An intelligent computational modeling framework for robotic kinematics and control using neural networks with genetic optimization and reinforcement learning

This paper presents an intelligent computational modeling framework that integrates data-driven surrogate modeling methods into the simulation and control pipeline of a six-degree-of-freedom (6-DOF) industrial manipulator. Traditional model-based approaches for robotic kinematics, dynamics, and trajectory optimization face inherent limitations when confronted with high-dimensional nonlinearities, kinematic singularities, and real-time computational constraints. To address these challenges, we propose a unified architecture in which Genetic Algorithms (GA), Neural Networks (NN), and Reinforcement Learning (RL) are systematically repositioned as surrogate solvers within a coherent computational modeling workflow. The core technical novelty lies not in the individual algorithms—which are well established—but in the unified agent computing layer that couples them through formally defined interfaces, enabling end-to-end surrogate-based simulation-to-control deployment. The framework encompasses: (1) a GA-based trajectory optimizer with formal time complexity analysis O(N·P·G) and empirical convergence validation, benchmarked against sequential quadratic programming (SQP) under identical constraints; (2) a feedforward neural network serving as a surrogate inverse kinematics solver, accompanied by generalization error bounds and the scaling of prediction accuracy with training data, compared against damped least-squares (DLS) and Levenberg–Marquardt (LM) numerical solvers; (3) an RL-based adaptive control policy trained via DDPG and benchmarked against PPO and SAC, and further compared against model predictive control (MPC), model-reference adaptive control (MRAC), and sliding-mode control (SMC) under identical disturbance conditions. Comprehensive numerical analyses include computational complexity, convergence curves, FLOP counts, and inference latency. A co-simulation validation protocol quantifies numerical synchronization error between virtual and physical platforms. A quantitative integration ablation study demonstrates that the coupled GA–NN–RL pipeline outperforms any single-module or two-module configuration, and an extended robustness characterization reveals the RL controller’s degradation boundary beyond 2.5 Nm disturbance torque. Experimental results demonstrate that the NN surrogate achieves a 4.2× speedup over analytical IK with mean joint error 0.41°, the GA optimizer reduces cycle time by 28.8%, and the RL agent achieves adaptive path correction within 0.3 s under ± 2 mm perturbations. The contributions are fourfold: (i) a unified framework integrating GA, NN, RL through a dedicated agent computing layer; (ii) rigorous numerical analyses with controlled comparisons against established baselines; (iii) a validated co-simulation protocol; (iv) a quantitative integration ablation and extended robustness analysis.