We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to minimize the number of impulses. Our architecture recognizes that traditional ETC is a greedy strategy towards optimizing average inter-event times and introduces the idea of a deadline policy for the optimization of long-term discounted inter-event times. A lower layer is designed employing event-triggered control to guarantee the satisfaction of control objectives, while a higher layer implements a deadline policy designed with reinforcement learning to improve the discounted inter-event time. We apply this scheme to the control of an orbiting spacecraft, showing superior performance in terms of actuation frequency reduction with respect to a standard (one-layer) ETC while maintaining safety guarantees.@en

Humans are able to negotiate downstep behaviors-both planned and unplanned-with remarkable agility and ease. The goal of this paper is to systematically study the translation of this human behavior to bipedal walking robots, even if the morphology is inherently different. Concretely, we begin with human data wherein planned and unplanned downsteps are taken. We analyze this data from the perspective of reduced-order modelling of the human, encoding the center of mass (CoM) kinematics and contact forces, which allows for the translation of these behaviors into the corresponding reduced-order model of a bipedal robot. We embed the resulting behaviors into the full-order dynamics of a bipedal robot via nonlinear optimization-based controllers. The end result is the demonstration of planned and unplanned downsteps in simulation on an underactuated walking robot.@en

This chapter reviews different methods for the control of legged locomotion with a special focus on bipedal locomotion. All locomotion systems are governed by complex nonlinear, hybrid dynamics, and are redundant, underactuated and often unstable, which makes their control a very challenging task.The chapter starts with a presentation of different concepts of stability and robustness of locomotion considering nominal walking situations as well as the reaction to larger external perturbations. Then, optimal control is discussed as a guiding principle of human and robot motion, and dynamic multibody system models as well as different optimization problem formulations for the generation, control and analysis of locomotion are shown. Constant or variable compliance plays an important role in biological and bio-inspired locomotion, but needs to be properly adapted in the design and control process which also can be addressed by optimal control. Next, impedance control in locomotion is discussed, looking at passive and active impedance and different approaches to emulated appropriate impedances for robots. The chapter also reviews control approaches for legged locomotion based on template models, i.e. very simple representations of the original locomotor system, with a focus using template models for the design of suitable controllers. The state of the art of passive dynamic walking robots as well as powered and almost passive dynamic robots is summarized and their achievements in terms of energy-efficiency, stability, robustness and versatility re discussed. Hybrid zero dynamics is presented as a control synthesis framework that reduces the complexity of whole-body dynamics control and allows to develop efficient controllers for dynamic walking and running motions. Finally, a control approach for locomotion based on the concept of central pattern generators is presented which helps to control locomotion of legged robots and gives insight into human movement control.

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