Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection

Abstract

This article proposes an effective iterative learning control (ILC) approach based on successive projection scheme for repetitive systems with randomly varying trial lengths. A modified ILC problem is formulated to extend the classical ILC task description to incorporate a randomly varying trial length, while its design objective considers the mathematical expectation of its tracking error to evaluate the task performance. To solve this problem, this article employs the successive projection framework to give an iterative input signal update law by defining the corresponding convex sets based on the design requirements. This update law further yields an ILC algorithm, whose convergence properties are proved to be held under mild conditions. In addition, the input signal constraint can be embedded into the design without violating the convergence properties to obtain an alternative algorithm. The performance of the proposed algorithms is verified using a numerical model to show the effectiveness at occasions with and without input constraints.

Optimal iterative learning control under varying iteration lengths with input saturation

“…Meanwhile, the perfect tracking could be achieved by the proposed control scheme via increasing the iteration times. According to Figures 4 and 5, the tracking performance induced by the proposed algorithm at the fifth iteration is better than that produced by Reference 31. Figure 6 further illustrates theirs comparison in terms of convergence speed and convergence error.…”

Orbit-Injection Strategy and Trajectory-Planning Method of the Launch Vehicle under Power Failure Conditions

“…The ATU-IGM is a trajectory online planning method specially design target degradation based on the IGM. The idea of the adaptive target upd (ATU) is similar to the iterative learning control (ILC), that is, using the p information to modify the system input to achieve more accurate control of [21]. The ATU-IGM obtains the baseline mission target parameters by the TD generation or the RBFNN at online planning; meanwhile, the ATU-IGM d spatial position of the target orbit ( , ,…”

“…The ATU-IGM is a trajectory online planning method specially designed for mission target degradation based on the IGM. The idea of the adaptive target updating strategy (ATU) is similar to the iterative learning control (ILC), that is, using the previous result information to modify the system input to achieve more accurate control of the target task [21]. The ATU-IGM obtains the baseline mission target parameters by the TDOS at sample generation or the RBFNN at online planning; meanwhile, the ATU-IGM determines the spatial position of the target orbit (i f , Ω f , φ f ) using the flight state at failure point and failure parameters; then, TAU-IGM adaptively adjusts the launch vehicle's injection position in orbit according to the predicted terminal position deviation.…”

Joint two‐stage multi‐innovation recursive least squares parameter and fractional‐order estimation algorithm for the fractional‐order input nonlinear output‐error autoregressive model

“…Replacing 𝝋 s (t, 𝛼) and 𝜽 l in (10) and (20) with their estimates φs (t, 𝛼) and θl (t − 1) gives the estimates of x(t):…”