An Observer-Based Adaptive Iterative Learning Control Using Filtered-FNN Design for Robotic Systems

Abstract

An observer-based adaptive iterative learning control using a filtered fuzzy neural network is proposed for repetitive tracking control of robotic systems. A state tracking error observer is introduced to design the iterative learning controller using only the measurement of joint position. We first derive an observation error model based on the state tracking error observer. Then, by introducing some auxiliary signals, the iterative learning controller is proposed based on the use of an averaging filter. The main control force consists of a filtered fuzzy neural network used to approximate for unknown system nonlinearity, a robust learning term used to compensate for uncertainty, and a stabilization term used to guarantee the boundedness of internal signals. The adaptive laws combining time domain and iteration domain adaptation are presented to ensure the convergence of learning error. We show that all the adjustable parameters as well as internal signals remain bounded for all iterations. The norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

An observer-based model reference adaptive iterative learning controller for MIMO nonlinear systems

“…If we apply the observerbased MRAILC (10), (11), (14), (16), (17), and (18) with adaptation laws (19) and (20), then we guarantee that e 1 ϕ (t), e 1 a (t), Θ 1 (t), ψ 1 (t) are bounded. …”

“…We will show that the nonlinear systems considered in [12], [13], [14], [15], [16] will become a special case of this work. In order to overcome the problem of state measurement, a simple Luenberger-like linear observer is firstly presented to estimate the state tracking error and provide the estimated state vector for controller design.…”

“…How to design an AILC for nonlinear systems using only output measurement is an interesting and challenge issue in this research field. In order to relax the requirement of state measurement, the AILC schemes using only output measurement [12], [13], [14], [15], [16] were presented in the past decade. Even though the AILC problem for nonlinear systems using only output measurement has been investigated in the above works, it is noted that the dynamics of nonlinear systems in these works need to satisfy some special structures or conditions.…”

“…We can find that the nonlinear systems in [12] have to be an output-feedback form which implies that f (X(t)) depends on output only and b(X(t)) = b is a constant vector. On the other hand, the nonlinear systems in [13], [16] are required to be the standard canonical form, i.e.,ẋ 1 (t) = x 2 (t),ẋ 2 (t) = x 3 (t), · · ·. In [14], the nonlinearity f (X(t)) should satisfy the properties of linear in parameter as well as input matching condition, and b(X(t)) must be a known constant matrix b.…”

An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor

“…Fuzzy neural networks (FNNs) incorporate the advantages of fuzzy inference and neurolearning [24][25][26][27][28]. FNNs can simulate the merits of human knowledge representation and thinking of fuzzy theory and associate the learning ability and computational power of NNs.…”