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Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging. In this paper, we consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. Leveraging this model, we develop a receding-horizon control-based decision-making strategy, that estimates online the other drivers' intentions using Bayesian filtering and incorporates predictions of nearby vehicles' behaviors under uncertain intentions. The effectiveness of the proposed decision-making strategy is demonstrated and evaluated based on simulation studies in comparison with a game theoretic controller and a real-world traffic dataset. 

Stabilization of a linear system under control constraints is approached by combining the classical variation of parameters method for solving ODEs and a straightforward construction of a feedback law for the variational system based on a quadratic Lyapunov function. Sufficient conditions for global closed-loop stability under control constraints with zero in the interior and zero on the boundary of the control set are derived, and several examples are reported. The extension of the method to nonlinear systems with control constraints is described. 

The problem of transforming a locally asymptotically stabilizing time-varying control law to a globally stabilizing one with accelerated finite/fixed-time convergence is studied. The solution is based on an extension of the theory of homogeneous systems to the setting where the symmetry and stability properties only hold with respect to a part of the state variables. The proposed control design advances the kind of approaches first studied in [1], and relies on the implicit Lyapunov function framework. Examples of finite-time and nearly fixed-time stabilization of a nonholonomic integrator are reported. 

This paper presents two machine learning-based constraint management approaches based on Reference Governors (RGs). The first approach, termed NN-DTC, uses regression neural networks to approximate the distance to constraints. The second, termed NN-NL-RG, uses regression neural networks to approximate the input-output map of a nonlinear RG. Both approaches are shown to enforce constraints for a nonlinear second order system. NN-NL-RG requires a smaller dataset size as compared to NN-DTC for well-trained neural networks. For systems with multiple constraints, NN-NL-RG is also more computationally efficient than NN-DTC. Finally, promising results are reported by having both approaches implemented on a more complex spacecraft proximity maneuvering and docking application, through simulations. 

A periodic model predictive control (MPC) scheme is proposed for tracking halo orbits. The problem is formulated and solved in the elliptic restricted three-body problem (ER3BP) setting. The reference trajectory to be tracked is designed by using eccentricity continuation techniques. The MPC design exploits the periodicity of the tracking model and guarantees exponential stability of the linearized closed-loop system, through a suitable choice of the terminal set and weight matrices. A sum-of-norms cost function is adopted to promote fuel saving. The proposed control scheme is validated on two simulated missions in the Earth–Moon system, which, respectively, involve station keeping on a halo orbit near the L1 Lagrange point and rendezvous to a halo orbit near the L2 Lagrange point. Results illustrate the advantage of designing the reference trajectory and the periodic control directly in the ER3BP setting versus approximate solutions based on the circular restricted three-body problem (CR3BP).