Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

Sensor Technologies for Intelligent Transportation Systems.

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.

Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone

As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg–Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

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Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

The need for more efficient and renewable means of transport makes the development of hybrid electric vehicles (HEVs) an important topic for both automobile manufacturers and academic researchers. Noise, vibration, and harshness (NVH) of a vehicle are important factors for vehicle users and essential for successful commercialization of this vehicle type. This paper is a compact state-of-the-art review of both NVH characteristics and relevant suppression methods for HEVs. The NVH-related problems are categorized into three categories: engine, powertrain, motor-related. A detailed overview of each category/problem-type is provided, the NVH-suppression methods for different types of HEVs are introduced, and their respective advantages discussed. In addition, emerging developments and ideas for future improvements are summarized. As a result, the paper should be particularly helpful for researchers, who are interested in the development of high-performance HEVs that can provide both substantially improved ride-comfort and reduced energy-consumption.

Noise and vibration suppression in hybrid electric vehicles: State of the art and challenges

This paper presents a novel control strategy for nonlinear uncertain vehicle active suspension systems without using any function approximators [eg, neural networks (NNs) or fuzzy logic systems (FLSs)] Unlike previous results that neglect the effect of actuator dynamics, this paper incorporates the dynamics of a hydraulic actuator that is used to create the required active suspension forces into the controller design To address the nonlinearities of this hydraulic system, an approximation-free control method is introduced In this method, the widely used NNs and FLSs are not needed This leads to reduced computational burden and complexity, and thus, it is more suited for practical applications Moreover, by introducing a prescribed performance function and the associated error transform, the proposed controller can guarantee both the transient and steady-state suspension responses The stability of the closed-loop system and the suspension performance requirements are rigorously proved Finally, comparative simulations are conducted to validate the improved performance and robustness of the proposed method

Approximation-Free Control for Vehicle Active Suspensions With Hydraulic Actuator

Vibration mitigation for in-wheel switched reluctance motor driven electric vehicle with dynamic vibration absorbing structures

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2015.1317

Adaptive neural network control for active suspension system with actuator saturation

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to...

Road excitation classification for semi-active suspension system based on system response:

Comprehensive Analysis for Influence of Controllable Damper Time Delay on Semi-Active Suspension Control Strategies

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards ...

Mingming Dong

Papers

Vibration mitigation for in-wheel switched reluctance motor driven electric vehicle with dynamic vibration absorbing structures

Adaptive neural network control for active suspension system with actuator saturation

Road excitation classification for semi-active suspension system based on system response:

Comprehensive Analysis for Influence of Controllable Damper Time Delay on Semi-Active Suspension Control Strategies

Suspension system state estimation using adaptive Kalman filtering based on road classification