dimension of the hidden layer from N×L to N×k(wherek under projection with random sparse-Bernoulli matrix. The advantages of VSRP-ELM are (1) faster training time Ok2N,k is obtained for large L; (2) the tuning time of L can be significantly reduced by initializing a large L, and then shrunk to k using just a few trials, while maintaining a comparable result of the original model accuracy; (3) higher generalization may be benefited from the cleaning of redundant or irrelevant information through VSRP. From the experimental results, the proposed VSRP-ELM can speed ELM up to 7 times, while the accuracy can be improved up to 6%. [ABSTRACT FROM AUTHOR]

Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images. [ABSTRACT FROM AUTHOR]

Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN. [ABSTRACT FROM AUTHOR]

Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level. [ABSTRACT FROM AUTHOR]