This paper presents a robust-adaptive nonlinear model predictive control (MPC) technique that leverages past experiences to achieve tractability on computationally constrained systems. We propose a robust extension of the Experiencedriven Predictive Control (EPC) algorithm via a Gaussian belief propagation strategy that computes an uncertainty set, bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance-constrained approach, thereby providing a probabilistic guarantee of constraint satisfaction. The parameterized form of the controllers produced by EPC coupled with online uncertainty estimates ensures that this robust constraint satisfaction property persists, even as the system switches controllers and experiences variations in the uncertainty model. We validate the online performance and robust constraint satisfaction of the proposed Robust EPC algorithm through a series of trials with a simulated ground robot and three experimental platforms: (1) a small quadrotor aerial robot executing aggressive maneuvers in wind with degraded state estimates, (2) a skid-steer ground robot equipped with a laser-based localization system, and (3) a hexarotor aerial robot equipped with a vision-based localization system.
We consider cooperative control of robots involving two different testbed systems in remote locations in different time zones, with communication on the internet. The goal is to have all robots properly follow a leader defined on one of the testbeds, while maintaining non-overlapping positions within each swarm and between swarms, assuming they are superimposed in the same virtual space. A dual-testbed design is developed involving real robots and remote network communication, performing a cooperative swarming algorithm based on a modified Morse Potential. Extensive experimental results were obtained with real internet communication and virtual testbeds running in each lab. The communication protocol was designed to minimize loss of packets, and average transfer delays are within tolerance limits for practical applications. We ran several experiments, with intentional packet loss, that illustrate the degradation of the results in the case of modest and severe packet loss. The novelty of this work is its experimental aspect involving long range network communication across a large distance via the internet. The work raises a series of interesting theoretical problems.
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