NEURAL NETWORK-BASED CONTROL ALGORITHMS IN ROBOTICS
Keywords:
neural networks, robotics, control algorithms, deep reinforcement learning, adaptive control, motion planning, autonomous systems.Abstract
This paper examines neural network-based control algorithms as applied to robotic systems, exploring their theoretical foundations, practical implementations, and comparative advantages over classical control methods. The study analyzes key architectures including feedforward networks, recurrent neural networks (RNN), convolutional neural networks (CNN), and deep reinforcement learning frameworks used for robotic motion planning, adaptive control, and sensor fusion. Findings suggest that neural network controllers achieve superior performance in non-linear, high-dimensional environments while demonstrating greater adaptability to environmental uncertainty. Challenges such as computational cost, interpretability, and real-time deployment constraints are also discussed.
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