This research explores the development and effectiveness of a two-wheeled balanced autonomous vehicle. Autonomous vehicles are capable of perceiving their environment and functioning without human intervention, gaining increasing attention due to their advanced automation capabilities. A fully autonomous system demonstrates self-awareness and independent decision-making. These vehicles utilize an array of sensors, actuators, machine learning algorithms, and high-performance processors to execute complex software tasks. The proposed two-wheeled autonomous vehicle achieves self-balancing through mechanisms such as PID or IADRC control systems and enables wireless steering via IoT connectivity using Bluetooth technology. Communication is facilitated through Java-based applications on mobile devices or personal computers. Equipped with radar sensors, the vehicle monitors nearby objects, while video cameras identify traffic lights, road signs, and pedestrians. Lidar technology aids in measuring distances, detecting road edges, and recognizing lane markings, and ultrasonic sensors ensure precise parking by identifying curbs and nearby obstacles. The innovative IADRC control system offers enhanced robustness, superior disturbance rejection, and effective obstacle avoidance. Additionally, the vehicle operates on clean energy, minimizing carbon emissions and contributing to environmental sustainability. Its potential applications include last-mile delivery services and other small-area operations, reinforcing its value as an eco-friendly and versatile solution for modern transportation challenges.

This paper presents the output state feedback approach, a unique adaptive control mechanism for power system dynamic stability. A new adaptive stabilizing method for synchronous power systems based on Minimal Control Synthesis (MCS) is proposed. Industrial applications can benefit from synchronous power systems. It boosts production and power efficiency. The MCS adaptive control structure uses hyper-stability theory. Power System Stabilizers (PSSs) have been used in industry for years to improve power system dynamic stability and dampening. most power systems are very dynamic and non-linear. Traditional PSS uses linearized power system model and fixed parameter linear control theory. Fixed parameter controllers can’t sustain power system dynamic stability. The MCS method’s key virtue is that it requires only a minimal framework and little computational resources. The controller manages plant nonlinearities, mild disturbances, and parameter changes using proportional and integral type adaptation to meet hyper-stability criteria. Stabilizing signals are created at the machine system’s excitation input for well-defined closed-loop performance. Synthesizing an output feedback control from observed feedback signals is desirable and technically achievable. The proposed control structure overcomes the difficulties of generating an online parameter estimator and choosing a reference model compared to MRAC or STAC. The investigated power system has an endless bus connected to a synchronous machine. Simulations verify the controller’s ability to moderate machine oscillations caused by minor power system disturbances. The results and MATLAB/Simulink operational simulation results end this research. The mode damping ratio is 0.0142, which is within the predicted range of 0.1 to 0.5.