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This paper presents the algorithm for a Kalman filter active vehicle suspension design. Based on simulations, two main issues have been investigated, (a) the effects of disturbances from the changes in road input and the variations of vehicle parameters on state observer estimation, (b) the benefits of adaptation of an active suspension to the changes of road input and the variations of vehicle parameters. Simulations showed the significant vehicle performance improvement from adaptation to road input; however, an adaptive Kalman filter is not very necessary.

A semi-track air-cushion vehicle (STACV) which combines an air-cushion lifting system with a semi-track propulsion system is an efficient solution for heavy-duty vehicles working on soft terrain, such as the vehicles for agricultural, oil industrial and military purposes. Focusing on optimizing fuel economy of the vehicle, five main issues are studied in this paper. Firstly, based on the analyses of resistances for the STACV in a sandy loam working condition, a theoretical model for fuel consumption of 100km, which is an evaluation index for fuel economy, is established. Secondly, through simplified by a group of constraint equations based on the physical structure of the vehicle, the running parameters and control target (fuel consumption of 100km) could be expressed by the following two measurable and adjustable parameters, fan rotational speed and vehicle forward speed.

For the purposes of on-line control, e.g., in an automatic driving system, or of closed-loop directional control simulation, an optimal preview artificial neural network (ANN) driver model based on error elimination algorithm(EEA) is built. Then the optimal preview times are discussed in high frequency range in this system. The simulation results of optimal preview ANN driver model and Error Elimination Algorithm driver model are compared under the condition of different vehicle speeds and paths, which shows that the proposed approach is efficient and reliable enough, particularly for driver-vehicle closed-loop system.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks.

An EV prototype, with all the wheels respectively driven by 4 inwheel motors, is developed, and undergoes a series of practical measurements and road tests. Based on the obtained vehicle parameters, a multi-body dynamics model is built by using SolidWorks and Adams/Car, and then validated by track test data. The virtual prototype is served as the control plant in simulation. An adaptive fractional order PID (A-FO-PID) controller is designed to enhance the handling and stability performance of the EV. Considering the model uncertainties, e.g. the variation in body mass distribution and the consequent change in yaw moment of inertial, a Parameter Self-Adjusting Differential Evolution (PSA-DE) algorithm is adopted for tuning the controller parameters, i.e. KP, KI, KD, λ and μ. As a modification of traditional DE algorithm, the so-called Variance of Population’s Fitness is utilized to evaluate the diversity of the population.

A new kind of integrated semi-track air-cushion pitch controller is proposed in this paper. The controller first compute the target working point based on a weighed function, which is the combination of optimal power consumption and pitch angle control demand. Then the sequential quadratic programming algorithm distributes the general target values to specific control values. The performance of the controller is verified through co-simulation between Matlab/Simulink and ADAMS/View. The simulation results show the effectiveness of the control algorithm and the correctness of the choice in physical configuration with two air cushions for vehicle body pitch control.