Stratis Kanarachos

Control allocation for regenerative braking of electric vehicles with an electric motor at the front axle using the state-dependent Riccati equation control technique

In this paper the systematic development of an integrated braking controller for a vehicle driven by an electric motor on the front axle is presented. The objective is to engage the electric motor only during braking, up to the point at which the vehicle reaches its manoeuvrability and stability limit. The control challenges are to distribute the braking effort correctly between the hydraulic brakes at the four tyres and the electric motor, to handle the tyre saturation and motor constraints effectively and to adapt the control allocation based on the vehicle’s states. The controller is designed using the state-dependent Riccati equation control technique, the vehicle state estimation and the ‘magic formula’ tyre model. The state-dependent Riccati equation control technique is a suboptimal control design technique for non-linear systems. A novel method for constructing the state-dependent coefficient formulation of the system dynamics is proposed. Soft constraints in the state dynamics are described, while an augmented penalty approach is suggested for handling the system’s hard constraints. The performance of the controller was evaluated for different braking scenarios using simulations in a MATLAB/Simulink environment. An eight-degree-of-freedom non-linear vehicle model was utilized. The numerical results show that the controller suboptimizes the regenerative braking effort while considering the tyre force saturation, the motor torque limits, the vehicle yaw rate and the slip angle error. A comparison with a constrained linear quadratic regulator shows the advantages of the proposed controller.

A new min-max methodology for computing optimised obstacle avoidance steering manoeuvres of ground vehicles

In this paper, a new methodology for computing optimised obstacle avoidance steering manoeuvres for ground vehicles is presented and discussed. Most of the existing methods formulate the obstacle avoidance problem as an optimal control problem which is hard to solve or as a numerical optimisation problem with a large number of unknowns. This method is based on a reformulation of Pontryagins Maximum Principle and leads to the solution of an adjustable time optimal controller. The control input is significantly simplified and permits its application in a sample and hold sense. Furthermore, with the proposed approach the maximum tyre forces exerted during the manoeuvre are minimised. In this study, it is shown how to ‘warm start’ the proposed algorithm and which constraints to ‘relax’. Numerical examples and benchmark tests illustrate the performance of the proposed controller and compare it with other standard controllers. A sensitivity analysis for different vehicle parameters is performed and finally conclusions are drawn. A significant advantage of the method is the small computational complexity. The overall simplicity of the controller makes it attractive for application on autonomous vehicles.

Design of an intelligent feed forward controller system for vehicle obstacle avoidance using neural networks

The design of a novel feed forward controller system for vehicle obstacle avoidance using the neural network methodology is proposed. Currently, most obstacle avoidance systems are designed based on a segmented procedure: a) parametric path planning; b) desired yaw moment computation based on a simplified model; c) yaw moment tracking; d) stable controller design. In this paper, a different strategy is followed. An intelligent ‘autopilot’, that has been trained using a set of optimised obstacle avoidance manoeuvres, decides how to avoid the obstacle. The obstacle avoidance manoeuvres have been optimised using a reformulation of the Pontryagin’s Maximum Principle and global numerical optimisation techniques. The proposed controller has the advantage that it respects ‘by design’ the internal dynamics of the system and can be adjusted in order to account any model uncertainties. Furthermore, it is computationally very efficient. The performance of the intelligent system is evaluated by means of simulations i...

Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares

This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire–road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker®. The results confirm its good and robust performance.

Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

Design of a transferable time series anomaly detection method.Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.Detection of road anomalies using smartphone data, facilitating crowdsourcing applications. The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts knowledge. Here a new signal processing algorithm inspired by the deep learning paradigm is presented that combines wavelets, neural networks, and Hilbert transform. The algorithm performs robustly and is transferable. The proposed neural network structure facilitates learning short and long-term pattern interdependencies; a task usually hard to accomplish using standard neural network training algorithms. The paper provides guidelines for selecting the neural networks buffer size, training algorithm, and anomaly detection features. The algorithm learns the systems normal behavior and does not require the existence of anomalous data for assessing its statistical significance. This is an essential attribute in applications that require customization. Anomalies are detected by analysing hierarchically the instantaneous frequency and amplitude of the residual signal using probabilistic Receiver Operating Characteristics. The method is shown to be able to automatically detect anomalies in the Seismic Electrical Signal that could be used to predict earthquake activity. Furthermore, the method can be used in combination with crowdsourcing of smartphone data to locate road defects such as potholes and bumps for intervention and repair.

Experimental evaluation of optimal Vehicle Dynamic Control based on the State Dependent Riccati Equation technique

Development and experimentally evaluation of an optimal Vehicle Dynamic Control (VDC) strategy based on the State Dependent Riccati Equation (SDRE) control technique is presented. The proposed nonlinear controller is based on a nonlinear vehicle model with nonlinear tire characteristics. A novel extended linearization scheme of the systems state space equations on the basis of the combined slip Pacejka tire model is developed. On-line control regulation in terms of optimal braking torque allocation is computed at each time increment by solving efficiently an Algebraic Riccati Equation. The proposed method is implemented and tested on a Jaguar XF test vehicle. The results show the effectiveness of the proposed controller in stabilizing the vehicle with less effect on the vehicle longitudinal motion.

Vehicle agile maneuvering: From rally drivers to a finite state machine approach

Rally drivers can perform extreme maneuvers and keep a vehicle on track by maximizing the vehicle agility. It is remarkable that this is achieved robustly, without a vehicle or tire model in mind. In this study, the Moment Method Diagram and Beta Method representations are used to show the maximum achievable yaw moment generated by the front and rear tires. A new maneuverability map is proposed to bypass the limitations imposed by the steady-state assumptions, based on the wheel slip - yaw moment representation. Furthermore, a simple driving automation strategy is developed to determine the sequence of inputs required for maximizing vehicle agility and negotiating extreme maneuvers. A finite state machine is designed and implemented using a two track vehicle model. The numerical results show that the finite state machine can resemble a rally driver.

KDamping: A stiffness based vibration absorption concept

The KDamper is a novel passive vibration isolation and damping concept, based essentially on the optimal combination of appropriate stiffness elements, which include a negative stiffness element. The KDamper concept does not require any reduction in the overall structural stiffness, thus overcoming the corresponding inherent disadvantage of the “Quazi Zero Stiffness” (QZS) isolators, which require a drastic reduction of the structure load bearing capacity. Compared to the traditional Tuned Mass damper (TMD), the KDamper can achieve better isolation characteristics, without the need of additional heavy masses, as in the case of the T Tuned Mass damper. Contrary to the TMD and its variants, the KDamper substitutes the necessary high inertial forces of the added mass by the stiffness force of the negative stiffness element. Among others, this can provide comparative advantages in the very low frequency range. The paper proceeds to a systematic analytical approach for the optimal design and selection of the parameters of the KDamper, following exactly the classical approach used for the design of the Tuned Mass damper. It is thus theoretically proven that the KDamper can inherently offer far better isolation and damping properties than the Tuned Mass damper. Moreover, since the isolation and damping properties of the KDamper essentially result from the stiffness elements of the system, further technological advantages can emerge, in terms of weight, complexity and reliability. A simple vertical vibration isolation example is provided, implemented by a set of optimally combined conventional linear springs. The system is designed so that the system presents an adequate static load bearing capacity, whereas the Transfer Function of the system is below unity in the entire frequency range. Further insight is provided to the physical behavior of the system, indicating a proper phase difference between the positive and the negative stiffness elastic forces. This fact ensures that an adequate level of elastic forces exists throughout the entire frequency range, able to counteract the inertial and the external excitation forces, whereas the damping forces and the inertia forces of the additional mass remain minimal in the entire frequency range, including the natural frequencies. It should be mentioned that the approach presented does not simply refer to discrete vibration absorption device, but it consists a general vibration absorption concept, applicable also for the design of advanced materials or complex structures. Such a concept thus presents the potential for numerous implementations in a large variety of technological applications, whereas further potential may emerge in a multi-physics environment.

Robust Virtual Sensing for Vehicle Agile Manoeuvring: A Tyre-Model-Less Approach

This paper presents a robust virtual sensor to estimate the chassis planar motion states and the tire forces during agile maneuvers using a tire-model-less approach. Specifically, virtual sensing is achieved from standard sensor signals available on the CAN bus of modern vehicles using a modular filter architecture composed of stochastic Kalman filters. A high-fidelity virtual testing environment is constructed in IPG CarMaker using a driver-in-the-loop setup to verify the virtual sensor without compromising drivers safety. Moreover, road random profiles are incorporated into the virtual road to assess the state estimator robustness to high vertical excitation levels. The virtual sensor is simulated under drifting maneuvers performed by an experienced test driver and tested experimentally under Fishhook and Slalom maneuvers. Finally, the state estimator is integrated into a drift controller, and autonomous drift control using exclusively readily available measurements is verified for the first time. As the drift equilibrium depends strongly on the tire–road friction, an adaptive neurofuzzy inference system has been integrated into the virtual sensor structure to provide a continuous approximation of the road friction characteristics (axle lateral force versus slip curve) in rigid and loose surfaces. The findings suggest that it may be possible to develop advanced vehicle controllers without using a tire model. This can lead to a substantial acceleration of development time, particularly in off-road applications, and remove the need for online estimation of tire properties due to pressure, wear, and age.

A Virtual Sensor for Integral Tire Force Estimation using Tire Model-less Approaches and Adaptive Unscented Kalman Filter

In this paper, a novel approach to estimate the longitudinal, lateral and vertical tire forces is presented. The innovation lies a) in the proposition of a modular state estimation architecture that lessens the tuning effort and ensures the filter’s stability and b) in the estimation of the longitudinal velocity relying only on the wheel speed information.The longitudinal forces are estimated using an Adaptive Random-Walk Linear Kalman Filter. The lateral forces per axle are estimated by combining an Adaptive Unscented Kalman filter and Neural Networks. The individual tire lateral forces are inferred from the axle lateral forces using the vertical load proportionality principle. The individual tire vertical forces are estimated using a steady-state weight transfer approach, in which the roll stiffness distribution is considered. The state estimator is implemented in SimulinkR