Arash Mohammadi

Adaptive Washout Algorithm Based Fuzzy Tuning for Improving Human Perception

The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the simulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adaptive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of high-pass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cueing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance, improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.

Adaptive Translational Cueing Motion Algorithm Using Fuzzy Based Tilt Coordination

Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simulator’s physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.

Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique

In This paper the previous Li-Ion battery charger techniques are reviewed and compared and the new fuzzy logic battery charging method which is proposed to optimize and improve the battery charger efficiently. According to results of comparison, using the fuzzy control charging system can shorten the charging time with higher efficiency and lower temperature rise. Additionally, we have used optimal Li-ion battery charging frequency by using AC impedance technique which means if the battery is charged by the optimal charging frequency fZmin, that obtain from Bode Plot of the Li-ion battery, the charging time and charging efficiency will improve. Thus using the switching frequency (fZmin) of the battery charger and the fuzzy logic control on the same system can optimize the performance on the charging process. According to the experimental results, the proposed charger can charge the Li-ion batteries with higher efficiency 97.16%, lower temperature rise1.513degree celosias, fast charging period around 50.43 minute and long life cycle. The results in this paper are presented by using MATLAB and dsPIC30F2020 is used as controller applying designed fuzzy logic inside.

A Particle Swarm Optimization-based washout filter for improving simulator motion fidelity

The washout filter for a driving simulator is able to regenerate high fidelity vehicle translational and rotational motions within the simulators physical limitations and return the simulator platform back to its initial position. The classical washout filter provides a popular solution that has been broadly utilized in different commercial simulators due to its simplicity, short processing time, and reasonable performance. One limitation of the classical washout filter is its sub-optimal parameter tuning process, which is based on the trial-and-error method. This leads to an inefficient workspace usage and, consequently, generation of false motion cues that lead to simulator sickness. Ignorance of a human sensation model in its design is another drawback of classical washout filters. The purpose of this study is to use Particle Swarm Optimization (PSO) to design and tune the washout filter parameters, in order to increase motion fidelity, decrease the human sensation error, and improve efficiency of the workspace usage. The proposed PSO-based washout filter is designed and implemented using the MATLAB/Simulink software package. The results indicate the effectiveness of the PSO-based washout filter in reducing the human sensation error, increasing the capability of reference shape tracking, and improving efficiency of the workspace usage.

MPC-based motion cueing algorithm with short prediction horizon using exponential weighting

A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a prediction horizon while respecting the constraints. Reducing the prediction horizon is desirable to minimize the computational burden; however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short prediction horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

Stabilization ball and beam by fuzzy logic control strategy

Fuzzy logic controller is a controller for designing the challenging nonlinear control systems by If-Then laws that is like human intelligence and it increase the accuracy of the control action .This paper present a success control function using a Fuzzy System approach which is to control the Ball-Beam balance system, throughout modeling, simulation, and implementation. First we applied fuzzy logic for system which means for the outer loop a fuzzy logic controller is designed and for the inner loop of a ball and beam system a PD controller is implemented. We design a traditional PID controller and pole placement controller for the beam position in order to compare the performance of these three types of controllers; thus FLC found to give better transient and steady state results and there is less overshoot in compare with classical PID and pole placement controller. Simulation results are presented to show the better performance of the ball and beam using these controllers.

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.

A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator–based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator’s physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.

CISR-ODE, A C++ Framework with ODE Solver for Code Based System Dynamics Simulation

Ordinary differential equations are used for modelling a wide range of dynamic systems. Even though there are many graphical software applications for this purpose, a fully customised solution for all problems is code-level programming of the model and solver. In this project, a free and open source C++ framework is designed to facilitate modelling in native code environment and fulfill the common simulation needs of control and many other engineering and science applications. The solvers of this project are obtained from ODEINT and specialised for Armadillo matrix library to provide an easy syntax and a fast execution. The solver code is minimised and its modification for users have become easier. There are several features added to the solvers such as controlling maximum step size, informing the solver about sudden input change and forcing custom times into the results and calling a custom method at these points. The comfort of the model designer, code readability, extendibility and model isolation have been considered in the structure of this framework. The application manages the output results, exporting and plotting them. Modifying the model has become more practical and a portion of corresponding codes are updated automatically. A set of libraries is provided for generation of output figures, matrix hashing, control system functions, profiling, etc. In this paper, an example of using this framework for a classical washout filter model is explained.