Master of Engineering (Research) / This thesis deals with the problem of cooperative explorations of a group of autonomous vehicles in unknown environments in the context of decentralized behaviour. The main contribution of this thesis is the development of a comprehensive decentralized cooperative exploration frame work in which each individual vehicle has the ability to explore an unknown environment by itself and also by cooperative behaviour in a team of several vehicles. To simulate the whole system, each individual vehicle will have the ability to explore an unknown environment by dynamically path-planning (with obstacle and collision avoidance), high-level con- trolling, updating the environment map, proposing potential destinations (frontiers), and solving online task assignment. In this thesis, the framework simulates an unknown environment as an occupancy grid map and uses a frontier-base exploration strategy, in which a cell will be marked as a frontier if it is adjacent at least one open cell, as the core architecture. In dealing with the uncertainties in process transition and observation models of autonomous vehicles, the well-known discrete extended Kalman filter (EKF) algorithm is investigated and implemented. When exploring the environment, a vehicle will update its surrounding information, then propose its potential destinations and evaluate the utility (benefit) to travel to each of those destinations. The benefit to go to each destination is derived from the subtraction of the utility (value) of that cell to the sum of the cost to travel to that cell and the steering cost. The key to cooperative exploration in the team of vehicles lies in each vehicle's ability to communicate the updates of the world to the whole team and to contribute to the global list of potential destinations. And each vehicle has the capability of solving the task assignment problem for the team by calling its own online-task-assignment solving engine. This algorithm results each vehicle in having a destination to visit, which benefits the whole team the most and reduces the total exploration time of the team.

Master of Engineering (Research) / This thesis deals with the problem of cooperative explorations of a group of autonomous vehicles in unknown environments in the context of decentralized behaviour. The main contribution of this thesis is the development of a comprehensive decentralized cooperative exploration frame work in which each individual vehicle has the ability to explore an unknown environment by itself and also by cooperative behaviour in a team of several vehicles. To simulate the whole system, each individual vehicle will have the ability to explore an unknown environment by dynamically path-planning (with obstacle and collision avoidance), high-level con- trolling, updating the environment map, proposing potential destinations (frontiers), and solving online task assignment. In this thesis, the framework simulates an unknown environment as an occupancy grid map and uses a frontier-base exploration strategy, in which a cell will be marked as a frontier if it is adjacent at least one open cell, as the core architecture. In dealing with the uncertainties in process transition and observation models of autonomous vehicles, the well-known discrete extended Kalman filter (EKF) algorithm is investigated and implemented. When exploring the environment, a vehicle will update its surrounding information, then propose its potential destinations and evaluate the utility (benefit) to travel to each of those destinations. The benefit to go to each destination is derived from the subtraction of the utility (value) of that cell to the sum of the cost to travel to that cell and the steering cost. The key to cooperative exploration in the team of vehicles lies in each vehicle's ability to communicate the updates of the world to the whole team and to contribute to the global list of potential destinations. And each vehicle has the capability of solving the task assignment problem for the team by calling its own online-task-assignment solving engine. This algorithm results each vehicle in having a destination to visit, which benefits the whole team the most and reduces the total exploration time of the team.

This research work presents a fault-tolerant flight guidance and control framework to deal with damaged aircraft. Damaged scenarios include the loss of thrust, actuator malfunction and airframe damage. The developed framework objective is to ensure that damaged aircraft can be stabilized and controlled at all times. The guidance system is responsible for providing the airspeed, vertical and horizontal flight path angle commands while considering aircraft dynamics. The control system, designed by the nonlinear state-dependent Riccati equation (SDRE) control method, is used to track the guidance commands and to stabilize the damaged aircraft. The versatility of SDRE allows it to passively adapt to the aircraft parameter variations due to damage. A novel nonlinear adaptive control law is proposed to improve the controller performance. The new control law demonstrated improved tracking ability. The framework is implemented on the nonlinear Boeing 747 and NASA Generic Transport Model (GTM) to investigate the simulation results.

This research work presents a fault-tolerant flight guidance and control framework to deal with damaged aircraft. Damaged scenarios include the loss of thrust, actuator malfunction and airframe damage. The developed framework objective is to ensure that damaged aircraft can be stabilized and controlled at all times. The guidance system is responsible for providing the airspeed, vertical and horizontal flight path angle commands while considering aircraft dynamics. The control system, designed by the nonlinear state-dependent Riccati equation (SDRE) control method, is used to track the guidance commands and to stabilize the damaged aircraft. The versatility of SDRE allows it to passively adapt to the aircraft parameter variations due to damage. A novel nonlinear adaptive control law is proposed to improve the controller performance. The new control law demonstrated improved tracking ability. The framework is implemented on the nonlinear Boeing 747 and NASA Generic Transport Model (GTM) to investigate the simulation results.

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.

There are two traditional aircraft categories: fixed-wing which have a long endurance and a high cruise airspeed and rotorcraft which can take-off and land vertically. The tailsitter is a type of aircraft that has the strengths of both platforms, with no additional mechanical complexity, because it takes off and lands vertically on its tail and can transition the entire aircraft horizontally into high-speed flight. In this dissertation, we develop the entire control system for a tailsitter with a ducted fan. The standard method to compute the quaternion-based attitude error does not generate ideal trajectories for a hovering tailsitter for some situations. In addition, the only approach in the literature to mitigate this breaks down for large attitude errors. We develop an alternative quaternion-based error method which generates better trajectories than the standard approach and can handle large errors. We also derive a hybrid backstepping controller with almost global asymptotic stability based on this error method. Many common altitude and airspeed control schemes for a fixed-wing airplane assume that the altitude and airspeed dynamics are decoupled which leads to errors. The Total Energy Control System (TECS) is an approach that controls the altitude and airspeed by manipulating the total energy rate and energy distribution rate, of the aircraft, in a manner which accounts for the dynamic coupling. In this dissertation, a nonlinear controller, which can handle inaccurate thrust and drag models, based on the TECS principles is derived. Simulation results show that the nonlinear controller has better performance than the standard PI TECS control schemes. Most constant altitude transitions are accomplished by generating an optimal trajectory, and potentially actuator inputs, based on a high fidelity model of the aircraft. While there are several approaches to mitigate the effects of modeling errors, these do not fully remove the accurate model requirement. In this dissertation, we develop two different approaches that can achieve near constant altitude transitions for some types of aircraft. The first method, based on multiple LQR controllers, requires a high fidelity model of the aircraft. However, the second method, based on the energy along the body axes, requires almost no aerodynamic information.

Novel methods of system identification are developed in this dissertation. First set of methods are designed to realize time varying linear dynamical system models from input-output experimental data. The preliminary results obtained in a recent paper by the author are extended to establish a new algorithm called the Time Varying Eigensystem Realization Algorithm (TVERA). The central aim of this algorithm is to obtain a linear, time varying, discrete time model sequence of the dynamic system directly from the input-output data. Important results relating to concepts concerning coordinate systems for linear time varying systems are developed (discrete time theory) and an intuitive understanding of equivalent realizations is provided. A procedure to develop first few time step models is detailed, providing a unified solution to the time varying identification problem. The practical problem of identifying the time varying generalized Markov parameters required for TVERA is presented as the next result. In the process, we generalize the classical time invariant input output AutoRegressive model with an eXogenous input (ARX) models to the time varying case and realize an asymptotically stable observer as a byproduct of the calculations. It is further found that the choice of the generalized time varying ARX model (GTV-ARX) can be set to realize a time varying dead beat observer. Methods to use the developed algorithm(s) in this research are then considered for application to the identification of system models that are bilinear in nature. The fact that bilinear plant models become linear for constant inputs is used in the development of an algorithm that generalizes the classical developments of Juang. An intercept problem is considered as a candidate for application of the time varying identification scheme, where departure motion dynamics model sequence is calculated about a nominal trajectory with suboptimal performance owing to the presence of unstructured perturbations. Control application is subsequently demonstrated. The dynamics of a particle in a rotating tube is considered next for identification using the time varying eigensystem realization algorithm. Continuous time bilinear system identification method is demonstrated using the particle example and the identification of an automobile brake model.

This thesis involves modeling, guidance, control, and flight simulations of a canard controlled guided missile. The autopilot is designed by a pole placement technique. Designed autopilot is used with the guidance systems considered in the thesis. Five different guidance methods are applied in the thesis, one of which is the famous proportional navigation guidance. The other four guidance methods are different fuzzy logic guidance systems designed considering different types of guidance inputs. Simulations are done against five different target types and the performances of the five guidance methods are compared and discussed.

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