Prescribed chattering reduction control for quadrotors using aperiodic signal updating

In the paper, a prescribed chattering reduction control using aperiodic signal updating is presented for quadrotors subject to parameter uncertainties and external disturbances. Using estimation errors instead of tracking errors to update adaptive laws, estimator-based minimum learning parameter (EMLP) observers capable of relaxing computational complexity are respectively explored in translational and rotational loops to reject fast time-varying disturbances, such that transient oscillations can be efficiently mitigated even with a large adaptive gain. Meanwhile, quantitative analysis for transient learning performance is characterized by means of L₂ norms of time differential of neural network weights. With the aid of disturbance estimates, a relative event-triggered robust control law is derived by inserting a compensation term to guarantee a favorable trajectory tracking with Zeno free behaviors and decreased sampling cost. Besides, an appointed-time prescribed performance control (APPC) is established, enforcing trajectory tracking errors to evolve within pre-given regions even in face of triggering errors, where a piecewise and continuous finite-time behavior function, rather than an exponential decaying function, is applied to enable a preassigned fast convergence time without retuning controller parameters. Finally, the stability of closed-loop system is proved via Lyapunov synthesis, while comparative studies are provided to validate the effectiveness of presented control method.