Adaptive iterative learning control for robot manipulators: Experimental results

Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction

This paper investigates the adaptive iterative learning control strategy for nonstrict-feedback nonlinear systems with state constraints, which perform repetitive tasks over the operation time interval. Based on backstepping technique, the adaptive iterative learning controller overcomes the algebraic loop problem caused by the nonstrict-feedback structure. A neural state observer is constructed to estimate the unavailable states. The hybrid time/iteration domain adaptation mechanism is utilised to estimate the unknown nonlinear functions comprising all state variables. Based on Lyapunov-like synthesis, a tan-type barrier Lyapunov function is introduced into the composite energy function, which can guarantee the tracking error convergence along the iteration axis without violating the constraint requirement. Finally, the simulation results illustrate the effectiveness of the proposed control schemes.

An adaptive iterative learning control scheme is proposed for a class of discrete-time nonlinear systems with random initial conditions and iteration-varying desired trajectories. The discrete Nussbaum gain method is incorporated into the control design to tackle the problem associated with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis. The effectiveness of the proposed control law is verified through numerical simulation.

A new adaptive iterative learning control (AILC) scheme is proposed to solve the finite-time hybrid function projective synchronization (HFPS) problem of chaotic systems with unknown periodic time-varying parameters. Fourier series expansion (FSE) is introduced to deal with the problem of uncertain time-varying parameters. The bound of the expanded remaining items is unknown. A typical convergent series is used to deal with the unknown bound in the design process of the controller. The adaptive iterative learning synchronization controller and parameter update laws are designed. Two different chaotic systems are synchronized asymptotically according to different proportional functions on a finite time interval by Lyapunov stability analysis. The simulation example proves the feasibility and effectiveness of the proposed method.

Further results on adaptive iterative learning control of robot manipulators

In this paper, a robust adaptive iterative learning control (ILC) scheme is developed for the high-precision attitude tracking control of spacecraft in the presence of parametric uncertainties and external disturbances. The proposed robust adaptive ILC law consists of three parts, i.e., the classic proportional-derivative (PD) feedback control term, the PD-type feedforward learning term, and the robust term. The adaptive updating laws are designed for the gain matrices of both the classic PD feedback control term and the PD-type feedforward learning term. The asymptotic stability of the whole closed-loop system is proved through the Lyapunov function–based convergence analysis. The proposed robust adaptive ILC scheme can not only compensate for the parametric uncertainties and repetitive disturbance, but also handle the nonrepetitive disturbance owing to the robust control concept. Moreover, the proposed robust adaptive ILC scheme can achieve the fast convergence speed benefiting from the adaptive technique. Numerical simulations illustrate the effectiveness and superiority of the proposed ILC scheme.

In this paper, we propose two simple adaptive iterative learning control (AILC) algorithms for trajectory tracking control problem of rigid robot manipulators that track the same control trajectory repeatedly over a finite time interval. The design comprises of a linear parameterization robot feedback control structure and a learning parametric adaptation law that iteratively updates unknown uncertain parameters based upon the use of a Lyapunov energy function. In contrast to other existing adaptive ILC schemes for robot manipulators, where large feedback and learning gains are required to get robustness against large modelling uncertainties and disturbances in the early stage of the operation, the proposed adaptive ILC schemes require small feedback gains. The presented scheme 2 is simpler in structure and easier to implement in the real-world operation in the sense that it requires less computational effort and computing power without any priori knowledge of robot dynamics. Owing to the robustness of the adaptation laws against large disturbances and modelling uncertainties in the early trials, a high-learning gain can be used in order to achieve fast learning convergence.

In this paper, the uncertain model of the robotic system is decomposed into repetitive and non-repetitive parts, and the norm model of the system is taken into account. By using the Lyapunov method, an adaptive robust iterative learning control scheme is presented for the robotic system with both structured and unstructured uncertainty, and the overall stability of the system in the iteration domain is established. In the scheme the bound parameters estimated as well as the iterative learning control input are adjusted in the iteration domain. The validity of the scheme is illustrate through a simulation example.

SummaryIn this study, we consider the boundary control problem of a flexible manipulator in the presence of system parametric uncertainty and external disturbances. The dynamic behavior of the flexible manipulator is represented by partial differential equations (PDEs). Based on the Lyapunov method, we propose an adaptive iterative learning control scheme for trajectory tracking and vibration suppressing of a flexible manipulator. The proposed control scheme is designed using both a proportional‐derivative feedback structure and an iterative term. The learning convergence of iterative learning control is achieved through rigorous analysis without any simplification or discretization of the PDE dynamics. Finally, the results are illustrated using numerical simulations for control performance verification. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

This chapter presents an adaptive iterative learning control (AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone. A novel nonlinear form of dead-zone nonlinearity is presented. The assumption of identical initial condition for iterative learning control (ILC) is removed by introducing boundary layer function. The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young0s inequality. Radial basis function neural networks are used to model the time-varying uncertainties. The hyperbolic tangent function is employed to avoid the problem of singularity. According to the property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, simulation examples are presented to verify the effectiveness of the proposed approach.

Learning control for robotic manipulators has been developed over the past decade and to the best of the authors’ knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

In this paper, an adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems. The neural network is introduced into iterative learning control. Unknown smooth function vectors and unknown time-delay functions are approximated by two neural networks, respectively. The requirement of traditional iterative learning control algorithm on the nonlinear functions (such as global Lipschitz condition) is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functional, all signals in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proved to converge to a small neighborhood of the desired trajectory.

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

In this paper, a novel observer-based adaptive backstepping control scheme is proposed for time-varying formation tracking of multiple unmanned aerial vehicle (multi-UAV) system consisting of multiple nonlinear UAVs with unknown parameters and immeasurable states. The communication network among the UAVs is represented by a directed graph and a part of the UAVs cannot obtain the reference information directly. A state observer is designed to estimate the immeasurable states. An adaptive control scheme is developed by employing backstepping control strategy and the designed state observer. It is proved that the proposed scheme can guarantee that the UAVs maintain the formation shape while tracking the reference trajectory. Besides, all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Simulation example is provided to show the effectiveness of the proposed scheme.