Virtual Model Of A Vehicle Adaptive Damper System

Several FCA vehicles are fitted with semi-active damper systems which modulate the level of damping implemented in the vehicle suspension system to improve both the handling and ride quality felt by vehicle’s occupants. Durability simulations are necessary to analyze a vehicle’s or a component’s structural integrity over an expected lifespan. Performing durability simulations in a virtual environment has streamlined the traditional development cycle by reducing the need to construct physical prototypes and conduct physical road or bench tests. It is essential that the vehicle is modeled as accurately as possible in the virtual environment to ensure the results are representative of real-world performance. Presently, the incorporation of a semi-active damper system in a virtual durability simulation involves the expensive and resource intensive use of empirically obtained data. The goal of this project is to improve the fidelity and efficiency of durability simulations by including the loading effects of a semi-active suspension system. To accomplish this, several semi active suspension control algorithms and practical considerations are studied. Using a car model developed in Simulink©, a neural network, clipped optimal control, and sliding mode control algorithms are developed to approximate operating characteristics of the supplier controller. The development of each controller, along with appropriate tuning and validation procedures in Simulink©, are presented. A process known as co-simulation is then used to integrate each of the chosen semi-active damper control systems into durability simulations used in vehicle development processes at FCA. Co-simulation is a process wherein the controller is executed in parallel with MSC Adams© CAE durability simulation software using Matlab©/Simulink©. The accuracy of the neural network, sliding mode controller, and clipped optimal controller are validated by correlating results to a Co-simulation carried out with a supplier controller. It is found that the performance of the neural network controller resulted in output chattering throughout the simulation. While performance is acceptable in ranges where the output data is expected to be low frequency and low amplitude, instances where this was not the case induced chattering events. These events are most likely due to the neural network receiving inputs outside of the range of data which it was trained on

An investigation of multibody system modelling and control analysis techniques for the development of advanced suspension systems in passenger cars

The subject of this thesis is the investigation of multibody system modelling and control analysis techniques for the development of advanced suspension systems in passenger cars. A review of the application of automatic control to all areas of automotive vehicles illustrated the important factors in such developments, including motivating influences, constraints and methodologies used. A further review of specific applications for advanced suspension systems highlighted a major discrepancy between the significant claims of theoretical performance benefits and the scarcity of successful practical implementations. This discrepancy was the result of idealistic analytical studies producing unrealistic solutions with little regard for practical constraints. The predominant application of prototype testing methods in implementation studies also resulted in reduced potential performance improvements. This work addressed this gap by the application of realistic modelling and control design techniques to practical realistic suspension systems. Multibody system modelling techniques were used to develop vehicle models incorporating realistic representations of the suspension system itself, with the ability to include models of the controllers, and facilitate control analysis tasks. These models were first used to address ride control for fully active suspension systems. Both state space techniques, including linear quadratic regulator and pole placement and frequency domain design methods were applied. For the multivariable frequency domain study, dyadic expansion techniques were used to decouple the system into single input single output systems representing each of the sprung mass modes. Both discretely and continuously variable damping systems were then addressed with a range of control strategies, including analytical solutions based on the active results and heuristic rule-based approaches. The controllers based on active solutions were reduced to satisfy realistic practical limitations of the achievable damping force. The heuristic techniques included standard rule-based controllers using Boolean logic for the discretely variable case, and fuzzy logic controllers for the continuously variable case

Modeling and Robust Control of Integrated Ride and Handling of Passenger Cars

Vehicle industries in the last decade have focused on improving ride quality and safety of passenger cars. To achieve this goal, modeling and simulation of dynamic behaviour of vehicles have been widely studied to design model based and robust control strategies. This PhD work presents a new integrated vehicle model and a nonlinear robust controller. The thesis is divided into two main sections: dynamic modeling and controller design. A new fourteen Degrees of Freedom integrated ride and handling vehicle model is proposed using Lagrangian method in terms of quasi-coordinates. The governing equations are derived considering the interaction between the ride and handling systems, Euler motion of the frames attached to the wheels and body, the load transfer among the wheels, acceleration and braking. A non-dimensional factor called coupling factor is introduced to study the coupling among different DOFs of the dynamic system for a defined vehicle maneuver. The coupling factor is considered as an indicator parameter to demonstrate the advantages of the developed model over the existing dynamic models. The improved model is validated using ADAMS/Car for different manoeuvres. The simulation results confirm the accuracy of the improved dynamic model in comparison with the ADAMS/Car simulations and the models available in the literature. Considering the proposed nonlinear integrated ride and handling vehicle model, a nonlinear robust controller is designed for an intermediate passenger car. The H∞ robust control strategy is designed based on the Hamiltonian-Jacobi-Isaacs (HJI) function, Linear Matrix Inequality and State Feedback techniques. In order to improve the ride and handling quality of the vehicle, a Magneto-rheological (MR) damper and a differential braking system are used as control devices. A frequency dependent MR damper model is proposed based on the Spencer MR damper model. The parameters of the model are identified using a combination of Genetic algorithms and Sequential Quadratic Programming approaches based on the experimental data. A mathematical model is validated using the experimental results which confirm the improvement in the accuracy of the model and consistency in the variation of damping with frequency. Based on the proposed MR damper model, an inverse model for the MR damper is designed. A differential braking system is designed to assign desired braking action. The dynamic behavior of the controlled vehicle is simulated for single lane change and bump input, considering three different road conditions: dry, rainy and snowy. The robustness of the designed controller is investigated when the vehicle is under these road conditions. The simulation results confirm the interactive nature of the ride and handling systems and the robustness of the designed control strategy

Integrated control of vehicle chassis systems

This thesis develops a method to integrate several automotive intelligent chassis systems, such as Anti-lock Brake System, Traction Control System, Direct Yaw Control and Active Rear Wheel Steering, using evolutionary approaches. The Integrated Vehicle Control System (IVCS) combines and supervises all controllable systems in the vehicle, optimising the over all performance and minimising the energy consumption. The IVCS is able to improve the driving safety avoiding and preventing critical or unstable situations. Furthermore, if a critical or unstable configuration is reached, the integrated system should be able to recover a stable condition. The control structure proposed in this work has as main characteristics the modularity, extensibility and flexibility, fitting the requirements of a 'plug-and-play' philosophy. The investigation is divided into four steps: Vehicle Modelling, Soft-Computing, Behaviour Based Control, and Integrated Vehicle Control System. Several mathematical vehicle models, which are applied to designing and developing the control systems, are presented. MATLAB, SIMULINK and ADAMS are used as tools to implement and simulate those models. A methodology for learning and optimisation is presented. This methodology is based on Evolutionary Algorithms, integrating the Genetic Leaming Automata, CARLA and Fuzzy Logic System. The Behaviour Based Control is introduced as the main approach to designing the controllers and coordinators. The methodology previously described is used to learn the behaviours and optimise their performance, and the same technique is applied to coordinators. Several comparisons with other controllers are also carried out. From this an Integrated Vehicle Control System is designed, developed and implemented under a virtual environment. A range of manoeuvres is carried out in order to investigate its performance under diverse conditions. The leaming and optimisation method proposed in this thesis shows effective performance being able to learn all the controller and coordinator structures. The proposed approach for IVCS also demonstrates good performance, and is well suited to a 'plug-and-play' philosophy. This research provides a foundation for the implementation of the designed controllers and coordinators in a prototype vehicle.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Feedforward model with cascading proportional derivative active force control for an articulated arm mobile manipulator

This thesis presents an approach for controlling a mobile manipulator (MM) using a two degree of freedom (DOF) controller which essentially comprises a cascading proportional-derivative (CPD) control and feedforward active force control (FAFC). MM possesses both features of mobile platform and industrial arm manipulator. This has greatly improved the performance of MM with increased workspace capacity and better operation dexterity. The added mobility advantage to a MM, however, has increased the complexity of the MM dynamic system. A robust controller that can deal with the added complexity of the MM dynamic system was therefore needed. The AFC which can be considered as one of the novelties in the research creates a torque feedback within the dynamic system to allow for the compensation of sudden disturbances in the dynamic system. AFC also allows faster computational performance by using a fixed value of the estimated inertia matrix (IN) of the system. A feedforward of the dynamic system was also implemented to complement the IN for a better trajectory tracking performance. A localisation technique using Kalman filter (KF) was also incorporated into the CPD-FAFC scheme to solve some MM navigation problems. A simulation and experimental studies were performed to validate the effectiveness of the MM controller. Simulation was performed using a co-simulation technique which combined the simultaneous execution of the MSC Adams and MATLAB/Simulink software. The experimental study was carried out using a custom built MM experimental rig (MMer) which was developed based on the mechatronic approach. A comparative studies between the proposed CPD-FAFC with other type of controllers was also performed to further strengthen the outcome of the system. The experimental results affirmed the effectiveness of the proposed AFC-based controller and were in good agreement with the simulation counterpart, thereby verifying and validating the proposed research concepts and models

Intelligent controllers for vechicle suspension system using magnetorheological damper

Semi-active suspension control with magnetorheological (MR) damper is one of the most fascinating systems being studied in improving the vehicle ride comfort. This study aims to investigate the development of intelligent controllers for vehicle suspension system using MR damper, namely, the proportional-integral-derivative (PID) and fuzzy logic (FL) controllers optimized using particle swarm optimization (PSO), firefly algorithm (FA) and advanced firefly algorithm (AFA). Since the conventional optimization method always has a problem in identifying the optimum values and it is time consuming, the evolutionary algorithm is the best approach in replacing the conventional method as it is very efficient and consistent in exploring the values for every single space. The PSO and FA are among of the evolutionary algorithms which have been studied in this research. Nevertheless, the weakness of FA such as getting trapped into several local minima is an attractive area that has been focused more as a possible improvement during the evolutionary process. Thus, a new algorithm based on the improvement of the original FA was introduced to improve the solution quality of the FA. This algorithm is called advanced firefly algorithm. A parametric modelling technique known as Spencer model was proposed and employed to compute the dynamic behaviour of the MR damper system. The Spencer model was experimentally validated and conducted to capture the behaviour of the Lord RD-1005-3 MR damper with the same excitation input. A simulation of a semi-active suspension system was developed within MATLAB Simulink environment. The effectiveness of all control schemes were investigated in two major issues, namely the ability of the controller to reject the unwanted motion of the vehicle and to overcome the damping constraints. The result indicates that, the PID-AFA control scheme is more superior as compared to the PID-PSO, PID-FA, FL-PSO, FL-FA, FL-AFA and passive system with up to 27.1% and 19.1% reduction for sprung mass acceleration and sprung mass displacement, respectively. Finally, the performance of the proposed intelligent control schemes which are implemented experimentally on the developed quarter vehicle suspension test rig shows a good agreement with the results of the simulation study. The proposed control scheme of PID-AFA has reduced the sprung mass acceleration and sprung mass displacement over the FL-AFA and passive system up to 28.21% and 16.9%, respectively

Integration of Active Chassis Control Systems for Improved Vehicle Handling Performance

This thesis investigates the principle of integration of vehicle dynamics control systems by proposing a novel control architecture to integrate the brake-based electronic stability control (ESC), active front steering (AFS), normal suspension force control (NFC) and variable torque distribution (VTD). A nonlinear 14 degree of freedom passive vehicle dynamics model was developed in Matlab/Simulink and validated against commercially available vehicle dynamics software CarSim. Dynamics of the four active vehicle control systems were developed. Fuzzy logic and PID control strategies were employed considering their robustness and effectiveness in controlling nonlinear systems. Effectiveness of active systems in extending the vehicle operating range against the passive ones was investigated. From the research, it was observed that AFS is effective in improving the stability at lower lateral acceleration (latac) region with less interference to the longitudinal vehicle dynamics. But its ability diminishes at higher latac regions due to tyre lateral force saturation. Both ESC and VTD are found to be effective in stabilising the vehicle over the entire operating region. But the intrusive nature of ESC promotes VTD as a preferred stability control mechanism at the medium latac range. But ESC stands out in improving stability at limits where safety is of paramount importance. NFC is observed to improve the ability to generate the tyre forces across the entire operating range. Based on this analysis, a novel rule based integrated chassis control (ICC) strategy is proposed. It uses a latac based stability criterion to assign the authority to control the stability and ensures the smooth transition of the control authority amongst the three systems, AFS, VTD and ESC respectively. The ICC also optimises the utilisation of NFC to improve the vehicle handling performance further, across the entire operating regions. The results of the simulation are found to prove that the integrated control strategy improves vehicle stability across the entire vehicle operating region