Iterative learning control in feedback systems
Automatica (Journal of IFAC)
Artificial Intelligence Review - Special issue on lazy learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Control Theory of Nonlinear Mechanical Systems
Control Theory of Nonlinear Mechanical Systems
Linear and Nonlinear Iterative Learning Control (Lecture Notes in Control and Information Sciences, 291)
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Brief Incorporation of experience in iterative learning controllers using locally weighted learning
Automatica (Journal of IFAC)
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Iterative learning control (ILC) is a new domain in control system that motivates, whether mechanical robots can learn a prescribed ideal motion by themselves using information represented by the measured data gathered in the previous practice. For each new desired trajectory task, the conventional ILC methods have to start its learning with zero initial input assumption. Instead of such zero initial input assumption, in this paper, the idea of using the past trajectory tracking experiences on the initial input selection for tracking new trajectory-tracking tasks have been highlighted. Certain methods of experience inclusion in iterative learning controllers (ILC) are proposed. Approximate fuzzy data model (AFDM) and type-1 fuzzy logic system (FLS) techniques have been adopted for the process of initial input selection. Performance of the proposed fuzzy rule based model-based ILC approaches on initial error reduction and in error convergence issues are proved. Numerical experimentation on a two-link manipulator model with the inclusion of actuator dynamics verifies the performance of the proposed fuzzy model-based ILC approaches. Comparison with existing local learning technique on the selection of initial input for ILC algorithm proves the efficacy of the proposed AFDM and type-1 FLS-based methods.