Many landing sites of scientific interest on Mars including most of the Southern Hemisphere at elevations above 2km Mars Orbiter Laser Altimeter reference are inaccessible due to current limitations in precision entry guidance and payload deceleration. Precision guidance and large payload deceleration is challenging due to the thin Martian atmosphere, large changes in free stream conditions during entry, and aerothermal and aerodynamic instability concerns associated with control systems with direct external flow field interaction. Such risks have descoped past Mars missions to unguided entry with the exception of Mars Science Laboratory's (MSL) bank angle guidance. Consequently, prior to MSL landing ellipses were on the order of 100's of km. MSL has approached the upper limit of payload deceleration capability for rigid, blunt body sphere cone aeroshells used on all successful Mars entry missions. Hypersonic Inflatable Aerodynamic Decelerators (HIADS) are in development for larger payload deceleration capability through inflated aeroshell diameters greater than rigid aeroshells constrained by the launch rocket diameter, but to date there has been limited dynamics, control, and guidance development for their use on future missions. This dissertation develops internal moving mass actuator (IMMA) control systems for improving Mars precision entry guidance of rigid capsules and demonstrating precision guidance capability for HIADs. IMMAs provide vehicle control moments without direct interaction with the external flow field and can increase payload mass delivered through reducing propellant mass for control and using portions of the payload for the IMMAs. Dynamics models for entry vehicles with rotation and translation IMMAs are developed. IMMA control systems using the models are developed for two NASA vehicle types: a 2.65 m, 602 kg Mars Phoenix-sized entry capsule and an 8.3 m, 5.9 metric ton HIAD approaching payload requirements for robotic precursor missions for future human missions. Linear Quadratic controllers with integral action for guidance command tracking are developed for translation and rotation IMMA configurations. Angle of attack and sideslip guidance laws are developed as an alternative to bank angle guidance for decoupling range and cross-range control for improved precision entry guidance. A new variant of the Apollo Earth return terminal guidance algorithm is implemented to provide the closed-loop angle of attack range control commands. Nonlinear simulations of the entire 8 degree of freedom closed-loop systems demonstrate precision guidance to nominal trajectories and final targets for off-nominal initial entry conditions for flight path angle, range, cross-range, speed and attitude. Mechanical power studies for IMMA motion show rotation IMMA require less total mechanical power than translation actuators, but both systems have low nominal mechanical power requirements (below 100 Watts). Precision guidance for both systems to terminal targets greater than 38 km down-range from an open-loop ballistic entry is shown for low mechanical power, low CM displacement, (< 4.5 in) and at low internal velocities (< 2 in/s) over significant dynamic pressure changes. The collective precision guidance results and low mechanical power requirements show IMMA based entry guidance control systems constitute a promising alternative to thruster based control systems for future Mars landers. / Ph. D.

<p> This work focuses on solving the general optimal control problems with smart-learning-enabled and theory-supported optimal control (SET-OC) approaches. The proposed SET-OC includes two main directions. Firstly, according to the basic idea of the direct method, the smart-learning-enabled iterative optimization algorithm (SEIOA) is proposed for solving discrete optimal control problems. Via discretization and reformulation, the optimal control problem is converted into a general quadratically constrained quadratic programming (QCQP) problem. Then, the SEIOA is applied to solving QCQPs. To be specific, first, a structure-exploiting decomposition scheme is introduced to reduce the complexity of the original problem. Next, an iterative search, combined with an intersection-cutting plane, is developed to achieve global convergence. Furthermore, considering the implicit relationship between the algorithmic parameters and the convergence rate of the iterative search, deep learning is applied to design the algorithmic parameters from an appropriate amount of training data to improve convergence property. To demonstrate the effectiveness and improved computational performance of the proposed SEIOA, the developed algorithms have been implemented in extensive real-world application problems, including unmanned aerial vehicle path planning problems and general QCQP problems. According to the theoretical analysis of global convergence and the simulation results, the efficiency, robustness, and improved convergence rate of the optimization framework compared to the state-of-the-art optimization methods for solving general QCQP problems are analyzed and verified. Secondly, the onboard learning-based optimal control method (L-OCM) is proposed to solve the optimal control problems. Supported by the optimal control theory, the necessary conditions of optimality for optimal control of the optimal control problem can be derived, which leads to two two-point-boundary-value-problems (TPBVPs). Then, critical parameters are identified to approximate the complete solutions of the TPBVPs. To find the implicit relationship between the initial states and these critical parameters, deep neural networks are constructed to learn the values of these critical parameters in real-time with training data obtained from the offline solutions.  To demonstrate the effectiveness and improved computational performance of the proposed L-OCM approaches, the developed algorithms have been implemented in extensive real-world application problems, including two-dimensional human-Mars entry, powered-descent, landing guidance problems, and fuel-optimal powered descent guidance (PDG) problems. In addition, considering there is no thorough analysis of the properties of the optimal control profile for PDG when considering the state constraints, a rigid theoretical analysis of the fuel-optimal PDG problem with state constraints is further provided. According to the theoretical analysis and simulation results, the optimality, robustness, and real-time performance of the proposed L-OCM are analyzed and verified, which indicates the potential for onboard implementation. </p>