To address the concern of classical guidance and control designs (where guidance and control loops are designed separately in an “outer loop – inner loop” structure), integrated guidance and control (IGC) ideas have been proposed in the recent literature. An important limitation of the existing IGC algorithms, however, is that they do not explicitly exploit the inherent time scale separation that exist in aerospace vehicles between rotational and translational motions, and hence, can be ineffective unless the engagement geometry is close to the collision triangle. To address this, a time scale separated partial integrated guidance and control (PIGC) structure has been proposed in this thesis. In this two-loop design, the commanded pitch and yaw rates are directly generated from an outer loop optimal control formulation, which is solved in a computationally efficient manner using the recently-developed model predictive static programming (MPSP) and Model Predictive Spread Control (MPSC) techniques. The necessary roll-rate command is generated from a roll-stabilization loop. The inner loop then tracks the outer loop commands using the nonlinear dynamic inversion philosophy. However, unlike classical guidance and control designs, in both the loops the Six-DOF interceptor model is used directly. This intelligent manipulation preserves the inherent time scale separation property between the translational and rotational dynamics, and hence overcomes the deficiency of current IGC designs, while preserving the benefits of the IGC philosophy. The new approach has been applied in the terminal phase of an endo-atmospheric interceptor for engaging incoming high speed ballistic missile targets. Six–DOF simulation results will be presented accounting for a 3-D engagement geometry to demonstrate the usefulness of this method. It offers two important advantages: (i) it leads to very small (near-zero) miss distance, resulting in a “hit-to-kill” scenario and (ii) it also leads to lesser and smoother body-rate demands, relaxing the demand on actuators as well as enlarging the ‘capture region’ (which relaxes the demand on mid-course guidance). Next, to address the problem of modeling inaccuracy that is inherent in aerospace vehicles (mainly because of the inaccuracy of aerodynamic model generated from wind-tunnel testing), a neuro-adaptive design is augmented to dynamic inversion technique in the inner loop. In this design the unmodelled dynamics is adaptively captured using three neural networks in the roll, pitch and yaw channels. Training of the neural networks is carried out online using the Lyapunov stability theory, which results in stability of the inner-loop error dynamics as well as boundedness of network weights. This adaptive body rate tracking loop augmented with the sub-optimal feedback guidance loop results in substantial enhancement of interception performance in presence of realistic (i.e. fairly large) modeling uncertainties of the interceptor. The results have also been validated with representative seeker noise.

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.

The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.