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Terminal iterative learning control (TILC) has been developed to track a single desired point at the terminal end of operation interval over iterations. In this paper, the feedback control knowledge of previous time instants is utilized via an equivalent dynamical predictive model to update the input signals for the TILC problem. The proposed scheme consists of a control input updating law with feedback...
This paper presents a new data-driven optimal terminal iterative learning control (TILC) using time-varying control input signals to enhance control performance. The iterative learning control input is updated using the terminal output in previous runs, together with the control input information in previous runs and previous time instants of the current run, without the need of any reference trajectory...
A new periodic least-squares estimator with nonlinear data weighting is developed and used to design a periodic adaptive control for a simple discrete-time nonlinear system in the presence of time-varying parametric uncertainties. A global stability result is obtained through Lyapunov analysis without assuming any growth conditions on the nonlinearities. Simulation results further confirm the effectiveness...
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