Path tracking controller of an autonomous armoured vehicle using modified Stanley controller optimized with particle swarm optimization

This study presents the development and optimization of a proposed path tracking controller for an autonomous armoured vehicle. A path tracking control is developed based on an established Stanley controller for autonomous vehicles. The basic controller is modified and applied on a non-linear, 7degree-of-freedom armoured vehicle model, and consists of various modules such as handling model, tire model, engine, and transmission model. The controller is then optimized using particle swarm optimization algorithm to select the optimum set of controller parameters. The main motivation of this study is that implementation of path tracking control on an autonomous armoured vehicle is still very limited and it is important to have a specific study on this field due to the different dynamics and properties of the armoured vehicle compared to normal passenger vehicles. Several road courses are considered and the performance of the developed controller in guiding the vehicle along these courses was compared against the original Stanley Controller. It was found that the optimized controller managed to improve the overall lateral error throughout the courses with 24–96% reduction in lateral error. Also, the optimization for the proposed controller was found to converge faster than its counterpart with up to 93% better solution

THERMAL CONDUCTIVITY CHARACTERISTIC OF TITANIUM DIOXIDE WATER BASED NANOFLUIDS SUBJECTED TO VARIOUS TYPES OF SURFACTANT

In nanofluid preparation, surfactants such as gum Arabic, sodium dodecylbenzenesulfonate, and polyvinylpyrrolidone are often added to minimize the nanoparticles sedimentation problems and eventually improve nanofluids stability. However, the inclusion of surfactant will affect the thermal conductivity of nanofluids. Proper amount of surfactant is required not only to improve nanofluids stability but also to optimize its thermal conductivity enhancement. Thus, the present study investigated the effect of gum Arabic, sodium dodecylbenzenesulfonate and polyvinylpyrrolidone with two different surfactant to nanoparticles ratio (1:1 and 2:1) on thermal conductivity of titanium dioxide water based nanofluids. The nanofluid samples were prepared via two-step method while KD2-Pro thermal analyser was used to measure the thermal conductivity. Study concluded that the thermal conductivity of non-surfactant titanium dioxide based nanofluids is higher than surfactant based titanium dioxide nanofluids. This study concludes that in comparison with types of surfactant, nanofluids (1:1 ratio) at 0.8 volume percentage of titanium dioxide added with sodium dodecylbenzenesulfonate exhibit highest thermal conductivity value followed by gum Arabic and polyvinylpyrrolidone

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the non-adaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver’s experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller

Route Planning Analysis In Holes Drilling Process Using Magnetic Optimization Algorithm For Electronic Manufacturing Sector

Electronic manufacturing sector uses computer numerical controlled machines for drilling holes. Most of the computer numerical controlled machines used nearest neighbour algorithm to plan the route for the drill bit to travel. Based on this motivation, this paper proposes an approach which is based on the experimentation of Magnetic Optimization Algorithm. In this implementation, each magnetic agent or particle in Magnetic Optimization Algorithm represents a candidate solution of the problem. The magnitude of the magnetic force between these particles is inversely proportional to the distance calculated by the solution they represented. Particles with greater magnetic force will attract other particles with relatively smaller magnetic force, towards it. The process is repeated until the stopping condition meets and the solution with lowest distance is taken as the best-found solution. Result obtained from the case study shows that the proposed approach managed to find the optimal solution. With this method, electronics manufacturing sector can optimize the drilling process hence will increase the productivity of the manufacturer. This study can be extended further by tuning the parameters of MOA in order to enhance the drilling route process

Modelling and control strategies in path tracking control for autonomous ground vehicles: a review of state of the art and challenges

Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller’s performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle

Modeliranje vedenja magnetoreološkega elastomera ob trku z brezparametričnim polinomskim modelom, optimiziranim z gravitacijskim iskalnim algoritmom

This paper presents an approach to model the impact behaviour of a dual-acting magnetorheological elastomer (MRE) damper using 4th-order polynomial functions optimized with a gravitational search algorithm. MRE is a type of smart material that can change its mechanical properties in response to an injected current, making it well-suited for a wide range of applications such as vibration absorption, noise cancellation, and shock mitigation. The proposed model uses a combination of polynomial functions designed to predict the nonlinearity of MRE during compression and extension stages. The model is tuned and validated using experimental data from impact tests conducted on the MRE damper under various currents. The results indicate that the developed model can accurately track the impact behaviour of MRE with minimum error. Additionally, an interpolation model is proposed to estimate the appropriate forces for median currents. The interpolation model predicts the force between the upper and lower currents, demonstrating the model\u27s ability to predict MRE behaviour accurately. The main contribution of this study is proposing a non-parametric model of MRE that is able to identify the hysteretic behaviour of the MRE based on specific current applied. In addition, an interpolation model is introduced in this study to cover not only the input current starting from 0 A to 2 A but also the intermediate current such as 0.3 A, 0.7 A, 1.3 A and 1.7 A

Hysteresis Behavior Modeling of Magnetorheological Elastomers under Impact Loading Using a Multilayer Exponential-Based Preisach Model Enhanced with Particle Swarm Optimization

Magnetorheological elastomers (MREs) are a type of smart material that can change their mechanical properties in response to external magnetic fields. These unique properties make them ideal for various applications, including vibration control, noise reduction, and shock absorption. This paper presents an approach for modeling the impact behavior of MREs. The proposed model uses a combination of exponential functions arranged in a multi-layer Preisach model to capture the nonlinear behavior of MREs under impact loads. The model is trained using particle swarm optimization (PSO) and validated using experimental data from drop impact tests conducted on MRE samples under various magnetic field strengths. The results demonstrate that the proposed model can accurately predict the impact behavior of MREs, making it a useful tool for designing MRE-based devices that require precise control of their impact response. The model’s response closely matches the experimental data with a maximum prediction error of 10% or less. Furthermore, the interpolated model’s response is in agreement with the experimental data with a maximum percentage error of less than 8.5%

Adaptive Fuzzy-PI control for active front steering system of armoured vehicles: Outer loop control design for firing on the move system

An armoured vehicle tends to lose its dynamic mobility when firing on the move. This is due to the effect of the firing force that reacts at the centre of the weapon platform, which creates an unwanted yaw moment at the vehicle's centre of gravity. In order to enhance the mobility performance of the armoured vehicle, a control strategy, i.e. yaw rejection control, is designed and test on an armoured vehicle model. The purpose of the control strategy is to maintain the directional mobility of the armoured vehicle by providing a steering correction angle to the pitman arm steering system. The control strategy proposed in this study consists of two main structures: yaw rate feedback control using a Proportional-Integral-Derivative (PID) controller and Lateral Force Rejection Control (LFRC) using an adaptive Fuzzy-Proportional-Integral (adaptive Fuzzy-PI) controller. The simulation results in terms of yaw and lateral motions were observed, and the proposed control strategy was shown to successfully improve the directional mobility of the armoured vehicle after firing. The benefit of the proposed control strategy with adaptive fuzzy-PI control is evaluated by comparing its performance to fuzzy-PI and proportional-integral (PI) control strategies