A new model predictive convex programming is proposed in this paper for state and input constrained vehicle guidance design. The proposed method defines a convex optimization framework considering a flexibly designed cost function subject to inequality constraints and a sensitivity relation between state increments and input corrections. This formulated convex optimization problem can be solved in a computationally efficient manner. Simulation studies of nonlinear missile and aircraft landing guidance problems demonstrate the effectiveness of the proposed approach.

With the emergence of unmanned aerial vehicles in recent time, the research on trajectory and path following control methodologies became increasingly popular due to various applications for these test platforms. In this context, this paper presents a trajectory / path following controller, which is derived based on nonlinear dynamic inversion of nonlinear error dynamics between a specified trajectory / path and the aircraft. The controller is designed as a modular part of an integrated auto flight system comprising the handling of system automation as well as waypoint lists, trajectory generation, standard autopilot functionality, and inner loop control. The modular structure introduced in this paper is emphasized by explanations of the generic interfaces and remarks towards testability. An illustrative example shows the efficacy of this trajectory / path following control concept.

In this paper an adaptive fault tolerant control allocation for a linear static actuator model is presented. The method proposed here has the advantage of not relying on the exact knowledge of the fault and of avoiding the use of online optimization routines. This is done using adaptive control techniques and by reformulating the problem of the control allocation with unknown control effectiveness. Furthermore, the usual assumption of positive definiteness of the control effectiveness matrix is relaxed. The approach is applied to the position tracking problem of a hexacopter system. The control law is composed of a backstepping baseline controller and an adaptive augmentation. The goal of this extension is improving the robustness against degradation and failures of the propulsion system. Simulation results show the efficiency of the approach.

The descent longitudinal trajectory control methodology of a hypersonic glider to carry out a pullup maneuver is presented. A dynamic pole placement controller is implemented as the baseline controller. The baseline controller is augmented with an ${\mathcal{L}}_1$ adaptive controller to cancel out the matched and unmatched uncertainties. The differences between the pole placement and the ${\mathcal{L}}_1$ augmented pole placement controller are presented in terms of performance with the help of Monte Carlo simulations. The dynamic pole placement controller is shown to have robustness in the presence of time-invariant and time-varying errors in the aerodynamic coefficients, control surface, and gravimetric uncertainties. The augmented controller improves the performance of the baseline controller in the presence of these uncertainties. This is concluded with a reduction in the tracking error norm ${\Vert e_p\Vert }_{{\mathcal{L}}_2}$ and the control surface norm, which is the energy of the deviation between the reference and real control signal; $M_{u_b}>M_{u_{{\mathcal{L}}_1}}$. The use of the adaptive controller, however, reduces the time delay margin of the system. The contribution of this paper lies in the application and determination of the performance of a piecewise constant ${\mathcal{L}}_1$ adaptive augmentation setup to a nonminimum phase linear time-varying state feedback system.