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Time-delay is a widespread phenomenon of control system. Time-delay characteristics have a serious impact on system stability and performance. It is valuable to study iterative learning control of time-delay system. In this paper, we study the effects of multiple time delay on the convergence of nonlinear time-delay systems applying ??-norm and a set of inequalities. It will be shown that time-delay...
In this paper, a PD-type sampled-data iterative learning control algorithm is proposed for a class of nonlinear systems with time delays and uncertain disturbances, including random input disturbance and random output measurement noise. By introducing Taloy' s norm, a rigorous proof is given for the convergence at each sampling point. A sufficient condition is derived to ensure that the real output...
A framework is developed which enables a general class of linear iterative learning control (ILC) algorithms to be applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the need for intervening reference points to be stipulated. It is shown that superior convergence and robustness properties are obtained compared with those associated...
In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories which are described by a high-order internal model. The high-order internal model (HOIM) is formulated as a polynomial between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders...
A framework is developed which allows a general class of ILC algorithm to be applied to tasks which require the plant output to reach a given point in a set time. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original ILC law to track an arbitrary reference trajectory satisfying the end-point conditions. Experimental results...
In this paper, we presents a new class of iterative learning control law for a class of nonlinear systems, denoted PD-IPD, which is composed by adding a derivative term in the integrator of PID learning control law. By using Bellman-Gronwall lemma, the simple explicit conditions on the learning gains to ensure convergence are provided in the sense of Lambda norm. An attractive feature of PD-IPD learning...
A conditional learning control is derived for uncertain nonlinear systems to track repetitive trajectory under the alignment initial condition. The learning process is conditional as it is performed only when the system response is mainly determined by the input. The major advantage of this method over adaptive ILC is that it can handle non-parametric uncertainties. A simulation example is presented...
In this paper, interconnected iterative learning control loops are used to drive the output of general multi-input-multi-output nonlinear systems with general input uncertainties to the desired output. New convergence conditions are obtained to ensure the convergence of the overall system. A simulation example shows the effectiveness of the proposed method.
Motion control systems have found their application in various industry products. Common issues encountered when designing this type of systems are nonlinearities and uncertainties (e.g., unknown parameters, unmodeled dynamics, and disturbances). In this paper, we study controller design for a class of motion systems with rotary components. The systems are assumed to have unknown parameters which...
A new iterative learning control method is presented for the trajectory tracking control of linear time-variant systems. This new method does not need detailed knowledge of the controlled system. However, a reference batch is designed, in which some small change of the input trajectory in the current batch is applied to the controlled system, and then its output trajectory is obtained. The ratio of...
In this paper, three iterative learning control (ILC) schemes are developed in the multirate signal processing domain. One is pseudo-downsampled ILC, in which the input update rate is different from the sampling rate of feedback system. The second one is a two-mode ILC, in which the input update rates of ILC are different at low and high frequency bands. The third one is a cyclic pseudo-downsampled...
Learning systems are evolving into a significant method to allow physical systems to track a desired trajectory. Frequency domain analysis has contributed to a better understanding of the behavior of learning systems. This paper proposes a method which uses the errors from both the previous trial and the second trial back. This law yields better convergence and model error tolerance than single step...
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