Automobile engineering often demands creative and innovative concepts to achieve their performance and efficiency targets. However, the strategies used to create these concepts are evolving with time. Recently, heightened attention has been noticed to using biomimetic concepts in the field of automobile design. The studies that apply biomimicry to automobile design often exhibit improvements in vehicle performance and fuel efficiency. Consequently, biomimetic concepts have extended an opportunity to the automobile industry to generate futuristic designs with advanced technology. However, the innovations and inventions in biology are usually published with more complex terminology that is not convenient for the research community working on automobile engineering. Hence, a notable delay is present in transferring the recent discoveries in biology to engineering researches so that they can develop strategies to mimic them. The purpose of this paper is to provide a brief overview of the biomimetic design approaches that have been and can be used in automobile design. In addition, the paper includes a classification of biomimetic concepts, which reveals the current research interests and highlights the areas with a deficit of research that is required to be addressed extensively in the future.

Designing and developing AI controllers on separately dedicated chips have many advantages. This paper reviews the development of a real-time fuzzy logic controller for speed control of a dc motor using Arduino Due board. The proposed fuzzy logic controller is based on Mamdani approach and has been tested on the aforementioned high performance microcontroller board and using MATLAB. During the real-time operation the dc motor behavior and the fuzzy controller's response were plotted and the data were stored in MATLAB without interrupting the fuzzy logic controller. Based on these observed information, the system settling time and the rise time reduction were calculated for each input wave patent trajectories while increasing the wave frequency. It was noted that the system overshoot is negligible. Utilizing the aforementioned parameters the Arduino Due board performance was analyzed with the fuzzy logic speed control approaches of dc motors made by past researchers as mentioned above. The system response shows a satisfactory performance for this particular dc motor application when the input signal (desired output signal) frequency is less than 2 Hz, but further research is needed when identifying the optimum performance of the Arduino Due board for different fuzzy logic algorithms while increasing the desired input signal frequency.

More than one billion people face disabilities worldwide, according to the World Health Organization (WHO). In Sri Lanka, there are thousands of people suffering from a variety of disabilities, especially hand disabilities, due to the civil war in the country. The Ministry of Health of Sri Lanka reports that by 2025, the number of people with disabilities in Sri Lanka will grow by 24.2. In the field of robotics, new technologies for handicapped people are now being built to make their lives simple and effective. The aim of this research is to develop a 3-finger anatomical robot hand model for handicapped people and control (flexion and extension) the robot hand using motor imagery. Eight EEG electrodes were used to extract EEG signals from the primary motor cortex. Data collection and testing were performed for a period of 42s timespan. According to the test results, eight EEG electrodes were sufficient to acquire the motor imagery for flexion and extension of finger movements. The overall accuracy of the experiments was found at 89.34 (mean=22.32) at the 0.894 precision. We also observed that the proposed design provided promising results for the performance of the task (grab, hold, and release activities) of hand-disabled persons.

This paper presents the speed regulation of a Sensored BLDC (Brushless Direct Current) Motor through a Genetic-Fuzzy controller, where the Sensored BLDC motor was modeled in MATLAB Simulink environment according to State-Space analysis approach. When designing Fuzzy Logic controllers (FLCs) there is no generalized defined approach and these controllers are mainly based on linguistically defined variables which are non-linear elements, that are impossible to model accurately. Our test results shows that the fuzzy controller's output highly depends on the fuzzy rules. In some situations, very experienced and a skillful expert's solutions (fuzzy rules) even may not satisfy the desired output. In many cases FLCs rule bases have been designed according to trial-and-error method which makes the optimization of the solution very difficult. As a solution, FLC of the Sensored BLDC motor was tuned through a stochastic search optimization technique which is based on GA (Genetic Algorithm) and the GA parameters (Crossover, Mutation rates etc.) adapted through another TSK-FLC (Takagi-Sugeno-Kang type FLC) in real-time. The optimization stochastic search process was implemented using a fitness function index (i.e. a predefined threshold level) which is calculated from the population (randomly generated solutions by the GA) based on the E (Error), MAE (Mean Absolute Error) and the RMSE (Root Mean Square Error). The simulated test results shows the proposed control technique has effectively reduced the maximum overshoot, settling time, steady state error and the rise time by 12%, 15%, 11% and 1% respectively. But further research is needed to optimize the search algorithm to increase the Genetic-Fuzzy controller's efficiency and the stability to withstand external disturbances while increasing the frequency for various desired input signal wave pattern trajectories.

This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.