We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by model uncertainty on active passive degrees freedom. The generic iteration algorithm makes use learned data in both phase, based optimization, control where partial feedback linearization dofs...

This paper considers the problem of distributed motion- and task-planning multi-agent multi-agent-object systems under temporal-logic-based tasks uncertain dynamics. We focus on manipulator-endowed robotic agents that can interact with their surroundings. present first continuous control algorithms for navigation cooperative object manipulation exhibit following properties. First, they are in s...

Recently developed, second-order sliding-mode control (2-SMC) algorithms are analyzed to assess their global convergence properties. While standard first-order sliding-mode control (1-SMC) algorithms derive their effectiveness from the global solution of the well known “reaching condition” _ ( = 0 being the actual sliding manifold), 2-SMC is based on more complex differential inequalities, for ...

In surveillance and monitoring systems with multiple sensors at spatially distributed locations, the locations of the sensors crucially affect the information-content of the sensor readings and thus the quality of the estimates obtained. This paper deals with the problem of choosing the sensor locations for estimating processes with nonlinear and uncertain dynamics that can be represented as Ma...

This paper describes an approach to the problem of optimal motion planning with uncertain dynamics. This problem can occur whenever a vehicle suffers damage or when the environment makes the effect of motion actions unpredictable and potentially risky. We address in particular the case of aircraft with structural and/or control surface damage. The goal in this problem is to find an optimal plan...

We present an algorithm which learns an online trajectory generator that can generalize over varying and uncertain dynamics. When the dynamics is certain, the algorithm generalizes across model parameters. When the dynamics is partially observable, the algorithm generalizes across different observations. To do this, we employ recent advances in supervised imitation learning to learn a trajector...

This technical memorandum describes a nonlinear guidance law for a single-missile single-target engagement. The guidance relies on the concepts of Lyapunov stability and backstepping, which are constructive methods in nonlinear control theory. The design of the guidance law allows taking into account the nonlinear relative kinematics between the missile and the target, and ensuring ultimate bou...