Fish Swarmed Kalman Filter for State Observer Feedback of Two-Wheeled Mobile Robot Stabilization

Over the past few decades, there have been significant technological advancements in field of robots, particularly area mobile robots. The performance standards speed, accuracy, and stability become key indicators progress robotic technology. Self-balancing robots are designed to maintain an upright position without toppling over. By continuously adjusting their center mass, they can even when disturbed by external forces. This research aims achieving maintaining balance is a complex task. must accurately sense orientation, calculate corrective actions, execute precise movements stay upright. Eliminating disturbances measurement noise self-balancing robot enhance accuracy output. One common technique for this using Kalman filters, which effective addressing non-stationary linear plants with unknown input signal strengths that be optimized through filter poles process covariances. Additionally, advanced methods developed account white noise. In research, state estimation was conducted Fish Swarm Optimization Algorithm (FSOA) provide feedback controller overcome effects measurements filter. FSOA mimics social interactions coordinated observed fish groups solve optimization problems. primarily used tasks where finding global optimal solution desired. results show use on two-wheeled handle system reduces values 38.37%, reaches steady value 3.8 s error 0.2%. addition, proposed method, filtering help improve self balancing robot’s System response becomes faster towards compared other also applied