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Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol's efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm's efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.

Functional losses associated with hand impairments have led to the growing development of hand exoskeletons. The main challenges are to develop the exoskeletons that work according to the user's motion intention, which can be done by utilizing the electromyogram signals generated by forearm muscles contributed from the movement and/or grasping abilities of the hand. In this research, modelling and EMG based control of hand exoskeletons with the aim to assist stroke survivors in regaining their hand strength and functionality, and improve their quality of life is presented.

This thesis presents the development of new nonlinear control algorithms with full-order state observers to overcome the control challenges encountered with nonlinear and underactuated systems. Quadcopter unmanned aerial vehicles are good example of underactuated systems, and this is selected in this research for validating the developed control and observer systems. It is shown through review of the literature that sliding mode control and sliding mode observer have many advantages over other control and observer methods in dealing with nonlinearity, underactuation, and coupled systems. However, the chattering phenomenon noted in such approaches can degrade the overall performance of the system. In this work, new methods to tackle the issue are proposed by employing sliding mode-based interval type-2 fuzzy control and sliding mode-based interval type-2 fuzzy observer. Moreover, in terms of control system, set-point weighting function and integral term are proposed and incorporated into the control system to improve the overall performance of the system. Furthermore, the weighting and integral terms are utilized to design further sliding mode-based control systems, including: quasi-sliding mode control, super-twisting algorithm of sliding mode control, and dynamic sliding mode control. In terms of observer, for the purposes of performance comparison, other nonlinear observer approaches are designed including: quasi-sliding mode observer, super-twisting algorithm of sliding mode observer, high order sliding mode observer, and extended Kalman filter. All designed control and observer methods are evaluated and compared their performances based on predefined performance criteria through numerical simulations. Wellperforming control and observer methods in simulated exercises are selected for further analysis and validation with real-time experimental investigations. From the simulation results obtained, overall set-point integral quasi-sliding mode control and quasi-sliding mode observer have performed better than other methods. The proposed methods of employing set-point weighting function and integral term to sliding mode-based control systems have performed very well in reducing overshoot, rise time, and steady-state errors. Meanwhile, in terms of experimental validations, set-point integral super-twisting algorithm of sliding mode control has shown better ability than set-point integral quasi-sliding mode control, and the observer method of quasi-sliding mode observer has yielded 2nd - order state well.

One of the common problems humans face when performing physical tasks is muscle/joint fatigue. Muscluskelatal disease is identified as one of the major health issues which result from fatigue. Fatigue could also cause performance degradation. Exoskeletons have been identified as means of overcoming such issues. Although, the realisation of exoskeletons for use in rehabilitation, military and industry applications has been widely carried out, however, their realisation for tackling human fatigue is very limited, and those researched are based on biology-based signal input such as electromyography signals. In addition, issues such as the design of upper-limb exoskeleton and conrresponding control approach need to be addressed. Several approaches have been proposed to de-weight the exoskeleton by using mechanical elements such as springs and intelligent control approaches based on fuzzy logic theory. The main aim of the research is to develop humanoid and exoskeleton models, within SimMechanics virtual environment, to perform an initial validation of proposed controller. The target users are people involved in prolonged and repetitive activity in domestic environments. The exoskeleton is aimed to assist and augment the upper-extremity in performing prolonged repetitive tasks by avoiding muscle/joint fatigue. A de-weighting upper extremity exoskeleton mechanism is proposed in this thesis. The de-weighting exoskeleton consists of a fuzzy-based PD and an extended fuzzy controller. To represent the human fatigue condition in the virtual platform, the quasi-static joint-level fatigue model is included. The exoskeleton is activated based on the information received from the human fatigue model. The results achieved demonstrate the capability of the exoskeleton with the proposed control approach in assisting human to carry out prolonged repetitive tasks.

Swarm intelligence algorithms, are among popular metaheuristic methods, developed and inspired by the collective behaviour of swarms that have attracted significant attention of researchers. The works related to swarm intelligence algorithms include the development of the algorithm itself, its modification and improvisation as well as its application in solving global optimisation problems. This thesis presents works on swarm intelligence algorithms that are inspired by real echolocation of a colony of bats and its performance evaluation to solve optimisation problems. The aim of the research is to introduce novel form of swarm intelligence algorithms based on real echolocation behaviour of bats. An adaptive bats sonar algorithm is proposed for solving single objective optimisation problems. A modified adaptive bats sonar algorithm is then proposed for solving constrained optimisation problems. Furthermore, a dual-particle swarm optimisation-modified adaptive bats sonar algorithm is proposed for solving multi objective optimisation problems. The algorithm is a hybrid algorithm that operates using dual level search strategy that takes merits of a particle swarm optimisation algorithm and a modified adaptive bats sonar algorithm. The superior performances of the developed bats echolocation-inspired algorithms are verified through rigorous tests with optimisation benchmark test functions and problems. Further, the performances of the developed algorithms are assessed in solving selected practical problems in business, mechanical/manufacturing engineering and electrical engineering fields. The results validate the better performance of the developed algorithms in single objective optimisation, constrained optimisation and multi objective optimisation problems of various fields.

In the last five decades, exoskeletons have emerged as a solution to assist paraplegic and elderly patients perform fundamental mobility tasks. The main challenge nowadays, is to develop a device that is safe, power sufficient, seamlessly integrates with the user, while being affordable. Several solutions have been proposed, and controllers have been identified as the only component which can enhance integration with the user without adding weight to the system, or increasing energy consumption. Moreover, a software platform where the mechanical design and control techniques can be assessed, prior to experimental trials, could save resources and decrease costs. In this thesis, the development of humanoid and exoskeleton models, within the SimWise virtual environment, to perform an initial validation of controllers proposed without the need of a physical prototype, is performed. Furthermore, the selected platform is evaluated regarding its fitness for this application. The methodology used to generate CAD models of a humanoid, exoskeletons and a wheel walker within the SimWise virtual environment is described, along with its integration with MATLAB Simulink. Two exoskeleton models with their corresponding controllers were developed, firstly, a hybrid exoskeleton with a wheel walker for restoration of walking in paraplegic patients. And secondly, an actuated exoskeleton for assistance in standing-up and sitting-down motions in both the elderly and paraplegic patients. The hybrid exoskeleton uses functional electrical stimulation as actuation for knee joints and a frame with brakes mounted at hip, knee and ankle joints to generate the walking cycle. The wheel walker is used for support and equilibrium. A fuzzy controller for the low level and a finite state controller for the middle level is developed. Validation of the system over repeated walking cycles, including external disturbances, and simulation of use by humanoids of different dimensions, is performed within the virtual environment and results discussed. PID low level control of hip and knee joints is used to analyse standing-up and sittingdown motions, and incorporated with an actuated exoskeleton for assisting elderly people on performing the aforementioned tasks. A finite state middle level control is developed to generate reference trajectories at variable velocities for the restoration of these motions for paraplegic patients. An optimisation algorithm is used to identify low level controller parameters for ankle joints. Finally, offline and online calculation and incorporation of zero moment point in the control loop is performed to assess equilibrium of the system.

Biologically-inspired soft-computing algorithms, which were developed by mimicking evolution and foraging techniques of animals in nature, have attracted significant attention of researchers. The works are including the development of the algorithm itself, its modification and its application in broad areas. This thesis presents works on biologically-inspired algorithm based on bacterial foraging algorithm (BFA) and its performance evaluation in modelling and control of dynamic systems. The main aim of the research is to develop new modifications of BFA and its combination with other soft computing techniques and test their performances in modelling and control of dynamic systems. Modification of BFA focuses for improving its convergence in terms of speed and accuracy. The performances of modified BFAs are assessed in comparison to that of original BFA. In the original BFA, in this thesis referred as standard BFA (SBFA), bacteria use constant chemotactic step size to head to global optimum location. Very small chemotactic step size around global optimum location will assure bacteria find the global optimum point. However, a large number of steps is needed for the whole optimisation process. Moreover, there is potential for the algorithm to be trapped in one of the local optima. On the contrary, big chemotactic step size will assure bacteria have faster convergence speed but the literature shows that it results oscillation around global optimum point and the algorithm potentially missing the global optimum point and leading to oscillation around the point. Thus SBFA can be improved by applying adaptable chemotactic step size which could change: very large when bacteria are in locations far away from the global optimum location, to speed up the convergence, and very small when bacteria are in the locations near the global optimum so that bacteria able to find global optimum point without oscillation. Here, four novel adaptation schemes allowing the chemotactic step size to depending on the cost function value have been proposed. The adaptation schemes are developed based on linear, quadratic and exponential functions as well as fuzzy logic (FL). Then, the proposed BFAs with adaptable chemotactic step size, i.e. linearly adaptable BFA (LABFA), quadratic adaptable BFA (QABFA), exponentially adaptable BFA (EABFA) and fuzzy adaptable chemotactic step size (FABFA), are validated by using them to find global minimum point of seven well-known benchmark functions commonly used in development of optimisation techniques development. The results show that all ABFAs achieve better accuracy and speed compared to those of SBFA. The ABFAs are then used in modelling and control of a single-link flexible manipulator system. This includes modelling (based on linear model structures, neural network (NN), and fuzzy logic (FL)), optimising joint-based collocated (JBC) proportional-derivative (PD) control, and optimising both PD and proportional integral derivative (PID) control of end-point acceleration feedback for vibration reduction of a single-link flexible manipulator. The results show that ABFAs outperform SBFA in terms of convergence speed and accuracy. Since all SBFA and ABFAs use the same general parameters and bacteria are initially placed randomly across the nutrient media (cost function), the superiority better performance of ABFAs are attributed to the proposed adaptable chemotactic step size.