Robust Partial Integrated Guidance And Control Of UAVs For Reactive Obstacle Avoidance
UAVs employed for low altitude jobs are more liable to collide with the urban structures on their way to the goal point. In this thesis, 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 algorithm is designed which uses the collision cone approach to predict any possible collision with the obstacle and computes an alternate aiming direction for the vehicle. The aiming direction of the vehicle is the line of sight line tangent to the safety ball surrounding the obstacle. The point where the tangent touches the safety ball is the aiming point. Once the aiming point is known, the obstacle is avoided by directing the vehicle (on the principles of pursuit guidance) along the tangent to the safety ball. First, the guidance algorithm is applied successfully to the point mass model of UAV to verify the proposed collision avoidance concept. Next, PIGC approach is proposed for reactive obstacle avoidance of UAVs. 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 cause 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. This causes delay in tracking in all the loops which will affect the system performance adversely and hence UAV will fail to avoid the obstacle. However, in the PIGC framework, it overcomes the disadvantage of both the IGC design and the conventional design, by introducing one more loop compared to the IGC approach and reducing a loop compared to the conventional approach, hence named as Partial IGC. Nonlinear dynamic inversion technique based PIGC approach utilizes the faster and slower dynamics of the full nonlinear Six-DOF model of UAV and 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 orientation of the velocity vector is achieved by enforcing the angular correction in the horizontal and vertical flight path angles and enforcing turn coordination. The outer loop generates the body angular rates which are tracked as the commanded signal in the inner loop. The enforcement of the desired body rates generates the necessary control surface deflections required to stir the UAV. Control surface deflections are realized by the vehicle through the first order actuator dynamics. A controller for the first order actuator model is also 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 the inner loop of the PIGC design. In the NA design, weight update rule based on Lyapunov’s theory provides online training of the weights. To enhance fast and stable training of the weights, preflight maneuvers are proposed. Preflight maneuvers provide stabilized pre-trained weights which prevents any misapprehensions in the obstacle avoidance scenario. Simulation studies have been carried out with the point mass model and with the Six-DOF model of the real fixed wing UAV in the PIGC framework to test the performance of the nonlinear reactive guidance scheme. Various simulations 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. A comparative study in NA augmented PIGC design was performed between the pre-trained weights and zero weights as used for weight initialization in online training. 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.