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.

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.

Advances in utilisng motion simulators for skill training and related applications have yielded numerous benefits, such as safety, availability, and serviceability, environmentally friendly, and economically beneficial. To give simulator users a sense of realistic feeling of driving, an accurate motion cueing algorithm (MCA) is essential, in order to respect the simulator platform limitation and avoid motion sickness. The use of Model Predictive Control (MPC) in MCA designs leads to respecting the constraints and considering the future dynamic behaviors of the simulator. However, the tuning process of the MPC prediction horizon and control horizon still need to be improved. These horizons are normally selected manually by the designer. Previous studies on meta-heuristic algorithms produce a large prediction horizon with a heavy computational load or a small prediction horizon that sacrifices the stability and accuracy of the simulator system. In this study, the Grasshopper Optimization Algorithm (GOA) is adopted to yield optimal prediction and control horizons in MPC-based MCA models. The results are compared with those from the Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA) in terms of sensation error and computation time. The GOA technique depicts the fastest process time to promptly detect proper MPC horizons. It does not affect the simulator's efficiency in utilising the workspace, as evidenced by the correlation coefficient and root mean square error between sensation from a real-world vehicle and the simulator.

Driving simulators are utilized for many applications including basic driver training, human factor studies, human-machine interaction, and vehicle prototyping in automobile industries. The main purpose of using driving simulator is to provide realistic driving experience. Since simulator platforms have physical limitations, Motion Cueing Algorithms (MCAs) are used to generate driving sensation for the simulator user while considering the simulator's physical and dynamical constraints. When using a model predictive control (MPC)-based MCA, the principle of MPC is leveraged to predict an optimized future behavior of the simulator where a series of control actions is developed across a defined future horizon using the explicitly specified process model. Corresponding to the pre-positioning or time-varying reference MPC, it is crucial to predict the future vehicle motion signals for the simulator accurately. The existing methods for predicting vehicle motion signals do not excel in predicting time-series of a long sequence due to the missing feedback loop or limited memory size. To address this issue, the Long Short-Term Memory (LSTM) model is developed to predict motion signals using Python. The performance of LSTM is compared with those from different traditional methods using several measurements criteria, which include the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (r). The results indicate that LSTM outperforms RNN by producing more accurate motion allowing the MCA to deliver realistic motion sensations, the LSTM model can be employed in a wide range of applications including autonomous vehicles trajectory prediction, and other prediction problems.

The conflicts between the perceived sensation of the different sensory systems can cause adverse effects which is known as motion sickness (MS) and the side effects of MS include nausea, dizziness, stomach awareness etc. Virtual reality sickness (also called Cybersickness or visually induced motion sickness (VIMS)) happens during exposure to a virtual environment when senses transfer conflicting sensation signals to the brain. The symptoms of Cybersickness are similar to motion sickness symptoms. The adverse effects of this common phenomenon can negatively affect the training outcome and benefits using VR, undermine users’ health and usefulness of simulators as it involves health risk and contributes to the increase of dropout rates. Therefore, to mitigate these issues, MS should be detected and measured. The primary objective of this study is to subjectively and objectively detect and quantify cybersickness level using a helicopter simulator. This study has also investigated the change in cybersickness self-reported scores in different weather conditions such as clear and stormy. Simulator sickness questionnaire (SSQ) has been employed for subjective scoring. This research also aimed to correlate SSQ scores with physiological data such as Galvanic Skin Response (GSR). The findings demonstrated that the SSQ total score (TS) has increased significantly from clear weather to stormy for the participants. There is also a positive correlation found between the change in TS and the amount of GSR but not significant.

Continuum robots are utilized in applications where a high-level accuracy and delicacy is desired, which in return demand a robust control design. However, due to their nonlinearity and elasticity, this task of robust control design presents a steep challenge. A challenge that is mostly simplified through restrictive modelling and operating assumptions. One common assumption used is the uncertainty matching condition, in which the controller is expected to have direct access to any affecting uncertainty. This assumption is difficult to justify in continuum robots as due to their high nonlinear dynamics, practical limitations, and congested operating environments. Uncertainties could affect continuum robots from any of their states and are not necessarily reachable by the controller. Here, we will solve this problem using the multi-surface sliding mode control technique in combination with RBF neural networks as our uncertainties approximators. First, Cosserat rod theory is used to derive the dynamic model of the continuum robot due to its generality. Then, we propose an adaptive RBF neural network based multi-surface sliding mode control to guarantee tracking stability. Simulation results are included to verify the effectiveness of the proposed control scheme.

People perform most of their activities while being in an upright position. Nonetheless, there are some circumstances where they are required to adapt to different positions other than the upright position as in air manoeuvres and sport gymnastics. In these unexpected scenarios, the physiological signals are likely to change which can affect their performance. While some studies investigated the correlation between physiological signals and different body positions, to our best knowledge, these studies were limited to a rotating chair (1 or 2 degree of freedoms). Here, we investigated and evaluated human physiological responses (such as pupil diameter, skin temperature, heart rate, and breathing rate) to different seated positions including seated supine, seated side, seated inverted, and seated upright using Universal Motion Simulator (UMS), a 6 degree of freedom simulator with the most realistic acceleration and motion sensation. Open loop acrobatic flight motion sensation for the 11 participants were created and accompanied with a series of pre- and post-questionnaires to subjectively assess the physical wellbeing of each participant. The results of the study based on the objective assessment of collected physiological data showed that the mean heart rate decreases during an inverted position (82 beats per minutes bpm) and increased by an average of 7 beats per minute in an upright position 89.5 bpm. Moreover, the mean breathing rate in an upright position was 18.3 respirations per minutes (rpm) which is higher than mean breathing rate in the side position 19.8 rpm. Furthermore, it was found that the mean pupil diameter (PD) in an upright position was 4.33 mm which is higher compared to other positions. Independent from the motion scenarios and body positions, the Skin Temperature kept increasing which might be because of excitement and other emotional factors.

The motion simulator platform can be used in many sectors, including transportation, aviation, and education. The motion cueing algorithm (MCA) is the main component of the motion simulator with the responsibility of motion cues regeneration while respecting the motion simulator’s joint limitations. Recently, a model predictive control (MPC) method has been introduced in the MCA, which is able to extract an optimum input signal within the model constraints. The washout speed of the end-effector using the existing MPC-based MCA model is not considered because the integral of linear displacement of the platform is omitted as an output. As a result, the motion simulator platform returns to the neutral position without the consideration of the motion behavior. In this study, the integral of the end-effector linear displacement is consider inside the MPC-based MCA model to select the best washout speed of the end-effector. Moreover, a grey wolf optimizer is utilized to identify the best MPC weighting indexes, in order to increase the model efficiency for both existing and proposed models. The proposed method outperforms the MPC-based MCA model in producing better regeneration of the motion cues. It yields a higher correlation coefficient and a lower root means square error between the motion sensation signal pertaining to the real vehicle and motion simulator platform users.