Adaptive nonlinear model predictive control using a neural network and input sampling
AssigneeThe Florida State University Research Foundation, Inc.
InventorsEmmanuel Collins, Brandon Reese, Damion Dunlap
Life of this patent
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Abstract
A novel method for adaptive Nonlinear Model Predictive Control (NMPC) of multiple input, multiple output (MIMO) systems, called Sampling Based Model Predictive Control (SBMPC) that has the ability to enforce hard constraints on the system inputs and states. However, unlike other NMPC methods, it does not rely on linearizing the system or gradient based optimization. Instead, it discretizes the input space to the model via pseudo-random sampling and feeds the sampled inputs through the nonlinear plant, hence producing a graph for which an optimal path can be found using an efficient graph search method.
Claims20
A method for adaptive nonlinear model predictive control of multiple input, multiple output systems, comprising:
- generating a plurality of inputs, each input further comprising an input state, the plurality of inputs and input states collectively comprising an input space;
- imposing one or more hard constraints on the inputs and the input states;
- executing a function operative to discretize the input space and generating a first set of sampled inputs;
- implementing a nonlinear model and generating one or more outputs based on the sampled inputs;
- executing a graph generating function and generating a graph of the sampled inputs and the outputs;
- and
- executing an optimizing function and determining an optimal path for the graph.
A method for adaptive nonlinear model predictive control of multiple input, multiple output systems, comprising:
- generating a plurality of inputs, each input further comprising an input state, the plurality of inputs and input states collectively comprising an input space;
- imposing one or more hard constraints on the inputs and the input states;
- executing a pseudo-random sampling function to discretize the input space and generating a first set of sampled inputs;
- implementing a nonlinear model and generating one or more outputs based on the sampled inputs;
- executing a graph generating function and generating a graph of the sampled inputs and the outputs;
- and
- executing an optimizing function and determining an optimal path for the graph.
A non-transitory computer readable medium containing computer program instructions, which when executed by one or more processors causes a device to:
- generating a plurality of inputs, each input further comprising an input state, the plurality of inputs and input states collectively comprising an input space;
- imposing one or more hard constraints on the inputs and the input states;
- executing a pseudo-random sampling function to discretize the input space and generating a first set of sampled inputs;
- implementing a nonlinear model and generating one or more outputs based on the sampled inputs;
- executing a graph generating function and generating a graph of the sampled inputs and the outputs;
- and
- executing an optimizing function and determining an optimal path for the graph.
Dependencies are inferred from the claim text.