Neuro-Adaptive Augmented Dynamic Inversion Based PIGC Design for Reactive Obstacle Avoidance of UAVs

Abstract

In this paper, the problem of reactive obstacle avoidance is addressed by an innovative partial integrated guidance and control (PIGC) approach using the Six-DOF model of real UAV unlike the kinematic models used in the existing literatures. The guidance strategy uses the collision cone approach to predict any possible collision with the obstacle and computes an alternate aiming direction for the vehicle to avoid the obstacle. The reactive nature of the avoidance problem within the available time window demands simultaneous reaction from the guidance and control loop structures of the system i.e, in the IGC framework (executes in single loop). However, such quick maneuvers causes the faster dynamics of the system to go unstable due to inherent separation between the faster and slower dynamics. On the contrary, in the conventional design (executes in three loops), the settling time of the response of different loops will not be able to match with the stringent time-to-go window for obstacle avoidance. Such tracking delays will affects the system performance adversely. However, in the PIGC framework, it overcomes the disadvantage of both the IGC design and the conventional design. PIGC approach executes the avoidance maneuver in two loops. In the outer loop, the vehicle guidance strategy attempts to reorient the velocity vector of the vehicle along the aiming point within a fraction of the available time-to-go. The outer loop generates the body angular rates which are tracked by the inner loop to generate the necessary control surface deflections. Control surface deflections are realized by the vehicle through the first order actuator dynamics. A controller for the first order actuator model is proposed in order to reduce the actuator delay. Every loop of the PIGC technique uses nonlinear dynamic inversion technique which has critical issues like sensitiveness to the modeling inaccuracies of the plant model. To make it robust against the parameter inaccuracies of the system, it is reinforced with the neuro-adaptive design. In NA design, weight update rule based on Lyapunov theory provides online training of the weights. To enhance fast and stable training of the weights, preflight maneuvers are proposed. Preflight maneuvers provides stabilized pre-trained weights which prevents any misapprehensions in the obstacle avoidance scenario. Simulation studies have been executed with different number and size of the obstacles. NA augmented PIGC design is validated with different levels of uncertainties in the plant model. Various comparative study shows that the NA augmented PIGC design is quite effective in avoiding collisions in different scenarios. Since the NDI technique involved in the PIGC design gives a closed loop solution and does not operate with iterative steps, therefore the reactive obstacle avoidance is achieved in a computationally efficient manner.