FEL and JIT approaches to tracking adaptive control based on the internal inverse models

49th IEEE Conference on Decision and Control (CDC)

Noguchi

2010

Much attention has been paid recently to controltheoretic research on what is called feedback error learning (FEL). This paper provides yet another FEL control scheme, which generalizes existing ones in two points: i) It can treat a plant which is the product of known and unknown transfer matrices, and thereby reduces the number of parameters to be learned. ii) The response model is less restrictive, due to a general two-degree-of-freedom structure. Numerical simulation illustrates the effectiveness of the proposed scheme.

Section: Introductionmentioning

confidence: 84%

Further results on feedback error learning control: Partially known plant and general response model

49th IEEE Conference on Decision and Control (CDC)

Noguchi

2010

“…JIT approach by Ushida et al [6] suggests that feedforward learning control may be generalized to a nonlinear plant by suitably changing the learning mechanism formulated above. In this direction, the authors have proposed S-LWR learning scheme [7], where it was assumed that every linear approximation is free of finite zeros.…”

Section: Motivation For Nonlinearitymentioning

confidence: 99%

“…Ushida et al [6] proposed yet another FF learning control scheme based on Just-In-Time (JIT) modeling, which was named after a famous manufacturing technique. While existing FEL schemes [2][3][4][5] use a single (but adjustable) model for inverse dynamics, in the JIT approach we prepare a big database consisting of all input-output (i/o) signal behaviors of the plant.…”

Section: Introductionmentioning

confidence: 99%

Feedforward learning control for SISO plant with finite zeros and nonlinearity

Proceedings of the 33rd Chinese Control Conference

Matsumoto

2014

This paper proposes a scheme for feedforward (FF) learning control based on scheduled locally weighted regression (S-LWR). For an unknown nonlinear single-input single-output (SISO) plant, it generates FF control signals based on local models of inverse dynamics identified by on-line S-LWR learning at a current operating point (called scheduling parameter). This scheme was proposed previously by the authors under the assumption that every linear approximation of the plant is free of finite zeros; i.e., the numerator of its transfer function is constant. The objective of this paper is to relax this restriction by providing adjustable FF controller poles to cancel the plant zeros. Numerical simulation is carried out to verify effectiveness of the propose scheme.

“…In these works, the plant is assumed to be unknown but linear time-invariant. The learning law has been further generalized to nonlinear or time-variant plant via locally weighted regression with the linear filter [3], [9]. Experimental validation of linear filter FEL has also been reported; e.g., [5].…”

Section: Introductionmentioning

confidence: 98%

Feedforward control with on-line tuning: A perspective on two-degree-of-freedom structure

Proceedings of the 2014 International Conference on Advanced Mechatronic Systems

Ito

Mateo

2014

The two-degree-of-freedom structure allows us to design independently feedforward and feedback controls for response shaping and stabilization, respectively. Feedforward control with on-line tuning is particularly promising for improvement of response, so that various schemes have been proposed in literature. This paper proposes yet another scheme along this direction with focus on the control structure as well as online tuning. The proposed scheme avoids transient instability by tuning both prefilter and feedforward controllers, which are linear in parameter. Numerical simulation is carried out to verify the effectiveness of the scheme.