초록 ▼

A method for linearization of feedback in neural networks, and a neural network incorporating the feedback linearization method are presented. Control action is used to achieve tracking performance for a state-feedback linearizable, but unknown nonlinear control system. The control signal comprises

A method for linearization of feedback in neural networks, and a neural network incorporating the feedback linearization method are presented. Control action is used to achieve tracking performance for a state-feedback linearizable, but unknown nonlinear control system. The control signal comprises a feedback linearization portion provided by neural networks, plus a robustifying portion that keep the control magnitude bounded. Proofs are provided to show that all of the signals in the closed-loop system are semi-globally uniformly ultimately bounded. This eliminates an off-line learning phase, and simplifies the initialization of neural network weights.

대표청구항 ▼

[ What is claimed is:] [1.] A method of adaptively controlling a plant having at least one measurable state and at least first and second unknown functions of said measurable state, comprising:sensing said at least one measurable state;comparing said sensed state with a desired state in a first feed

[ What is claimed is:] [1.] A method of adaptively controlling a plant having at least one measurable state and at least first and second unknown functions of said measurable state, comprising:sensing said at least one measurable state;comparing said sensed state with a desired state in a first feedback loop to produce an error signal;calculating, as a function of said sensed state, a first unknown function estimate using a first multi-layer neural network process in a second feedback loop, said first multi-layer neural network process having multiple layers of neurons with tunable weights;calculating, as a function of said sensed state, a second unknown function estimate using a second multi-layer neural network process in a third feedback loop, said second multi-layer neural network process having multiple layers of neurons with tunable weights;calculating a smooth control action as a function of said error signal, and as a function of said first and second unknown function estimates;applying said smooth control action to said plant to maintain said at least one measurable state at said desired state; andadaptively adjusting said tunable weights of said first and second multi-layer neural network processes as a function of said error signal.