Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

Arduino kits were used in place of general microcontrollers in a mechatronics course. The hand-on learning environment were mostly self-learned with provided learning kits, tutorials and examples from online resources with lecturers as facilitators. Efficient learning could be achieved with 20-30 students in groups of 4 or 5 against 1 lecturer. The theory was assessed by a written exam from which comparable performances to previous years were observed. The learning kits were also used for the semester project in which students had a limited budget for parts and access to a small-scale manufacturing facility.

XY gantry systems play an important role in many applications in diverse industries, where they are used to position a part or tool along the xy plane within the working area of the system. The increased demand for enhanced performance and low cost of XY gantry systems has driven research to develop alternative structural designs and improve their capabilities. A two-axis, parallel H-frame XY positioning system (H-Bot) is of increasing interest as a candidate for development due to its low number of moving parts, lightweight, low cost and speed of the system. However, the system has an uncertainty of cart or end-effector position when moving at high speed because of the friction and flexibility of the elastic timing belt. The H-bot developed here using an adaptive gain control showed a good repeatability and improved accuracy, reducing the root mean square error between the desired and the actual trajectory of 32.7% and 53.2% on the x-axis and y-axis, respectively, for drawing a 80 mm diameter circle in 36 seconds.

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