Stijn De Bruyne

Virtual engineering at work: the challenges for designing mechatronic products

The product race has become an innovation race, reconciling challenges of branding, performance, time to market and competitive pricing while complying with ecological, safety and legislation constraints. The answer lies in “smart” products of high complexity, relying on heterogeneous technologies and involving active components. To keep pace with this evolution and further accelerate the design cycle, the design engineering process must be rethought. The paper presents a mechatronic simulation approach to achieve this goal. The starting point is the current virtual prototyping paradigm that is widely adopted and that continues to improve in terms of model complexity, accuracy, robustness and automated optimization. Two evolutions are discussed. A first one is the extension to multi-physics simulation answering the design needs of the inherent multi-disciplinarity of “intelligent” products. Integration of thermal, hydraulic, mechanical, haptic and electrical functions requires simulation to extend beyond the traditional CAD-FEM approach, supporting the use of system, functional and perception models. The second evolution is the integration of control functions in the products. Where current industrial practice treats mechanical system design and control design as different design loops, this paper discusses their integration in a model-based design process at all design stages, turning concepts such as software-in-the-loop and hardware-in-the-loop into basic elements of an industrial design approach. These concepts are illustrated by a number of automotive design engineering cases, which demonstrate that the combined use of perception, geometric and system models allows to develop innovative solutions for the active safety, low-emission and high-comfort performance of next-generation vehicles. This process in turn poses new challenges to the design in terms of the specification and validation of such innovative products, including their failure modes and fault-tolerant behaviour. This will imply adopting a model-based system engineering approach that is currently already common practice in software engineering.

Autonomous Vehicle Control: A Nonconvex Approach for Obstacle Avoidance

This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions. The goal of the controller is to track the centerline of the roadway while avoiding obstacles. An outer-loop nonlinear model predictive control is adopted for generating the collision-free trajectory with the resultant input based on a simplified vehicle model. The optimization is solved through the generalized minimal residual method augmented with a continuation method. A sufficient condition to overcome limitations associated with continuation methods is introduced. The inner loop is a simple linear feedback controller based on an optimal preview distance. Simulation results illustrate the effectiveness of the approach. These are bolstered by scaled-vehicle experimental results.

Online Estimation of Vehicle Inertial Parameters for Improving Chassis Control Systems

Abstract Vehicle chassis control systems aim at increasing vehicle safety and performance, while ensuring superior passenger comfort. Nearly all control algorithms are sensitive to the inertial parameters of a vehicle. As the vehicle mass, the moments of inertia, and the centre of gravity (COG) position can change significantly during operation, an accurate online estimation of these properties could substantially improve the performance of an active system. This paper presents an innovative algorithm for the online estimation of the inertial parameters of a road-vehicle. Using low-frequent suspension displacement signals and suspension stiffness characteristics, the vehicle mass and horizontal COG position are estimated. A Monte Carlo method determines the most probable mass distribution. Based on the assigned passenger weight, anthropometric data sets allow to calculate the inertial properties of every passenger, eventually resulting in the inertial parameters of the complete, loaded vehicle. The accuracy of the proposed algorithm is validated by means of a test campaign on an accurate kinematics and compliance testrig.

Preview control of a constrained hydraulic active suspension system

This paper analyzes the impact of realistic actuator constraints on the potential performance of a hydraulic active suspension system. The optimal control force, requested by an independent vehicle-level control law, can often not be generated by the hydraulic actuator due to physical constraints, resulting in a significant reduction of the suspension performance. Specifically preview-based vehicle-level control laws are considered, since they could, theoretically, result in a strong performance improvement. An innovative vehicle-level control law, based on hybrid Model Predictive Control (MPC), is proposed to improve the performance of such a constrained hydraulic active suspension. Based on a simulation analysis, the performance of both control strategies is evaluated.

Model Based Actuator Management for a Hydraulic Active Suspension System: Improving Comfort Performance by Advanced Control

Active suspension systems aim at increasing safety by improving vehicle ride and handling performance while ensuring superior passenger comfort. This paper addresses the influence of the actuator management on the comfort performance of a complete hydraulic active suspension system. An innovative approach, based on nonlinear Model Predictive Control, is proposed and compared to a classical approach that employs a steady-state performance map of the actuator. A simulation analysis shows how taking into account actuator dynamics improves the actuator’s force tracking performance, leading to an improvement of the overall vehicle comfort performance.Copyright

Improving active suspension performance by means of advanced vehicle state and parameter estimation

Active suspension systems aim to increase safety by improving vehicle ride and handling performance while ensuring superior passenger comfort. To achieve good control of this system, the control algorithm must be provided with reliable and accurate input signals. This paper presents the design and development of a state estimator that accurately provides the information required by a sky-hook controller, using a minimum of sensors. The vehicle inertial parameters are estimated by an algorithm based on Monte Carlo simulations and anthropometric data. All state updating is performed using Kalman filters. The resulting performance enhancement has been proven during test drives.

A decentralized algorithm for control of autonomous agents coupled by feasibility constraints

In this paper a decentralized control algorithm for systems composed of N dynamically decoupled agents, coupled by feasibility constraints, is presented. The control problem is divided into N optimal control sub-problems and a communication scheme is proposed to decouple computations. The derivative of the solution of each sub-problem is used to approximate the evolution of the system allowing the algorithm to decentralize and parallelize computations. The effectiveness of the proposed algorithm is shown through simulations in a cooperative driving scenario.

A cooperative driving NLMPC for real time collision avoidance

A decentralized cooperative driving Non Linear Model Predictive Control (NLMPC) approach for path following and collision avoidance is presented in this paper.The proposed decentralized approach is based on an information network, which communicates when two or more vehicles are near and so they might collide. In the case in which vehicles are far, online trajectory control is independently computed on-board by means of a NLMPC. When two or more vehicles get closer, trajectory control is no more independently carried out: optimal solution for these vehicles is coupled and thus their trajectories are computed dependently.Performance of the proposed decentralized NLMPC for cooperative driving was assessed through numerical simulations involving two vehicles. Results were compared with ones of a centralized approach to assess optimality of the solution.Copyright

Model Based Control of a Multi-Axis Hydraulic Shaker Using Experimental Modal Analysis

Abstract This paper describes the development of a decoupling vibration control system for a multi-axis hydraulic shaker facility. The control system is based on the Internal Model Control (IMC) architecture. While an accurate model of the hydraulic subsystem should only be identified once, there is a strong need for a systematic procedure for identifying the mechanical subsystem. Based on the PolyMAX identification method, an accurate mechanical model can be obtained through experimental modal analysis. In a simulation analysis, the resulting PolyMAX-IMC control system has proven to achieve an improved performance regarding reference tracking, limited time harmonic distortion and cross-talk reduction.