Abstract-This paper presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-topoint motion problems. The key contribution of this paper is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating timeconsuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to (i) achieve tracking accuracy up to the repeatability of the test setup, (ii) minimize the rms value of the tracking error while respecting the actuator input constraints, (iii) learn energyoptimal system inputs for point-to-point motions.
Iterative learning control (ILC) is an open-loop control strategy that learns the system input to track a desired trajectory from previous executions. A major limitation of ILC is that for every new trajectory, the ILC is reinitiated and thus takes a number of iterations to learn the new optimal system input. This paper presents a novel methodology for linear time-invariant systems to calculate a better initialization of an ILC based on a previously learned similar trajectory and a disturbance model. To illustrate the potential of the developed method, it is applied to a permanent magnet linear motor and compared to a model-based feedforward control scheme. The experimental results show that the proposed method outperforms the model-based feedforward control scheme in the case of similar motion trajectories, yielding a better initialization of an ILC.
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