Stabilization Control of 3D Inverted Pendulum on Aircraft

Stabilizing a 3D inverted pendulum on an aircraft represents a highly challenging control task, requiring precise nonlinear dynamic system modeling and efficient control strategy development. This research achieves stabilization through multiple advanced control methods with comprehensive robustness validation.

First, reinforcement learning-based adaptive dynamic programming plays a crucial role. Value iteration and policy iteration algorithms enable the system to gradually optimize control policies under uncertain conditions, allowing adaptation to dynamically changing environmental parameters. This approach is particularly suitable for complex systems that are difficult to model accurately, where iterative Q-learning updates and policy improvement steps can be implemented through recursive Bellman equation computations.

Neural network control methods provide another powerful tool. By training neural networks to approximate system dynamics, the controller can real-time adjust parameters according to different operational states. The nonlinear mapping capability of neural networks, particularly using multilayer perceptron architectures with tanh/ReLU activation functions, demonstrates excellent performance when handling highly nonlinear inverted pendulum systems. The network training typically involves backpropagation through time (BPTT) for temporal sequence learning.

The LQR (Linear Quadratic Regulator) optimal control method provides theoretical optimal solutions for state regulation. Although LQR relies on linear assumptions, proper linearization around equilibrium points yields satisfactory performance near balancing positions. When integrated with other methods, the combined control system achieves enhanced stability. Implementation involves solving the algebraic Riccati equation to obtain optimal feedback gain matrices.

To validate system robustness, Gaussian white noise disturbance experiments were conducted. Results demonstrate that the control system maintains pendulum stability even under external disturbances, proving the effectiveness of the implemented approaches. The robustness testing typically involves Monte Carlo simulations with varying noise covariance matrices to assess performance degradation.

By integrating multiple control strategies, this research not only achieves stable 3D inverted pendulum control on aircraft but also provides valuable references for similar complex system control problems. The hybrid control architecture may involve supervisory switching logic or weighted combination of different controllers' outputs.