Adaptive Road Profile Estimation in Semi-Active Car Suspensions

International audienceThe enhancement of the passengers comfort and their safety are part of the constant concerns for car manufacturers. As a solution, the semi-active damping control systems have emerged to adapt the suspension features, where the road profile is one of the most important factors that determine the automotive vehicle performance. Because direct measurements of the road condition represent expensive solutions and, are susceptible to be contaminated, this paper proposes a novel road profile estimator that offers the essential information (road roughness and its frequency) for the adjustment of the vehicle dynamics by using conventional sensors of cars. Based on the Q-parametrization approach, an adaptive observer estimates the dynamic road signal, posteriorly, a Fourier analysis is used to compute online the road roughness condition and perform an ISO 8608 classification. Experimental results on the rear-left corner of a 1:5 scale vehicle, equipped with Electro-Rheological (ER) dampers, have been used to validate the proposed road profile estimation method. Different ISO road classes evaluate online the performance of the road identification algorithm, whose results show that any road can be identified successfully at least 70% with a false alarm rate lower than 5%; the general accuracy of the road classifier is 95%. A second test with variable vehicle velocity shows the importance of the online frequency estimation to adapt the road estimation algorithm to any driving velocity, in this test the road is correctly estimated 868 of 1,042 m (error of 16.7%). Finally, the adaptability of the parametric road estimator to the semi-activeness property of the ER damper is tested at different damping coefficients

Global Chassis Control System Using Suspension, Steering, and Braking Subsystems

A novel Global Chassis Control (GCC) system based on a multilayer architecture with three levels: top: decision layer, middle: control layer, and bottom: system layer is presented. The main contribution of this work is the development of a data-based classification and coordination algorithm, into a single control problem. Based on a clustering technique, the decision layer classifies the current driving condition. Afterwards, heuristic rules are used to coordinate the performance of the considered vehicle subsystems (suspension, steering, and braking) using local controllers hosted in the control layer. The control allocation system uses fuzzy logic controllers. The performance of the proposed GCC system was evaluated under different standard tests. Simulation results illustrate the effectiveness of the proposed system compared to an uncontrolled vehicle and a vehicle with a noncoordinated control. The proposed system decreases by 14% the braking distance in the hard braking test with respect to the uncontrolled vehicle, the roll and yaw movements are reduced by 10% and 12%, respectively, in the Double Line Change test, and the oscillations caused by load transfer are reduced by 7% in a cornering situation

Fault Tolerant Control in a Semi-active Suspension

6 pagesInternational audienceA Fault Tolerant Control System (FTCS) in a Quarter of Vehicle (QoV ) model is proposed. The control law is time-varying using a Linear Parameter-Varying (LPV ) based controller, which includes two scheduling parameters. One parameter for monitoring the nonlinear behavior of the damper, and another for fault accommodation using a reference model obtained by a state observer of the normal operating regime. The QoV model represents a semi-active suspension, including an experimental magneto-rheological damper model. The FTCS is analyzed when the velocity sensor fails abruptly and the QoV model is susceptible to disturbances in the road pro le. Simulation results show the e ectiveness of the FTCS in terms of vehicle comfort, suspension detection and road holding in comparison with a conventional LPV based control system. In the FTCS, the comfort index based on the power spectral density is within the desirable bound (1.8) in all range of frequencies, once the sensor fault has occurred; while, the conventional control system deteriorates the comfort 54 %, specially at low frequencies (0-4 Hz). Additionally, the FTCS improves the road holding and suspension de ection indexes, 33% and 39% respectively, when the fault accommodation is considered

Hardware-in-the-loop Testing of On-Off Controllers in Semi-Active Suspension Systems

International audienceThis paper presents an experimental validation of a proposed Frequency Estimation-Based (FEB) controller for semi-active suspensions by using a Hardware-in-the-Loop (HiL) platform of a Quarter of Vehicle (QoV) model. The FEB approach is compared with three commercial On-Off controllers that have shown good results in comfort and road holding: Sky-Hook (SH), Groud-Hook (GH) and Mix-1-sensor (M1S). The comparison was done under the same experimental tests; the standards ISO-2631 and BS-6841 are used to evaluate the comfort and the Root Mean Square (RMS) index to quantify the road holding. The QoV model belongs to a front-left corner of a pick-up truck; the used experimental Magneto-Rheological (MR) damper is not symmetric and only hast 2 manipulation states. Experimental results show that the FEB controller has the best comfort performance at low frequencies (outperforms the benchmark controllers at 11.2%); while, for road holding, the improvement is slight; however, FEB controller works better for both goals simultaneously. By analyzing the suspension deflection, the FEB controller reduces up to 32.8% of motion respect to the GH controller. Additionally, the manipulation of the SH and GH controllers have several changes of actuation that do not allow the stabilization of the force in its desirable value; while FEB controller has a soft actuation defined on bandwidths

A Comparison between a Model-free and Model-based Controller of an Automotive Semi-active Suspension System : Independent Wheel-stations

International audienceA comparison between two control strategies of automotive semi-active suspension systems is presented by using a pick-up truck model in CarSimTM; the controllers are based on different frameworks. The Linear Parameter Varying (LPV ) controller, considered as model-based controller, includes the constraints of the semi-active damper by using two scheduling parameters; while, the Frequency Estimation-Based (FEB) controller only requires measurements of a wheel-station for defining the damping force according to the objective controls, i.e. is free of a vehicle and actuator model. Since each wheel-station has an independent controller, the global semi-active suspension control system does not include the coupling effect among the four vehicle corners; however this effect is embedded into the controller performance. Experimental data are used to model a MR damper, which is used in each quarter of vehicle. A bounce sine sweep test is used to compare the performance in comfort and road holding of both controllers. Simulation results in CarSimTM shows that the FEB controller has the best comfort and road holding performance; in comparison with the passive suspension system, the pitch angle is reduced 19%, the front and rear suspension deflection decrease 23% and 58% respectively and the tire compression is reduced 3% (front wheel) and 10% (rear wheel)

Analytical Design and Optimization of an Automotive Rubber Bushing

The ride comfort, driving safety, and handling of the vehicle should be designed and tuned to achieve the expectations defined in the company’s design. The ideal method of tuning the characteristics of the vehicle is to modify the bushings and mounts used in the chassis system. To deal with the noise, vibration and harshness on automobiles, elastomeric materials in mounts and bushings are determinant in the automotive components design, particularly those related to the suspension system. For most designs, stiffness is a key design parameter. Determination of stiffness is often necessary in order to ensure that excessive forces or deflections do not occur. Many companies use trial and error method to meet the requirements of stiffness curves. Optimization algorithms are an effective solution to this type of design problems. This paper presents a simulation-based methodology to design an automotive bushing with specific characteristic curves. Using an optimum design formulation, a mathematical model is proposed to design and then optimize structural parameters using a genetic algorithm. To validate the resulting data, a finite element analysis (FEA) is carried out with the optimized values. At the end, results between optimization, FEA, and characteristic curves are compared and discussed to establish the correlation among them

Road Adaptive Semi-active Suspension in a Pick-up Truck using an LPV Controller

International audienceA novel road adaptive LPV controller for the semi-active suspension system of a pick-up truck is proposed. The analysis is carried on the front-left Quarter of Vehicle (QoV) model generated via CarsimTM vehicle simulator. By using an on-line road roughness estimation, considered as scheduling parameter, the proposed LPV/H∞ controller is designed to improve comfort and road holding. The road profile detector is based on the frequency and amplitude estimation of the road irregularities by using a Fourier analysis. AnH∞ robust observer is designed to estimate the variables related to the QoV vertical dynamics, which are used to compute the road frequency and roughness. Different ISO road classes are used to evaluate on-line the proposed road identification algorithm. A Receiver Operating Characteristic (ROC) curve is used to monitor the performance of the roughness estimation; the results show that any road can be identified (at least 70% of success with a false alarm rate lower than 5%). The average error of road identification is 16.2%. Finally, simulation results show that the proposed controller with road adaptation is capable to manage the trade-off between comfort and road holding. The road adaptive controller increases the comfort (35.8% )when the vehicle is driven over a road of bad quality, by considering an uncontrolled damper (passive suspension) as benchmark. While, when the vehicle is driven over a smooth runway at high velocity, the road holding index is increased 50%

Control of Automotive Semi-Active MR Suspensions for In-Wheel Electric Vehicles

In this work, four different semi-active controllers for a quarter of vehicle and full vehicles are evaluated and compared when used in internal combustion engine (ICE) vehicles vs electric vehicles (EVs) with in-wheel motor configuration as a way to explore the use of semi-active suspension systems in this kind of EVs. First, the quarter of vehicle vertical dynamics is analyzed and then a full vehicle approach explores the effectiveness of the control strategies and the effects of the traction in the vertical Control performances. Aspects like the relation between traction and suspension performances, and the resonance frequencies are also discussed

Power Consumption Profile of a Service Robot: Characterization and Analysis

International audienceThis paper presents a comprehensive analysis of the power consumption characteristics of an autonomous service robot across different operational scenarios. The study investigates the discharging profile of a 14.8V 4-cell 10,000mAh LiPo battery, providing insightful observations on the relationship between the State of Charge (SOC) and internal resistance, alongside voltage dynamics during discharge. The results indicate an inverse relationship between the SOC and the system's internal resistance, and a consistent voltage decrement corresponding to SOC. Furthermore, the energetic impacts of various robot components during the Powering On Procedure and Teleoperation and Navigating Procedure are detailed, identifying critical components contributing to high energy consumption. The paper suggests an autonomy duration of approximately 3.5 hours for the electrical system and 2 hours of continuous movement. The study provides a basis for optimizing energy efficiency in autonomous service robots