Kyle Nelson

Bio: Kyle Nelson is an academic researcher from Deakin University. The author has contributed to research in topics: Model predictive control & Motion simulator. The author has an hindex of 11, co-authored 16 publications receiving 308 citations.

Multiobjective and Interactive Genetic Algorithms for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm

Abstract: Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker’s satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Optimizing Model Predictive Control horizons using Genetic Algorithm for Motion Cueing Algorithm

Abstract: Driving simulators are effective tools for producing the feeling of driving a real car through generation of a similar environment and motion cues. The main problem of motion simulators is their limited workspace which does not allow them to produce the exact motions of a real vehicle, hence they need a Motion Cueing Algorithm (MCA). A high-fidelity motion simulator can be used for vehicle prototyping and testing as well as driver/pilot training to enhance transportation safety. Using motion simulators with the capability of replacing realistic motions for these purposes is less risky for drivers and more time and cost-effective. Due to workspace limitations, washout filters have been designed to bring motion simulators back to a neutral position; however, the problem of violation of platform constraints is still an issue. Recently Model Predictive Control (MPC) has become popular in driving simulators. The primary advantage of this control method is respecting constraints and consideration of future dynamics. The horizon windows of future control and prediction affect the computational burden and the output performance. As these horizons are chosen manually by the designer, they are sub-optimal and in some cases too wide or narrow. In this paper, a novel method based on Genetic Algorithm (GA) is employed to achieve the best control and prediction horizons considering minimization of several terms such as sensation error, displacement and the computational burden. This new method is proposed to eliminate the MPC-MCA drawbacks such as time-consuming empirical guessing by iterative trial-and-error for the initial control and prediction horizons as selecting the initial control and prediction horizons based on trial-and-error can lead to large sensation error, low motion fidelity, inefficient platform usage as well as the computational burden. Therefore, this method provides a new framework for tuning not only the MPC-MCA optimally but also all the MPC-based applications while minimizing the desired cost function and computational load. The simulation results show the effectiveness of the proposed method in terms of output performance improvement and the computational burden.

Openga, a C++ genetic algorithm library

Abstract: In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from such as forcing a user to define the solutions as vectors or limiting the output of evaluation functions to a predefined format. In addition, the user has the entire control over genetic operations such as solution creation, mutation and crossover. The multi-object mode performs a Non-dominated Sorting Genetic Algorithm known as NSGA-III to obtain the pareto-optimal front while preserving the solution diversity. This library can handle multi-threading computations for single and multi-objective problems to increase the speed of the calculations significantly. The interactive mode is suitable for applications where human subjectivity is involved for evaluation of the cost function. Several simulation and tests are performed to verify the effectiveness of this library for calculations of optimization problems.

Vehicle motion simulators, a key step towards road vehicle dynamics improvement

Abstract: Real road vehicle tests are time consuming, laborious, and costly, and involve several safety concerns Road vehicle motion simulators (RVMS) could assist with vehicle testing, and eliminate or reduce the difficulties traditionally associated with conducting vehicle tests However, such simulators must exhibit a high level of fidelity and accuracy in order to provide realistic and reliable outcomes In this paper, we review existing RVMS and discuss each of the major RVMS subsystems related to the research and development of vehicle dynamics The possibility of utilising motion simulators to conduct ride and handling test scenarios is also investigated

Human Perception-Based Washout Filtering Using Genetic Algorithm

Abstract: The Motion Cueing Algorithm MCA transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers' experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in understanding the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm GA for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limitations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Ieee transactions on automatic control

Abstract: Consider a given dynamical system, described by ẋ = f(x) (wheref is a nonlinear function) and [x0] a subset ofR. We present an algorithm, based on interval analysis, able to show that there exists a unique equilibrium statex∞ ∈ [x0] which is asymptotically stable. The effective method also provides a set[x] (subset of[x0]) which is included in the attraction domain of x∞.