Nonlinear Model Predictive Control for Quadrotors Using Simultaneous Optimization Approach

The quadrotor has found extensive applications across a spectrum of fields, including environmental research, military operations, transportation, and firefighting. The design of its control system can be approached using various methodologies such as sliding mode control, backstepping, and model predictive control (MPC). The primary objective of the control system is to achieve trajectory tracking while simultaneously avoiding collisions with obstacles. In this paper, a nonlinear model predictive control (NMPC) strategy is adopted for calculating control inputs that guide the quadrotor along a desired trajectory while ensuring obstacle avoidance. The obstacle avoidance constraints that are formulated pose a nonlinear and intricate challenge, rendering the optimization problem complex for the nonlinear program solver to address. To tackle this, linear constraints representing hyperplanes between the quadrotor and obstacles are employed. These hyperplanes dynamically adjust as the quadrotor moves, thereby averting collisions effectively. The application of NMPC to quadrotor control, as demonstrated in this paper, underscores the suitability and practicality of the proposed approach. It successfully showcases the efficacy of NMPC in facilitating quadrotor control while navigating through complex environments.