Model-free Adaptive Iterative Learning Control Research Articles

High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence

Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain high-performance tracking control. In this article, a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations; meanwhile, a high-order estimation algorithm is designed to estimate the pseudopartial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller are verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFAILC can track the desired trajectory with improved convergence and tracking performance.

Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control

In this paper, a distributed disturbance-compensation based model free adaptive iterative learning control (MFAILC) algorithm is proposed to achieve the consensus tracking of nonlinear multi-agent systems (MAS) with unknown disturbance. Here, both fixed and iteration varying topologies are considered. A general dynamic linearization model with disturbance input is first proposed to each agent along the iteration axis for nonlinear MAS. Due to the existence of unknown disturbance, an online disturbance estimation algorithm is proposed to estimate actual disturbance only based on the input/output (I/O) measurement data. Then, a distributed MFAILC method with disturbance compensation is developed such that consensus tracking errors are convergent. Last, the effectiveness of the developed method can be illustrated from the simulation examples.

Model Free Adaptive Iterative Learning Consensus Tracking Control for a Class of Nonlinear Multiagent Systems

This paper proposes a distributed model free adaptive iterative learning control (MFAILC) method for a class of unknown nonlinear multiagent systems to perform consensus tracking. Here, both fixed and iteration-varying topologies are considered and only a subset of followers can access the desired trajectory in each topology. To design the control protocol, the agent’s dynamic is first transformed into a dynamic linearization model along the iteration axis, and then a distributed MFAILC scheme is constructed to guarantee that all agents can track the desired trajectory. Through rigorous analysis, it is shown that under this novel distributed MFAILC scheme, the tracking errors of all agents are convergent along the iteration axis. The main merit of this design is that consensus tracking task can be achieved only utilizing the input/output data of the multiagent system. Three examples are given to validate the effectiveness of the proposed design.

A novel dynamic proportional-type iterative learning control design for non-linear systems

This paper presents a model-free adaptive iterative learning control (ILC) scheme called a proportional-type ILC scheme for non-linear systems. The obvious characteristic of the proposed ILC scheme is that we can easily finish the ILC task just utilising the Lipschitz constant of the system. In the proposed ILC scheme, the time-vary learning gain can be produced merely by input and output (I/O) measurements. Moreover, the convergence conclusion can be expressed by the ranges of the pseudo-partial derivative and the learning gain. In actual operation, a reasonable and useful convergence condition by a constant is also provided for selection. At last, the effectiveness of the proposed ILC scheme is shown by simulations.

An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications

A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given.

Robust H∞ Stabilization of Uncertain T-S Fuzzy Systems via Dynamic Integral Sliding Mode Control

Abstract Duo to the periodicity of farm cropping, harvesting, seeding and spraying, a discrete time model free adaptive iterative learning control (MFAILC) algorithm for path tracking of farm vehicle is proposed. Based on the dynamical form linearization model for farm vehicle kinematic model l, the MFAILC algorithm can be designed just depending on the I/O data of the farm vehicle. The proposed method also can compensate the effect of initial shifting to preserve a perfect tracking. Simulations results illustrate the validity of the proposed methods.

Model Free Adaptive Iterative Learning Control for Farm Vehicle Path Tracking

Abstract Duo to the periodicity of farm cropping, harvesting, seeding and spraying, a discrete time model free adaptive iterative learning control (MFAILC) algorithm for path tracking of farm vehicle is proposed. Based on the dynamical form linearization model for farm vehicle kinematic model l, the MFAILC algorithm can be designed just depending on the I/O data of the farm vehicle. The proposed method also can compensate the effect of initial shifting to preserve a perfect tracking. Simulations results illustrate the validity of the proposed methods.