State and parameter estimator design for control of vehicle suspension system

Modern vehicle stability and navigational systems are mostly designed using inaccurate bicycle models to approximate the full-car models. This results in incomplete models with various unknown parameters and states being neglected in the controller and navigation system design processes. Earlier estimation algorithms using the bicycle models are simpler but have many undefined parameters and states that are crucial for proper stability control. For existing vehicle navigation systems, direct line of sight for satellite access is required but is limited in modern cities with many high-rise buildings and therefore, an inertial navigation system utilizing accurate estimation of these parameters is needed. The aim of this research is to estimate the parameters and states of the vehicle more accurately using a multivariable and complex full-car model. This will enhance the stability of the vehicle and can provide a more consistent navigation. The proposed method uses the kinematics estimation model formulated using special orthogonal SO3 group to design estimators for vehicles velocity, attitude and suspension states. These estimators are used to modify the existing antilock braking system (ABS) scheme by incorporating the dynamic velocity estimation to reduce the stopping distance. Meanwhile the semi-active suspension system includes suspension velocity and displacement states to reduce the suspension displacements and velocities. They are also used in the direct yaw control (DYC) scheme to include mass and attitude changes to reduce the lateral velocity and slips. Meanwhile in the navigation system, the 3-dimensional attitude effects can improve the position accuracy. With these approaches, the stopping distance in the ABS has been reduced by one meter and the vehicle states required for inertial navigation are more accurately estimated. The results for high speed lane change test indicate that the vehicle is 34% more stable and 16% better ride comfort on rough terrains due to the proposed DYC and the active suspension system control. The methods proposed can be utilized in future autonomous car design. This research is therefore an important contribution in shaping the future of vehicle driving, comfort and stability

Hungarian Mechanism based Sectored FFR for Irregular Geometry Multicellular Networks

The growing demands for mobile broadband application services along with the scarcity of the spectrum have triggered the dense utilization of frequency resources in cellular networks. The capacity demands are coped accordingly, however at the detriment of added inter-cell interference (ICI). Fractional Frequency Reuse (FFR) is an effective ICI mitigation approach when adopted in realistic irregular geometry cellular networks. However, in the literature optimized spectrum resources for the individual users are not considered. In this paper Hungarian Mechanism based Sectored Fractional Frequency Reuse (HMS-FFR) scheme is proposed, where the sub-carriers present in the dynamically partitioned spectrum are optimally allocated to each user. Simulation results revealed that the proposed HMS-FFR scheme enhances the system performance in terms of achievable throughput, average sum rate, and achievable throughput with respect to load while considering full traffic

Antilock braking system using dynamic speed estimation

Antilock braking systems use slip to control braking, for which the velocity of the car and wheel speeds of the wheels are required. The wheel speeds can be measured directly but the velocity of the vehicle is difficult to measure. Although the wheel speed can be used to calculate the linear velocity of the vehicle using the tire characteristic function, it depends upon various environmental and time varying parameters. The dominant factor in the characteristic function is the road friction coefficient. Due to the difficulties in proper estimation of the road friction, most systems calculate the optimal values offline and apply them at different speeds using switching functions. By using the tire model and the optimal friction coefficients, the velocity of the vehicle is estimated and used for calculating the optimal braking force, resulting in inappropriate control of braking creating longer braking distances. In the method proposed in this paper, an estimator will be used to estimate the velocity, which is proved to be more accurate than calculated from the wheel speeds. The estimated velocity and the pitch angle will be used to schedule the braking forces in order to reduce the braking time. The braking time of the proposed system lies between the ideal braking time and the conventional reference wheel speed related braking time, indicating an improvement in reducing the braking distance

Review of modern vehicle modelling

Earlier models were a simplified form of the complex vehicle system, which was governed by the important parameters and states. These parameters and states were either obtained indirectly through some simplified equations or directly measured using instrumentation. In either case, the models were designed on the basis of parameters and state availability or importance, and not to have a complete model representation and maximum parameter utilization through estimation and instrumentation. Since a complete vehicle model can lead to a better controller. Without considering all the important parameters, a complete model is not obtained to design better controllers. All types of controllers, classical, optimal, nonlinear and linear controllers use the basic equations that govern the vehicle dynamics. This paper reviews all the previous models with greater insight into each system. The aim is to provide a better understanding of each model, its shortcomings and how it can represent the complex vehicle model

Direct yaw control of vehicle using state dependent riccati equation with integral terms

Direct yaw control of four-wheel vehicles using optimal controllers such as the linear quadratic regulator (LQR) and the sliding mode controller (SMC) either considers only certain parameters constant in the nonlinear equations of vehicle model or totally neglect their effects to obtain simplified models, resulting in loss of states for the system. In this paper, a modified state-dependent Ricatti equation method obtained by the simplification of the vehicle model is proposed. This method overcomes the problem of the lost states by including state integrals. The results of the proposed system are compared with the sliding mode slip controller and statedependent Ricatti equation method using high fidelity vehicle model in the vehicle simulation software package, Carsim. Results show 38% reduction in the lateral velocity, 34% reduction in roll and 16% reduction in excessive yaw by only increasing the fuel consumption by 6.07%

The effects of distance in dynamic psychological factors of one-dimensional queue

Psychological dynamics that are created by the interactions among persons within One-Dimensional Queue, specially the spread of actions from person to person is discussed in this paper. The instability of psychological dynamic factors can be overcome by persons’ interactions in a crowd dynamic. Surowiecki‘s theory states that the individuals in a crowd are not affected by each other in terms of attitudes. This state indicates wisdom of crowd while Gustav Le Bon’s theory is in contrast with. The purpose of this paper is to prove that Surowiecki‘s theory is originated from Gustav Le Bon’s theory if there is a large distance between the members of group in the one dimensional queue. This paper proposes a dynamic crowd model by Bergey, Spieser and Davison based on Gustav Le Bon’s theory for static crowd model with variable distance interaction; resulting into better control of crowd dynamics using distance interaction