Real-time estimation of the road bank and grade angles with unknown input observers

This is an Accepted Manuscript of an article published by Taylor & Francis in Vehicle System Dynamics on 2017-01-24, available online: http://dx.doi.org/10.1080/00423114.2016.1275706This paper proposes an approach for the estimation of the road angles independent from the road friction conditions. The method employs unknown input observers on the roll and pitch dynamics of the vehicle. The correlation between the road angle rates and the pitch/roll rates of the vehicle is also investigated to increase the accuracy. Dynamic fault thresholds are implemented in the algorithm to ensure reliable estimation of the vehicle body and road angles. Performance of the proposed approach in reliable estimation of the road angles is experimentally demonstrated through vehicle road tests. Road test experiments include various driving scenarios on different road conditions to thoroughly validate the proposed approach

Full Vehicle State Estimation Using a Holistic Corner-based Approach

Vehicles' active safety systems use different sensors, vehicle states, and actuators, along with an advanced control algorithm, to assist drivers and to maintain the dynamics of a vehicle within a desired safe range in case of instability in vehicle motion. Therefore, recent developments in such vehicle stability control and autonomous driving systems have led to substantial interest in reliable road angle and vehicle states (tire forces and vehicle velocities) estimation. Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road angles. In this direction, developing a flexible and reliable estimation structure at a reasonable cost to operate the available sensor data for the proper functioning of active safety systems in current vehicles is a preeminent objective of the car manufacturers in dealing with the technological changes in the automotive industry. This thesis presents a novel generic integrated tire force and velocity estimation system at each corner to monitor tire capacities and slip condition individually and to address road uncertainty issues in the current model-based vehicle state estimators. Tire force estimators are developed using computationally efficient nonlinear and Kalman-based observers and common measurements in production vehicles. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties including tire properties, inflation pressure, and effective rolling radius, does not need tire parameters and road friction information, and can transfer from one car to another. The main challenges for velocity estimation are the lack of knowledge of road friction in the model-based methods and accumulated error in kinematic-based approaches. To tackle these issues, the lumped LuGre tire model is integrated with the vehicle kinematics in this research. It is shown that the proposed generic corner-based estimator reduces the number of required tire parameters significantly and does not require knowledge of the road friction. The stability and performance of the time-varying velocity estimators are studied and the sensitivity of the observers' stability to the model parameter changes is discussed. The proposed velocity estimators are validated in simulations and road experiments with two vehicles in several maneuvers with various driveline configurations on roads with different friction conditions. The simulation and experimental results substantiate the accuracy and robustness of the state estimators for even harsh maneuvers on surfaces with varying friction. A corner-based lateral state estimation is also developed for conventional cars application independent of the wheel torques. This approach utilizes variable weighted axles' estimates and high slip detection modules to deal with uncertainties associated with longitudinal forces in large steering. Therefore, the output of the lateral estimator is not altered by the longitudinal force effect and its performance is not compromised. A method for road classification is also investigated utilizing the vehicle lateral response in diverse maneuvers. Moreover, the designed estimation structure is shown to work with various driveline configurations such as front, rear, or all-wheel drive and can be easily reconfigured to operate with different vehicles and control systems' actuator configurations such as differential braking, torque vectoring, or their combinations on the front or rear axles. This research has resulted in two US pending patents on vehicle speed estimation and sensor fault diagnosis and successful transfer of these patents to industry

Continuous-Time Switched H∞ Proportional-Integral observer: Application for sideslip and road bank angles estimation

International audience— In this work, a Continuous-Time Switched H ∞ Proportional-Integral (CTSH ∞ PI) observer is presented. The estimation method is based on a proportional-integral observer introduced by [13], [11], [12]. The estimation method is used to estimate simultaneously the state variables and unknown inputs of switched systems. A design method is established using a common Lyapunov function and H ∞ norm. Its stability and global convergence conditions are proved and expressed in term of LMIs. All conditions are established under given switching signals. The estimation method is applied in vehicle dynamics to estimate simultaneously the vehicle sideslip angle and road bank angle. Moreover, the switching signal is deduced from measured premise variables. Simulation tests with experimental data are included to demonstrate the advantage of this method

Development of an Integrated Estimation Method for Vehicle States, Parameters and Tire Forces

Stability and desirable performance of vehicle control systems are directly dependent on the quality and accuracy of sensory and estimated data provided to the controllers. Tire forces and vehicle states such as lateral and longitudinal velocities are required information for most vehicle control systems. However, there are challenges associated with efficient estimation of tire forces and vehicle states. Furthermore, changes in vehicle inertial parameters, road grade, and bank angle all have major influences on both tire forces and vehicle states. Efficient identification of these parameters requires sufficient information about a set of vehicle states and tire forces. This duality relationship mandates the development of efficient methods for simultaneous estimation of tire forces, vehicle states, and vehicle and road parameters. This research proposes the design of an integrated estimation structure that can simultaneously estimate tire forces, vehicle velocity, vehicle inertial parameters, and road angles. The proposed structure is robust against variations in tire parameters because of tire brand, wear, and road friction coefficient. The methods developed in this thesis are all validated experimentally on multiple vehicle platform.4 month

On the vehicle sideslip angle estimation: a literature review of methods, models and innovations

Typical active safety systems controlling the dynamics of passenger cars rely on real-time monitoring of the vehicle sideslip angle (VSA), together with other signals like wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, known as the yaw rate. The VSA (aka attitude or “drifting” angle) is defined as the angle between the vehicle longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory therefore it is a vital piece of information enabling directional stability assessment, in transients following emergency manoeuvres for instance. As explained in the introduction the VSA is not measured directly for impracticality and it is estimated on the basis of available measurements like wheel velocities, linear and angular accelerations etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e. Observer-based and Neural Network-based, focusing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. Advantages and limitations of each technique have been highlighted and discussed

Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Nowadays, industrial plants are calling for high-performance fault diagnosis techniques to meet stringent requirements on system availability and safety in the event of component failures. This paper deals with robust fault estimation problems for stochastic nonlinear systems subject to faults and unknown inputs relying on Takagi-Sugeno fuzzy models. Augmented approach jointly with unknown input observers for stochastic Takagi-Sugeno models is exploited here, which allows one to estimate both considered faults and full system states robustly. The considered unknown inputs can be either completely decoupled or partially decoupled by observers. For the un-decoupled part of unknown inputs, which still influence error dynamics, stochastic input-to-state stability properties are applied to take nonzero inputs into account and sufficient conditions are achieved to guarantee bounded estimation errors under bounded unknown inputs. Linear matrix inequalities are employed to compute gain matrices of the observer, leading to stochastic input-to-state-stable error dynamics and optimization of the estimation performances against un-decoupled unknown inputs. Finally, simulation on wind turbine benchmark model is applied to validate the performances of the suggested fault reconstruction methodologies

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error

Real time kick estimation and monitoring in managed pressure drilling system

The influx of reservoir fluid (kick) has a significant impact on drilling operations. Unmitigated kick can lead to a blowout causing financial losses and impacting human lives on the rig. Kick is an unmeasured disturbance in the system, and so detection, estimation, and mitigation are essential for the safety and efficiency of the drilling operation. Our main objective is to develop a real time warning system for a managed pressure drilling (MPD) system. In the first part of the research, an unscented Kalman filter (UKF) based estimator was implemented to simultaneously estimate the bit flow-rate, and kick. The estimated kick is further used to predict the impact of the kick. Optimal control theory is used to calculate the time to mitigate the kick in the best case scenario. An alarm system is developed based on total predicted influx and pressure rise in the system and compared with actual well operation control matrix. Thus, the proposed method can estimate, monitor, and manage kick in real time, enhancing the safety and efficiency of the MPD operation. So, a robust warning framework for the operators based on real life operational conditions is created in the second part of the research. Proposed frameworks are successfully validated by applying to several case studies

Commande couplée longitudinale/latérale de véhicules par platitude et estimation algébrique

International audienceThis paper deals with a coupled longitudinal/lateral vehicle nonlinear control and road bank angle estimation. The control law is designed using the differential flatness property of nonlinear longitudinal and lateral vehicle model. An algebraic estimation approach is employed to estimate simultaneously the vehicle dynamic parameters (like lateral forces and roll angle) and unknown inputs (like road bank angle and corrective yaw moment). The control and estimation approaches use also the algebraic estimators to perform the filtering and the numerical derivatives of noisy measurements. To perform some coupled vehicle tasks like an automatic line keeping of straight or curved lines, a line change maneuver, obstacle avoidance, a combined control of longitudinal and lateral vehicle motions is required. Note that this controller is designed via steering angle and traction/braking torque of vehicle. Furthermore, some other parameters like road bank angle has an important influence on vehicle lateral dynamics behavior and control vehicle systems. For this, the algebraic based unknown input estimator is considered here to estimate road bank angle and consider a more realistic trajectories. Interesting results are obtained using real data which are acquired under high lateral accelerations

Robust finite-time fault estimation for stochastic nonlinear systems with Brownian motions

Motivated by real-time monitoring and fault diagnosis for complex systems, the presented paper aims to develop effective fault estimation techniques for stochastic nonlinear systems subject to partially decoupled unknown input disturbances and Brownian motions. The challenge of the research is how to ensure the robustness of the proposed fault estimation techniques against stochastic Brownian perturbations and additive process disturbances, and provide a rigorous mathematical proof of the finite-time input-to-stabilization of the estimation error dynamics. In this paper, stochastic input-to-state stability and finite-time stochastic input-to-state stability of stochastic nonlinear systems are firstly investigated based on Lyapunov theory, leading to simple and straightforward criteria. By integrating augmented system approach, unknown input observer technique, and finite-time stochastic input-to-state stability theory, a highly-novel fault estimation technique is proposed. The convergence of the estimation error with respect to un-decoupled unknown inputs and Brownian perturbations is proven by using the derived stochastic input-to-state stability and finite-time stochastic input-to-state stability theorems. Based on linear matrix inequality technique, the robust observer gains can be obtained in order to achieve both stability and robustness of the error dynamic. Finally, the effectiveness of the proposed fault estimation techniques is demonstrated by the detailed simulation studies using a robotic system and a numerical example