Introduction: Cascaded PID control remains popular in quadcopter platforms because it is simple to implement and certify; however, tuning the coupled attitude–altitude loops is often time‑consuming and sensitive to disturbances, actuator limits, and model mismatch. This work targets the inner-loop controller of an existing MATLAB/Simulink quadcopter model, where four PIDs regulate roll (φ), pitch (θ), yaw (ψ), and altitude (z) under an outer-loop command generator.
Methods: Three tuning strategies are compared under an equal simulation budget: (i) a classical baseline using Simulink PID Tuner followed by manual refinement, (ii) particle swarm optimization (PSO) directly optimizing the 12 PID gains, and (iii) reinforcement-learning‑guided PSO (RL‑PSO), where PSO searches the gain vector while an RL agent adapts PSO hyperparameters (inertia weight and acceleration coefficients) online based on swarm progress and diversity features. The objective function combines integrated time‑weighted absolute error (ITAE) tracking terms for φ, θ, ψ, and z with penalties on overshoot, control effort, actuator saturation, and unstable responses.
Results: A robustness benchmark is defined using two disturbance-focused scenarios: (1) feedback-path perturbations and (2) plant-side disturbances. Performance will be reported using Monte‑Carlo statistics of RMS error, overshoot, settling time, control effort, and constraint violations, together with convergence curves (best cost versus iteration and total model evaluations). The study is designed to test the hypothesis that RL‑PSO improves worst‑case disturbance rejection and reduces constraint violations relative to standard PSO and classical tuning.
Conclusions: RL‑PSO provides a practical, simulation-based route to robust multi-loop PID tuning for quadcopter attitude–altitude control without altering the overall cascaded control structure.