Iteration-varying Reference Trajectory Research Articles

Adaptive Iterative Learning Trajectory Tracking Control for Spraying Manipulator With Arbitrary Initial States and Iteration-varying Reference Trajectory

Adaptive Iterative Learning Trajectory Tracking Control for Spraying Manipulator With Arbitrary Initial States and Iteration-varying Reference Trajectory

Quantized iterative learning control for singular nonlinear fractional-order time-delay multi-agent systems with iteration-varying reference trajectories and switching topologies

This paper addresses the consensus tracking of the leader-following singular nonlinear fractional-order system with state time-delay in face of iteration-varying communication topologies and reference trajectories via an iterative learning control scheme. Firstly, by means of the neighbor information, the closed-loop Dα-type quantized iterative learning protocol is constructed based on output tracking errors. Secondly, sufficient conditions of consensus errors between each follower agent and leader agent are given in a fixed time interval and convergence analysis is also presented under the fixed topology and reference trajectory. Thirdly, the extension to iteration-varying reference trajectory tracking and switching topologies cases are investigated. It is shown that the developed protocol can also effectively work along the iteration axis on a finite time interval, although the communication topologies and reference trajectories vary dynamically with respect to iteration. Finally, numerical examples are shown to verify the effectiveness of the obtained results.

Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Pneumatic artificial muscle actuators possess great potential in compliant rehabilitation devices since they are flexible and lightweight. The inherent high nonlinearities, uncertainties, hysteresis and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. The angle tracking problem based on iterative learning control technology is considered in this work. This research proposes a new initial-rectification adaptive iterative learning control scheme for a pneumatic artificial muscle-actuated device with nonzero initial errors and iteration-varying reference trajectories. A barrier Lyapunov function is used to deal with the constraint requirement. A new initial rectification construction method is given to solve the nonzero initial error problem. Nonparametric uncertainties in the system are approximated by using a neural network, whose optimal weight is estimated by using difference learning method. As the iteration number increases, the system states of angle and angular velocity can accurately track the reference trajectories over the whole interval, respectively. In the end, the simulation results show excellent trajectory tracking performance of the iterative learning controller even if the reference trajectories are non-repetitive over the iteration domain.

Time-Varying Boundary Layer Based Iterative Learning Control for Nonlinearly Parametric Time-Delay Systems With Arbitrary Initial Errors and Iteration-Varying Reference Trajectories

In this paper, the tracking problem for a class of nonlinearly parametric time-delay systems with arbitrary initial state errors and iteration-varying reference trajectories is studied. An adaptive iterative learning control scheme is developed by using Lyapunov approach. A time-varying boundary layer is constructed for removing the zero initial error condition, which is necessary in normal iterative learning control algorithms. The time-varying nonlinearly parametric uncertainties and time-delay uncertainties are well compensated by using parameter separation technique, signal replacement mechanism and robust strategy. As the iteration number increases, the state tracking error asymptotically converges to a tunable residual set. In the end, a simulation example further verifies the theoretical results.

Robust Learning Control for Robot Manipulators With Random Initial Errors and Iteration-Varying Reference Trajectories

In this paper, we propose an error-tracking iterative learning control scheme to tackle the position tracking problem for robot manipulators with random initial errors and iteration-varying reference trajectories. Different from general usual ones, the control strategy in our work is to drive system errors perfectly track the desired error trajectories over the whole time interval as the iteration number increases, by which, the position trajectory and velocity trajectory can respectively track their reference trajectories during the predefined part operation interval. For fulfilling the control design, a new construction method of desired error trajectories is presented to remove the perfect initial resetting condition, which must be satisfied in most traditional iterative learning control algorithms. The uncertainties and disturbances in the robotic system dynamics are compensated by the robust approach and iterative learning approach, combinedly.

A Survey on High-Order Internal Model Based Iterative Learning Control

Iterative learning control (ILC) has been developed for decades and is mainly used to solve the repetitive control tasks. However, in the actual operation of systems, there are many non-strictly repetitive or iteration-varying factors, such as the iteration-varying reference trajectory, non-repetitive system parameters, iteration-related initial states, iteration-dependent input and output disturbances, etc. In order to solve the non-strictly repetitive problems, the High-Order Internal Model (HOIM)-based ILC is proposed. HOIM can be formulated as a polynomial in the iteration domain, which is auto-regressive. HOIM-based ILC for nonlinear systems is more complex than HOIM-based ILC for linear systems, and when the system parameters change iteratively, the Lyapunov-based analysis method is used instead of traditional contraction mapping method. Not only can HOIM be integrated into the traditional ILC, it can be also combined with other control methods, such as adaptive control, terminal control, repetitive control and so on. In this paper, we review the advances in HOIM-based ILC, systematically sort out the development and main contents of HOIM, summarize its main applications and extensions, and finally put forward some further development directions.

High-Order Internal Model-Based Iterative Learning Control for 2-D Linear FMMI Systems With Iteration-Varying Trajectory Tracking

This paper is concerned with iterative learning control (ILC) algorithms for two-dimensional (2-D) linear discrete systems described by the first Fornasini–Marchesini model (FMMI) with iteration-varying reference trajectories/profiles. The variation of reference trajectories in iteration domain is represented by a high-order internal model (HOIM) formula. Robustness and convergence of two types of HOIM-based ILC laws with different boundary conditions are investigated, respectively. A strategy employed in this paper is to reconstruct the HOIM-based ILC process of the 2-D linear FMMI system into a set of 2-D linear inequalities or a 2-D linear Roesser model such that sufficient robustness/convergence conditions of the HOIM-based ILC laws are obtained. Under random boundary conditions, the designed ILC law (9) is capable to drive the ILC tracking error into a bounded range. Moreover, under the HOIM-based boundary conditions, a perfect tracking to the iteration-varying reference trajectories can be achieved by utilizing the proposed ILC law (32). Two simulation examples are given to validate the effectiveness of the two proposed ILC algorithms.

An adaptive discrete-time ILC strategy using fuzzy systems for iteration-varying reference trajectory tracking

In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.

Adaptive iterative learning control of non-linear MIMO continuous systems with iteration-varying initial error and reference trajectory

In this article, an adaptive iterative learning control (ILC) approach is presented to deal with a class of non-linear multi-input multi-output (MIMO) continuous systems with parametric uncertainty. Unlike general ILC techniques, the proposed adaptive ILC approach allows that both the initial error and the reference trajectory are iteration-varying in the ILC process. The designed ILC tracking strategy is to set out a very small initial time interval, and track the iteration-varying reference trajectory beyond the initial time interval. The reference trajectory tracking error beyond the initial time interval can be driven to zero. While the proposed adaptive ILC technique is applied to the repetitive learning control of non-linear MIMO continuous systems, a complete reference trajectory tracking over the whole time interval can be achieved.

Adaptive discrete-time iterative learning control for non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory

Most of the available results in iterative learning control (ILC) hitherto have considered the ILC systems with fixed initial error and iteration-invariant reference trajectory. An adaptive discrete-time ILC scheme is presented to deal with the ILC problem of non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory. The designed adaptive ILC law learns parametric system dynamics and pursues the iteration-varying reference trajectory tracking from iteration to iteration. It can drive the ILC tracking error to zero asymptotically beyond the initial time step, and keep all adjustable parameters and system signals bounded as the number of iteration approaches infinity. Numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.