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.