Low-cost sensors play a vital role in diverse applications, yet their accuracy limitations hinder widespread adoption. To address this problem, this study proposes a calibration technique using Artificial Neural Networks (ANNs) optimised with metaheuristic algorithms. We apply five well-known and widely used metaheuristic algorithms: Particle Swarm Optimisation (PSO), Harris Hawk Optimisation (HHO), Driving Training Based Optimisation (DTBO), Squirrel Search Optimisation (SSO) and Whale Optimisation Algorithm (WOA). These algorithms are used to tune the hyperparameters of an ANN to improve sensor accuracy via calibration. In this study, data collection was conducted alongside corresponding ground truth, resulting in a comprehensive dataset suitable for various applications such as environmental monitoring and sensor calibration. A comparative analysis among the algorithms revealed that WOA consistently outperformed the other metaheuristic optimisation techniques with the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This study provides valuable insights into combining metaheuristic optimisation techniques with ANNs for sensor calibration, potentially enhancing the development of more resilient and precise sensor technologies in the future.

This paper presents a novel robust path-tracking controller for autonomous ground vehicles. Environmental and vehicle factors like variation in road conditions and varying speed can adversely affect autonomous ground vehicles' path-tracking capability. A polytopic linear parameter varying model for autonomous ground vehicle that accounts for system uncertainties with varying speeds and road conditions is formulated. Then, an$H$∞based robust path-tracking controller is developed using this model to minimise the vehicle's lateral velocity, heading error, and slip angle. Simulation results comparing the proposed controller with a conventional robust controller are presented. The findings show that the proposed controller performs well and is more effective than the conventional robust controller.

Vehicle Dynamics Models (VDMs) face a trade-off scenario between accuracy and speed. More complex models can generate more accurate predictions of vehicle state but are more computationally slow. Additionally, many VDMs rely on the explicit estimation of unknown parameters. To avoid these limitations, we propose a feed-forward Time Delay Neural Network (TDNN) which surpasses the physics-based VDMs in accuracy and speed without explicit estimation of unknown quantities. Notably, the proposed TDNN model was able to accurately predict the vehicle state on various road surfaces despite no knowledge of the tyre-road friction. The TDNN predicts the longitudinal and yaw accelerations of the vehicle with a Root Mean Square Error (RMSE) of as low as 0.0387 rad/s2.

This study reports the robust sideslip angle estimation of vehicles with an uncertain tire cornering stiffness and fading measurements. The missing measurement and possible inaccuracy in the measurement of the vehicle's yaw rate are considered by using a random variable distributed over [0, 1]. Norm-bounded uncertainties are considered in the vehicle's tire cornering stiffness. Next, the Lyapunov stability theory is used to design a sideslip angle estimator such that the filtering error dynamics is stochastically stable and the$H_{\infty}$performance criterion is met. The desired parameters of the proposed$H_{\infty}$sideslip angle estimator are gained by solving a linear matrix inequality (LMI) problem. Simulation results show that the proposed novel estimator can efficiently estimate the sideslip angle while it demonstrates robust performance to uncertainties and fading measurements.

The increased use of commercial Unmanned Aerial Vehicles (UAVs) has generated a great interest in their potential to be used for transporting loads and other equipment. However, as the attraction of a UAV is its versatility and cost effectiveness, constraints are placed on the size and capability of a single UAV. Therefore, using multiple UAVs in flight formation has become an elegant solution to these limitations. The formation control of a team of load bearing UAVs is far from trivial. The UAV itself is an underactuated nonlinear system posing significant control challenges. Recent research on this topic has shown promising results and interesting modelling methods such as the Udwadia-Kalaba method has been proposed, to model the loaded system. This research will explore this problem using this method while seeking to bring in robust control techniques for low-level UAV stabilization by designing a sliding mode control system. The proposed low level controller will be combined with the formation controller and the stability demonstrated through simulations.

Model Predictive Control (MPC) is a high-performing solution for Autonomous Vehicle’s (AV) control. This technique can tailor balance between various aspects of vehicle dynamics such as vehicle’s speed, acceleration and jerk. This study proposes a longitudinal controller for AV using a data-driven MPC based on human driving demonstration. A novel parameterised cost function-based MPC is designed in order to provide a general solution for different driving scenarios. This parametric cost function provides a customisable approach towards longitudinal motion generation by learning a proper set of parameter values from the user’s driving style. Instead of using any classification technique for identifying driving styles, we asked human drivers to drive with different styles and use that data directly to learn the values of the parameters. The Bayesian Optimisation (BO) approach is used to learn an optimised set of parameters minimising the gap between some carefully chosen feature values of the controller and human-generated motion. The observations of simulation show that the proposed controller is capable of generating customisable longitudinal vehicle speed, acceleration, jerk, as well as headway distance between vehicles based on a specific human driving style.

Path tracking controller of Autonomous Vehicles (AVs) plays an important role in improving the dynamic behaviour of the vehicle. Model Predictive Control (MPC) is one the most capable controllers that can handle multiple optimisation objectives, and accommodate the physical limits of the actuators and vehicle states to ensure safety and the other desired behaviour. As a high-potential solution, learning cost function from human demonstration can be integrated into an MPC. By learning the cost function from human demonstrations, extensive parameters tuning can be avoided, and more importantly, the controllers can be adjusted to provide desired control actions which are more natural to the human. In this study, an innovative Inverse Optimal Control (IOC) algorithm is proposed to learn a suitable cost function for the control task using collected data from human demonstration. The objective is to design a controller that generates motion which matches specific features of human-generated motion. These features include lateral acceleration, lateral velocity and deviation from the center of the lane. From the results, it is observed that the designed controller is capable of learning the desired features of human driving and implementing them while generating the appropriate control actions.