Real-Time Intelligent Bio-Robotic Control Using Advanced Nonlinear Neural Networks

Intelligent and adaptive control is necessary for bio-robotic systems to deal with complex nonlinear dynamics and variations in the environment. Typical robot controllers are not very adaptable, have high trajectory errors, and have slow response times for dynamic motion commands. To overcome these drawbacks, in this paper, a novel real-time intelligent bio-robotic control framework, named Bio-Adaptive Nonlinear Neural Control Network with Adaptive Particle Swarm Optimization (BANN-ControlNet-APSO), is proposed. The proposed model combines the advanced nonlinear neural learning with Adaptive Particle Swarm Optimization (APSO) to optimize neural weights, control gains, and parameters associated with the response of the actuators. First, the motion information of the robot is measured by the sensor and preprocessed to obtain essential dynamic motion information. The BANN-ControlNet learns nonlinear robotic behavior and anticipates adaptive control actions, and APSO enhances the accuracy of the trajectory tracking and system stability. To reduce the deviation from the planned motion, a real-time feedback correction mechanism is integrated, which improves the autonomous decision-making function. The experimental results show that the proposed framework can achieve better control accuracy, shorter response time, smaller trajectory tracking error, better stability, and lower energy consumption, which are more suitable for the application of advanced bio-robotics and rehabilitation.