Zhengchao Xie

Model predictive engine air-ratio control using online sequential extreme learning machine

Air-ratio is an important engine parameter that relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes an advanced modelling technique, called online sequential extreme learning machine (OSELM), to develop an online sequential extreme learning machine MPC (OEMPC) for air-ratio regulation according to various engine loads. The proposed OEMPC was implemented on a real engine to verify its effectiveness. Its control performance is also compared with the latest MPC for engine air-ratio control, namely diagonal recurrent neural network MPC, and conventional proportional–integral–derivative (PID) controller. Experimental results show the superiority of the proposed OEMPC over the other two controllers, which can more effectively regulate the air-ratio to specific target values under external disturbance. Therefore, the proposed OEMPC is a promising scheme to replace conventional PID controller for engine air-ratio control.

Chassis Integrated Control for Active Suspension, Active Front Steering and Direct Yaw Moment Systems Using Hierarchical Strategy

ABSTRACT This paper proposes a novel integrated controller with three-layer hierarchical structure to coordinate the interactions among active suspension system (ASS), active front steering (AFS) and direct yaw moment control (DYC). First of all, a 14-degree-of-freedom nonlinear vehicle dynamic model is constructed. Then, an upper layer is designed to calculate the total corrected moment for ASS and intermediate layer based on linear moment distribution. By considering the working regions of the AFS and DYC, the intermediate layer is functionalised to determine the trigger signal for the lower layer with corresponding weights. The lower layer is utilised to separately trace the desired value of each local controller and achieve the local control objectives of each subsystem. Simulation results show that the proposed three-layer hierarchical structure is effective in handling the working region of the AFS and DYC, while the quasi-experimental result shows that the proposed integrated controller is able to improve the lateral and vertical dynamics of the vehicle effectively as compared with a conventional electronic stability controller.

Integrating Support Vector Regression with Particle Swarm Optimization for Numerical Modeling for Algal Blooms of Freshwater

Algae-releasing cyanotoxins are cancer-causing and very harmful to the human being. Therefore, it is of great significance to model how the algae population dynamically changes in freshwater reservoirs. But the practical modeling is very difficult because water variables and their internal mechanism are very complicated and non-linear. So, in order to alleviate the algal bloom problems in Macau Main Storage Reservoir (MSR), this work proposes and develops a hybrid intelligent model combining Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) to yield optimal control of parameters that predict and forecast the phytoplankton dynamics. In this process, collected data for current month’s variables and previous months’ variables are used for model predict and forecast, respectively. In the correlation analysis of 23 water variables that monitored monthly, 15 variables such as alkalinity, Bicarbonate (HCO 3 ), dissolved oxygen (DO), total nitrogen (TN), turbidity, conductivity, nitrate, suspended solid (SS) and total organic carbon (TOC) are selected, and data from 2001 to 2008 for each of these selected variables are used for training, while data from 2009 to 2011 which are the most recent three years are used for testing. It can be seen from the numerical results that the prediction and forecast powers are respectively estimated at approximately 0.767 and 0.876, and naturally it can be concluded that the newly proposed PSO–SVR is working well and can be adopted for further studies. 2015 Elsevier Inc. All rights reserved.

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Semi-active air suspension is increasingly used on heavy-duty vehicles due to its capabilities of consuming less power and low cost and providing better ride quality. In this study, a new low cost but effective approach, fuzzy-wheelbase preview controller with wavelet denoising filter (FPW), is developed for semi-active air suspension system. A semi-active suspension system with a rolling lobe air spring is firstly modeled and a novel front axle vertical acceleration-based road prediction model is constructed. By adopting a sensor on the front axle, the road prediction model can predict more reliable road information for the rear wheel. After filtering useless signal noise, the proposed FPW can generate a noise-insensitive control damping force. Simulation results show that the ride quality, the road holding, the handling capability, the road friendliness, and the comprehensive performance of the semi-active air suspension with FPW outperform those with the traditional active suspension with PID-wheelbase preview controller (APP). It can also be seen that, with the addition of the wavelet filter, the impact of sensor noise on the suspension performance can be minimized.

Design and analysis of an integrated sliding mode control–two-point wheelbase preview strategy for a semi-active air suspension with stepper motor-driven gas-filled adjustable shock absorber

This article proposes an integrated sliding mode control–two-point wheelbase preview strategy for semi-active air suspension system with gas-filled adjustable shock absorber. First of all, a vehicle suspension model with rolling lobe air spring and gas-filled adjustable shock absorber is built, following with a road input model for the front wheel. By describing the detailed structure and working process of the gas-filled adjustable shock absorber, the regulating mechanism between the stepper motor and the designed gas-filled adjustable shock absorber is established. Subsequently, the sliding mode control algorithm is applied to generate the desired damping force with the real-time state of the vehicle. Moreover, to predetermine the road profile for the rear wheel, a two-point wheelbase preview approach is proposed and its superiority is also illustrated as compared with the conventional single-point wheelbase preview approach. To evaluate the performance of the proposed system, numerical analysis is conducted with other three comparative schemes, namely, passive suspension system, active suspension system with H infinity control, and sliding mode control–controlled semi-active air suspension system with adjustable shock absorber. Simulation results show that the integrated sliding mode control–two-point wheelbase preview strategy can be successfully utilized in the semi-active air suspension system with stepper motor-driven gas-filled adjustable shock absorber, and the vehicle performance with the proposed system can be greatly improved.

Design of an Active Vehicle Suspension Based on an Enhanced PID Control with Wheelbase Preview and Tuning Using Genetic Algorithm

In this work, a new model of active suspension on a half-car with a wheelbase preview of information and a nonlinear differentiator are developed. First, a sensor placed on the front suspension collects and sends road input as preview information to the rear suspension system. Then, with given preview information and the feedback signal of vehicle state, an enhanced Proportional-Integral-Differential (PID) controller combined with a nonlinear differentiator is used to generate control signal to apply active force on rear suspension in order to attenuate the suspension vibration. The PID controller with a nonlinear differentiator provides more stable results than regular PID control in the presence of noise. The numerical result further shows that with preview information considered, the accelerations of the unsprung masses in the front and rear wheels both decrease in comparison to those of the suspension controlled by regular PID control. The active suspension system developed in this work is then tuned by using Genetic Algorithm (GA) to obtain optimum configurations on several design parameters so as to achieve a better ride quality.

Design of a fuzzy preview active suspension system for automobiles

The paper proposes a new active vehicle suspension with wheelbase preview information for a half-car model. A sensor placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system. Then a fuzzy controller is used to process the obtained preview information and the feedback signal of vehicle state. The objective of application of the fuzzy controller is to improve the suspension robustness against sensor noise. Numerical simulations show that the fuzzy controller provides higher robust performance than a conventional PID controller in the presence of sensor noise. Moreover, the preview information significantly decreases the rear wheel unsprung mass acceleration so that the riding comfort can be improved.

Design of a Road Friendly SAS System for Heavy-Duty Vehicles Based on a Fuzzy-Hybrid-ADD and GH-Control Strategy

Semiactive suspension (SAS) system has been widely used for its outstanding performance in offering competent ride quality, road holding, and handling capacity. However, the road friendliness is also one of the crucial factors that should be attached in the design of the SAS system for heavy-duty vehicles. In this study, a fuzzy controlled hybrid-acceleration driven damper (ADD) and ground hook- (GH-) control strategy is proposed for SAS system of heavy-duty vehicles. Firstly, a quarter-vehicle model with SAS system is constructed. Then, aiming to improve the ride quality and road friendliness, a hybrid-ADD and GH-control strategy is proposed under the coordination of the fuzzy controller. Numerical results show that the ride quality and road friendliness of the SAS system with the proposed control strategy outperform those with traditional hybrid-sky hook and ground hook-control strategy. It is also verified that the proposed strategy is superior to the sole ADD approach and sole ground hook approach in improving the vehicle overall performance.

Multi-objective sliding mode control on vehicle cornering stability with variable gear ratio actuator-based active front steering systems

Active front steering (AFS) is an emerging technology to improve the vehicle cornering stability by introducing an additional small steering angle to the driver’s input. This paper proposes an AFS system with a variable gear ratio steering (VGRS) actuator which is controlled by using the sliding mode control (SMC) strategy to improve the cornering stability of vehicles. In the design of an AFS system, different sensors are considered to measure the vehicle state, and the mechanism of the AFS system is also modelled in detail. Moreover, in order to improve the cornering stability of vehicles, two dependent objectives, namely sideslip angle and yaw rate, are considered together in the design of SMC strategy. By evaluating the cornering performance, Sine with Dwell and accident avoidance tests are conducted, and the simulation results indicate that the proposed SMC strategy is capable of improving the cornering stability of vehicles in practice.

An Experimental Study on Dynamics of a Novel Dual-Belt Continuous Variable Transmission Based on a Newly Developed Test Rig

A novel dual-belt Van Doorne’s continuous variable transmission (DBVCVT) system, which is applicable to heavy-duty vehicles, has been previously proposed by the authors in order to improve the low torque capacity of traditional single-belt CVT. This DBVCVT is a novel design among continuously variable transmissions and is necessary to be prototyped for experimental study, and the analytical dynamic model for this DBVCVT also needs to be experimentally validated. So, this work originally fabricated a prototype of DBVCVT and integrates this prototype to a light-load hardware-in-the-loop test rig by replacing the engine and load equipment with the AC motor and magnetic powder dynamometer. Moreover, with the use of this newly developed test rig, this work implements the experimental study of this DBVCVT for the first time. The comparison of experimental and simulation results validates the previously proposed analytical model for DBVCVT, and some basic characteristics of the DBVCVT in terms of the reliability, speed ratio, and transmission efficiency are also experimentally studied. In all, this developed test rig with the analytical model lays the foundation for further study on this novel DBVCVT.