Road Geometry and Steering Reconstruction for Powered Two Wheeled Vehicles

International audienceThis paper deals with the estimation of both motorcycle lateral dynamics and road geometry reconstruction. A linear parameter varying (LPV) unknown input observer is designed to estimate the whole motorcycle dynamic states including road banking angles and the rider's steering torque taken into account the variation of the forward velocity. The road bank angle and the lateral slip angle are relevant parameters for improving rider's safety and handling, thus, it is interesting to estimate the road geometry. The observer convergence study is based on Lyapunov theory and the established convergence conditions are expressed in linear matrix inequalities (LMIs) formalism. The main idea consists in getting a set of conditions to design an observer transformed into a polytopic form, which estimates a part of the motorcycle dynamics states independently of some inputs (rider torque) and/or other states (zeros dynamics: roll angle) taken into account the variation of the longitudinal velocity

Gain-scheduling LPV control for autonomous vehicles including friction force estimation and compensation mechanism

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomousvehicles. It is based on a gain-scheduling linear parameter-varying (LPV) control approach combined with the use of anUnknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain-scheduling LPV controllers are usedin cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided bythe Unknown Input Observer (UIO). The LPV–UIO is designed in an optimal manner by solving a set of linear matrix inequalities(LMIs). On the other hand, the design of the kinematic and dynamic controllers lead to solve separately two LPV–LinearQuadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbanceaffected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with atrajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the casesthat the friction force estimation is used or not to show its usefulnessPeer ReviewedPostprint (author's final draft

Contributions to fuzzy polynomial techniques for stability analysis and control

The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees. The contributions of the thesis are: ¿ Improved domain of attraction estimation of nonlinear systems for both continuous-time and discrete-time cases. An iterative methodology based on invariant-set results is presented for obtaining polynomial boundaries of such domain of attraction. ¿ Extension of the above problem to the case with bounded persistent disturbances acting. Different characterizations of inescapable sets with polynomial boundaries are determined. ¿ State estimation: extension of the previous results in literature to the case of fuzzy observers with polynomial gains, guaranteeing stability of the estimation error and inescapability in a subset of the zone where the model is valid. ¿ Proposal of a polynomial Lyapunov function with discrete delay in order to improve some polynomial control designs from literature. Preliminary extension to the fuzzy polynomial case. Last chapters present a preliminary experimental work in order to check and validate the theoretical results on real platforms in the future.Pitarch Pérez, JL. (2013). Contributions to fuzzy polynomial techniques for stability analysis and control [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34773TESI

Adaptive Real-Time Estimation on Road Disturbances Properties Considering Load Variation via Vehicle Vertical Dynamics

Vehicle dynamics are directly dependent on tire-road contact forces and torques which are themselves dependent on the wheels’ load and tire-road friction characteristics. An acquisition of the road disturbance property is essential for the enhancement of vehicle suspension control systems. This paper focuses on designing an adaptive real-time road profile estimation observer considering load variation via vehicle vertical dynamics. Firstly, a road profile estimator based on a linear Kalman filter is proposed, which has great advantages on vehicle online control. Secondly, to minimize the estimation errors, an online identification system based on the Recursive Least-Squares Estimation is applied to estimate sprung mass, which is used to refresh the system matrix of the adaptive observer to improve the road estimation efficiency. Last, for mining road category from the estimated various road profile sequencse, a road categorizer considering road frequency and amplitude simultaneously is approached and its efficiency is validated via numerical simulations, in which the road condition is categorized into six special ranges, and this road detection strategy can provide the suspension control system with a better compromise for the vehicle ride comfort, handling, and safety performance

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

Transient modelling of a diesel engine and air-path control

Due to the inherent nonlinearity of the diesel engine, real-time control of the variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) valve still remains a challenging task. A controller has to be capable of coping with the transient operating condition of the engine, the interactions between the VGT and EGR, and also the trade-off effect in this control problem. In this work, novel real-time fuzzy logic controllers (RFLC) were developed and tested. Firstly, the proposed controllers were calibrated and validated in a transient diesel engine model which was developed and validated against the Caterpillar 3126B engine test bed located at the University of Sussex. The controllers were then further tested on the engine test bed. Compared to conventional controllers, the proposed controllers can effectively reduce engine emissions as well as fuel consumption. Experimental results show that compared to the baseline engine running on the Nonroad Transient Cycle (NRTC), mean values of the exhaust gas opacity and the nitrogen oxides (NOx) emission production were reduced by 36.8% and 33%, respectively. Instant specific fuel consumption of the RFLC engine was also reduced by up to 50% compared to the baseline engine during the test. Moreover, the proposed fuzzy logic controllers can also reduce development time and cost by avoiding extensive engine mapping of inlet air pressure and flow. When on-line emission measurements were not available, on-board emission predictors were developed and tested to supply the proposed fuzzy logic controller with predictions of soot and NOx production. Alternatively, adaptive neuro fuzzy inference system (ANFIS) controllers, which can learn from fuzzy logic controllers, were developed and tested. In the end, the proposed fuzzy logic controllers were compared with PI controllers using the transient engine model