Trajectory tracking controller for nonlinear systems with disturbances using iterative learning algorithm without resetting condition

In this paper, a new high order P-type iterative learning control scheme is presented to solve the trajectory tracking problem of Multi Input Multi Output (MIMO) nonlinear systems with unknown input saturation. It is well known that most systems can be affected by input uncertainties such as saturation. This undesirable input has the potential to destabilize the system. Thus, it is important to mitigate the effect of saturation. In this paper, we take this problem into account. In addition, the controller scheme is very simple, in which the control input in each trial is adjusted by using the tracking error signals obtained from previous and current trials. As the iterations continue, the control system eventually learns the task and follows the desired trajectory with little or no errors. The asymptotic stability of the closed loop system under unknown input saturation is guaranteed over the whole finite time by using the λ-norm method. Finally, to illustrate the effectiveness of the proposed method, simulation results are presented.

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“…Applying ( 8) and ( 9), we obtain (10) Adding and subtracting f (x k ) + B(t)(sat(u k , u * ) + P1e k + P2e k+1 ), we get…”

Section: Introductionmentioning

confidence: 99%

“…The second approach is the λ-norm. This method has been defined and used in the first publication of the ILC technique [9], and many works employ this method, see for example [10], [11], [12], [13]. In our work, we use λ-norm to prove the asymptotic stability of the closed loop system.…”

mentioning

confidence: 99%