Backlash compensation using neural network
First Claim
1. An adaptive neural network compensator for compensating backlash of a mechanical system, comprising:
- a feedforward path;
a proportional derivative tracking loop comprising a proportional derivative path in the feedforward path;
a filter in the feedforward path, the filter configured to take a filtered derivative of a tracking error signal of the compensator to determine a filtered tracking error signal; and
a neural network in the feedforward path and coupled to the filter, the neural network configured to compensate the backlash by estimating an inverse of the backlash and applying the inverse to an input of the mechanical system.
2 Assignments
0 Petitions
Accused Products
Abstract
Methods and systems for backlash compensation. Restrictive assumptions on the backlash nonlinearity (e.g. the same slopes of the lines, etc.) are not required. The compensator scheme has dynamic inversion structure, with a neural network in the feedforward path that approximates the backlash inversion error plus filter dynamics needed for backstepping design. The neural network controller does not require preliminary off-line training. Neural network tuning is based on a modified Hebbian tuning law, which requires less computation than backpropagation. The backstepping controller uses a practical filtered derivative, unlike the usual differentiation required by earlier backstepping routines. Rigorous stability proofs are given using Lyapunov theory. Simulation results show that the proposed compensation scheme is an efficient way of improving the tracking performance of a vast array of nonlinear systems with backlash.
37 Citations
20 Claims
-
1. An adaptive neural network compensator for compensating backlash of a mechanical system, comprising:
-
a feedforward path;
a proportional derivative tracking loop comprising a proportional derivative path in the feedforward path;
a filter in the feedforward path, the filter configured to take a filtered derivative of a tracking error signal of the compensator to determine a filtered tracking error signal; and
a neural network in the feedforward path and coupled to the filter, the neural network configured to compensate the backlash by estimating an inverse of the backlash and applying the inverse to an input of the mechanical system. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
3. The compensator of claim 1, wherein the filtered tracking error signal is uniformly ultimately bounded.
-
4. The compensator of claim 1, wherein weight estimates of the neural network are uniformly ultimately bounded.
-
5. The compensator of claim 1, wherein the filtered tracking error signal and weight estimates of the neural network are uniformly ultimately bounded.
-
6. The compensator of claim 1, wherein the proportional derivative tracking loop is configured to provide stable feedback control of the mechanical system while weights of the neural network are adjusted from initialization values.
-
7. The compensator of claim 1, wherein the mechanical system comprises an actuator or robot.
-
8. An adaptive neural network compensator for compensating backlash of a mechanical system, comprising:
-
a filter in a feedforward path, the filter configured to determine a filtered tracking error signal;
a neural network in the feedforward path, the neural network configured to compensate the backlash by estimating an inverse of the backlash and applying the inverse to an input of the mechanical system; and
means for tuning the neural network so that the filtered tracking error signal of the compensator is uniformly ultimately bounded. - View Dependent Claims (9, 10)
-
-
11. An adaptive neural network compensator for compensating backlash of a mechanical system, comprising:
-
a feedforward path;
a proportional derivation tracking loop comprising a proportional derivative path in the feedforward path;
a neural network in the feedforward path; and
a nonlinear estimate feedback loop coupled to the feedforward path. - View Dependent Claims (12, 13)
-
-
14. A method of adaptively compensating backlash in a mechanical system, comprising:
-
estimating an inverse of the backlash using a neural network in a feedforward path;
taking a filtered derivative of a tracking error signal of the compensator using a filter in the feedforward path to form a filtered tracking error signal;
adjusting weights of the neural network as a function of the filtered tracking error signal using a Hebbian tuning algorithm to achieve closed loop stability; and
applying the inverse to an input of the mechanical system to compensate the backlash. - View Dependent Claims (15, 16, 17, 18, 19, 20)
-
Specification