Abstract: A method to supervise a local dynamical system having multiple preset control objectives and operating in conjunction with other dynamical systems. The method includes receiving state input from dynamical systems in an environment at a distributed soft computing level, generating weights and applying the weights to the preset control objectives using soft computing methods to form weighted control objectives. The weights are computed based on the received state input. The method also includes generating a command signal for the local dynamical system based on the weighted control objectives and transmitting the command signal to a controller in the local dynamical system.

Abstract: A method to supervise a local dynamical system having multiple preset control objectives and operating in conjunction with other dynamical systems. The method includes receiving state input from dynamical systems in an environment at a distributed soft computing level, generating weights and applying the weights to the preset control objectives using soft computing methods to form weighted control objectives. The weights are computed based on the received state input. The method also includes generating a command signal for the local dynamical system based on the weighted control objectives and transmitting the command signal to a controller in the local dynamical system.

Abstract: A system for a variable configuration CO2 removal within an air recovery framework. The system may include description and model development. There may be time modeling that incorporates inter-mode switching time and intra-mode dynamics time. The intra-mode dynamics time may have a mode time interval divided into finite elements and the finite elements may each have collocation points. There may be nonlinear model predictive control with objective function development and tuning. Statistical verification of controller safety performance may be included.