Fitri Yakub

Comparative Study of Autonomous Path-Following Vehicle Control Via Model Predictive Control and Linear Quadratic Control

This paper describes a comparative study of steering and yaw moment control manoeuvres in the model predictive control and linear quadratic control approaches for path-following control of an autonomous vehicle. We present the effectiveness of the model predictive control and linear quadratic control approaches for stability control of the vehicle’s lateral position and yaw angle for different control manoeuvres: two-wheel steering, four-wheel steering and direct yaw moment control. We then propose model predictive control with a feedforward controller to minimize the tracking errors of the lateral position and the yaw angle in an active front steering manoeuvre, and these are compared with the results from linear quadratic control that has a feedforward controller. Model predictive control is designed on the basis of the simple yaw–lateral motions of a single-track vehicle with a linear tyre model (i.e. a bicycle model), which is an approximation of the more realistic model of a vehicle with double-track yaw–roll motion with a non-linear tyre model (i.e. the Pacejka model). The linear quadratic controller is designed on the basis of the same approach as adopted for the model predictive controller to achieve a fair comparison. On the basis of a given trajectory, we simulate the manoeuvre of the vehicle at a low speed, a middle speed and a high speed because load transfer effects will influence the roll dynamics especially at a high speed. We also perform simulations on low-road-friction surfaces in a double-lane-change scenario with the aim of following the desired trajectory as closely as possible while maintaining the vehicle stability. The simulation results show that model predictive control through two-wheel steering and four-wheel steering with direct yaw moment control performed better in terms of trajectory tracking at a high forward speed and low road surface variation. The proposed model predictive control with a feedforward controller is shown to be effective in minimizing the trajectory tracking errors. For all control manoeuvres, model predictive control gives a better tracking performance than linear quadratic control does. In addition, when the roll dynamics are considered, model predictive control significantly improves the vehicle stability and the trajectory along the desired path.

Recent trends for practical rehabilitation robotics, current challenges and the future

This paper presents and studies various selected literature primarily from conference proceedings, journals and clinical tests of the robotic, mechatronics, neurology and biomedical engineering of rehabilitation robotic systems. The present paper focuses of three main categories: types of rehabilitation robots, key technologies with current issues and future challenges. Literature on fundamental research with some examples from commercialized robots and new robot development projects related to rehabilitation are introduced. Most of the commercialized robots presented in this paper are well known especially to robotics engineers and scholars in the robotic field, but are less known to humanities scholars. The field of rehabilitation robot research is expanding; in light of this, some of the current issues and future challenges in rehabilitation robot engineering are recalled, examined and clarified with future directions. This paper is concluded with some recommendations with respect to rehabilitation robots.

Rollover prevention with Predictive Control of differential braking and rear wheel steering

This paper contributes to increase knowledge about a Model Predictive Control (MPC) approach to untripped rollover prevention of heavy vehicles which make a panic lane change maneuver in order to avoid an obstacle in the path. Active differential braking control shows good effect on limit roll angle during an urgent situation. However, at the same time of avoiding rollover accidents, prohibiting the vehicle from the drivers intended and the vehicles actual lane can also yield other accidents such as bumping into guard rails or cones. Thus, it is necessary to track the drivers desired path as closely as possible while preventing the vehicle from rollover. Here, a new control method of switching the MPC controllers which uses differential braking with dead zone and active rear steer are proposed. Simultaneously, the trade-off between rollover prevention and path tracking is highlighted through simulation results. The effectiveness of using switching controllers designed for the trade-off solution is also confirmed through simulation results.

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

Effects of roll dynamics for car stability control by laguerre functions

In this paper, we introduce and present the Laguerre functions in model predictive control approach for path following in car vehicle dynamics system. Start with the model predictive control based on linearizing vehicle and tire model where the most popular bicycle model is used. We compare the controller based on Laguerre functions for simple and complex bicycle model without and with including the roll vehicle dynamics. The roll dynamics will get influent in the high speed maneuver due to the load transfer effects. Based on simulation results, we perform the vehicle in high speed maneuvers for double lane change scenario. The result shows and presents that the predictive control based Laguerre functions give a better performance than conventional which is use Pulse operator function and fewer complexes in term of algorithm implementation. It also demonstrates that with the roll dynamics take into consideration, it significantly improves the vehicle stability and trajectory along the desired path.

Enhancing the yaw stability and the manoeuvrability of a heavy vehicle in difficult scenarios by an emergency threat avoidance manoeuvre

This study aims to investigate the switching model predictive control strategy for a heavy-vehicle system in order to coordinate the actuator between active rear steering and differential braking control manoeuvres for emergency threat avoidance in difficult environments. We present the controller performances for the lateral dynamic behaviour, the yaw stability and the manoeuvrability of a vehicle when subjected to a sudden threat or disturbance such as a gust of wind, a road bank angle or a split-μ road surface in order to enable a fast safe lane-change trajectory to be followed. The vehicle was driven at a medium forward speed and a high forward speed in order to investigate the effectiveness of the proposed approach in avoiding the threat, maintaining the stability and enablinge a fast safe lane-change trajectory to be followed. We compared two different controllers (a model predictive controller and a switching model predictive controller) for two different control manoeuvres (active rear steering with differential braking control and active rear steering with direct yaw moment control). The simulation results demonstrate that the proposed switching model predictive control method provides an improved fast safe lane-change manoeuvre in a threat avoidance scenario for both control manoeuvres. It also demonstrated that the proposed active rear steering with differential braking control is more useful for maintaining the stability of the vehicle in a threat avoidance scenario with disturbance effects than is active rear steering with direct yaw moment control.

Enhancing vehicle ride comfort through intelligent based control

The research presented in this paper is carried out to investigate the performance of a suspension systems either an active or passive type. Controllers that are used in this study are proposed fuzzy logic controller and proportional integral derivative controller as a benchmarking comparison. The simulations in this research have been carried out using Simulink of MATLAB. The parameters in the simulation model for the suspension system under study include car body mass, wheel mass, spring and damping elements of shock absorber, and tire. The block model of the suspension system has been designed to represent the equation of motion of the sedan car suspension system. The road disturbance for the active suspension system is modelled in two different ways, namely, unit step input signal and sine wave input signal. The simulation results indicate that fuzzy logic control of an active car suspension system has better performance compared to the passive system.

Heavy vehicle stability and rollover prevention through switching model predictive control

This study contribute to enhance the maneuverability safety for coordination of active rear steering and direct yaw moment control for un-tripped rollover prevention that make a panic lane change maneuver to avoid an obstacle in the path. At the same time of avoiding rollover accidents, prohibiting the vehicle from the drivers intended and the vehicles actual lane, and the effect of crosswind is also important, since it may yield other accidents. Thus, it is necessary to track the drivers desired path as closely as possible while preventing the vehicle from rollover, and maintaining the vehicle stability along the desired path. Here, we start from the results presented in [1], then we extend and propose switching model predictive control which uses direct yaw moment control and active rear steer. Simultaneously, the trade-off between rollover prevention and path tracking is highlighted, and an effectiveness of using switching controllers designed for the trade-off solution is also confirmed through simulation results.

Practical Control Method for Two-Mass Rotary Point-To-Point Positioning Systems

Motion control systems play an important role in industrial engineering applications such as advanced manufacturing systems, semiconductor manufacturing system, computer numerical control (CNC) machining and robot systems. In general, positioning system can be classified into two types, namely point-to-point (PTP) positioning systems and continuous path (CP) control system (Crowder R.M, 1998). PTP positioning systems, either of one-mass or multi-mass systems, is used to move an object from one point to another point either in angular or linear position. For example, in application with one-mass system, such as CNC machines, PTP positioning is used to accurately locate the spindle at one or more specific locations to perform operations, such as drilling, reaming, boring, tapping, and punching. In multi-mass systems application, such as in spot-welding robot, which has a long arm for linear system or long shaft in rotary system, PTP positioning is used to locate the manipulator from one location to another.

Exploting the Orthonormal Function Based on Model Predictive Control for Automotive Application

In this paper, we introduced and explored the Laguerre and Kautz functions as an orthonormal functions based of model predictive control approach for automotive applications with particularly focuses on vehicle path following control and motor position control. The first implementation starting with the known trajectory, model predictive control based on linearized vehicle and tire model was used to follow the desired trajectory as close as possible, we performed a vehicle at middle speed maneuver on double lane change scenario. In the second application; predictive control is designed in order to track the desired motor position. We compared the predictive controllers designed based on Delta, Laguerre and Kautz functions for both applications in term of tracking error and position error performances. The result showed that the predictive control based on Kautz function gave a better tracking performance compared to Laguerre function and Delta function (conventional method). However, Laguerre function is easier and simpler in term of algorithm implementation than the other two functions. Moreover, for all functions based on predictive control; the simulation results demonstrated that orthonormal function can be implemented in predictive control approaches particularly to automotive application, thus, it is believe can be extended to other applications.