This paper presents a novel control laboratory project that provides hands-on experience in feedback control concepts (embedded control systems) through dedicated assignments, with a particular focus on the design and implementation of fuzzy control. The project is structured around an inexpensive, portable self-balancing robot (SBR), whose embedded system is realized using commercially available breakout boards as the first assignment. For the stabilization of the plant, students are guided to execute the essential stages of control system design, from system modeling and parameter optimization, over basic or advanced control strategy design in the MATLAB/Simulink environment, to both implementation and validation of the closed loop on the real robot. To demonstrate and foster the application of fuzzy logic, the second part of the paper introduces a simple control strategy based on fuzzy logic controllers. Then, a lookup table-based implementation technique is described for the demonstration of manual interfacing and embedded coding of fuzzy control strategies. The proposed methods are clear and straightforward; they highly foster the understanding of feedback control techniques and allow students to gain vast knowledge in the practical implementations of control systems.

This paper proposes two novel approaches to estimate accurately mobile robot attitudes based on the fusion of low-cost accelerometers and gyroscopes. The first part of the paper demonstrates the use of a special test bench that both enables simulations of various dynamic behaviors of wheeled robots and measures their real attitude angles along with the raw sensor data. These measurements are applied in a simulation environment and we outline an offline optimization of Kalman filter parameters. The second part of the paper introduces a novel adaptive Kalman filter structure that modifies the noise covariance values according to the system dynamics. The instantaneous dynamics are characterized regarding the magnitudes of both the instantaneous vibration and the external acceleration. The proposed adaptive solution measures these magnitudes and utilizes fuzzy-logic to modify the filter parameters in real time. The results show that the adaptive filter improves the overall filter convergence by a remarkable 10.9% over using the optimized Kalman filter, thereby demonstrating its efficacy as an accurate and robust attitude filter. The proposed filter performances are also benchmarked against other common methods indicating that the flexibility of the developed adaptive filter allowed it to compete and even outperform the benchmark filters.