Iterative learning consistency control for multi-agent systems with forgetting factors under random actuator failures

With the accelerating integration of renewable energy into new power system, load frequency control (LFC) plays a crucial role in maintaining system stability and reliability. Traditional LFC methods usually adopt fixed control strategies, which are vulnerable to the dynamic characteristics of the system. This vulnerability is especially evident during complex load fluctuations and the continuous decrease in system inertia, highlighting the need to enhance controller performance. Moreover, with the expanding role of communication networks in frequency modulation, this paper proposes a novel LFC strategy for power systems. This strategy utilizes an adaptive event-triggered mechanism based on iterative learning. The adaptive event-triggered mechanism is employed to reduce the transmission requirements of the communication network, and an iterative learning control algorithm is incorporated to improve frequency modulation performance. The effectiveness of this method is verified through simulations under various operating conditions. The results show that the adaptive event-triggered mechanism can dynamically adjust the execution frequency of the control strategy, thereby reducing communication burdens and energy losses. Additionally, the iterative learning control strategy not only improves the system response speed and stability but also enables the system to tolerate a larger event-triggered threshold, thus better coordinating with the adaptive event-triggered mechanism.

We propose a simple recursive iterative learning control (ILC) scheme for a class of uncertain time-varying nonlinear systems. The proposed constructive backstepping-like procedure provides, in a systematic way, a control Lyapunov-like function leading to a simple recursive ILC scheme guaranteeing the existence of a finite time-interval on which a perfect tracking can be achieved when the number of iteration goes to infinity. Simulation results are also provided to show the effectiveness of the proposed ILC scheme.

In this work, two practical iterative learning control (ILC) schemes are analyzed and compared in frequency domain. Both ILC schemes consist of a feedback and a learning based feedforward compensation. Through analysis, we show that the difference in the learning updating mechanisms, in the sequel, exploit the circumstances where one ILC scheme can outperform the other. Based on the system Bode plots, we can easily check the learning convergence condition, the complementary property of feedback and feedforward compensation, and which ILC scheme can perform better. For the purpose of comparison and verification, both ILC schemes are applied to a real-time ball-and-beam system.

Predictive Iterative Learning Control using Laguerre Functions

Active magnetic bearing is a promising candidate for next generation spindle motors of hard disk drives. In this paper, an iterative control scheme is proposed to suppress the rotor poison runout induced by the unbalanced forces in AMB. The ILC controller can produce the synchronous compensation force by iteratively learning rotor runout information. This compensation force makes the rotor rotate about its geometric axis

By applying iterative learning control approach, the consensus is studied for multi‐agent systems (MASs) with one‐sided Lipschitz (OSL) nonlinearity. Firstly, the P‐type and D‐type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner‐bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite‐time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples.

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 article, a class of switched nonlinear discrete-time heterogeneous (NDTH) multi-agent systems (MASs) with switching signals in both agent dynamics and communication topologies are considered, and a distributed iterative learning control (ILC) law is proposed for the output consensus of the switched NDTH MAS so that the outputs of all follower agents can well track the leader agent. With the proposed ILC controller, the iterative learning dynamic process of the switched NDTH MAS is firstly formulated as a two-dimensional (2-D) linear discrete Roesser model with specified boundary states at each switching sub-interval. Afterwards, according to the exploited properties on solution of the 2-D linear discrete Roesser model, a sufficient convergence theorem for the presented ILC controller is derived. At last, the validity of the ILC-based consensus controller is illustrated through a simulation experiment.

Iterative learning formation control for continuous-time multi-agent systems with randomly varying trial lengths

This paper considers the consensus problem of two class of multi-agent systems with communication disturbance by using iterative learning control(ILC) method and both of them has a fixed information topology with a spanning tree. Firstly, a distributed iterative learning-based control protocol is designed for a time-varying linear multi-agent system, a sufficient condition is obtained such that the consensus problem is solved. By the same way, a sufficient condition is also given for a class of nonlinear time-varying multi-agent systems to solve consensus problem. The main contribution of the proposed results is that they can restrain any type of repetitive disturbance. The validity of them is verified by the simulation results.

The 2D state space system model is established with state delay and actuator faults, and what is more, the iterative learning control (ILC) law required by the batch process model is designed. Using the knowledge of 2D system theory and an iterative learning control law, the 2D state space model can be changed into an equivalent 2D-Roesser closed-loop system model. According to the optimized cost function and Lyapunov stability theory, sufficient conditions for the solvability of model predictive control (MPC) problems in the form of linear matrix inequalities (LMIs) are given. A new solution is proposed which depends on the bound of the state delay. At last, the effectiveness of the method is proved through simulation experiments.

This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem of unknown nonlinear multi-agent systems, where the interactions between agents include cooperation and antagonistic interactions. To design the controller, the agent’s dynamics is transformed into the linear data model based on the dynamic linearization method, and then a quantized MFAILC algorithm is established based on the quantized values of the relative output measurements. The designed controller only depends on the input and output data of the agent. We prove that under the quantized MFAILC algorithm, the multi-agent systems can achieve the bipartite containment, that is, the output trajectories of followers converge to the convex hull formed by the leaders’ trajectories and the leaders’ symmetric trajectories. Finally, we provide simulations to illustrate the effectiveness of our theoretical results.

In order to improve the convergence speed of iterative learning control and reduce the fluctuation of the system error, a class of linear steady-state systems is considered. The convergence of the algorithm and error fluctuations are studied by introducing the variable-gain idea into the D-type iterative learning control algorithm with variable forgetting factor. According to the related properties of the λ norm theory, the convergence of the improved iterative learning algorithm is proved. Compared with iterative learning control with forgetting factor and iterative learning control with variable gain, MATLAB simulation analysis is performed. The simulation results show that the algorithm is effective. The improved iterative learning law not only makes the iterative error smoother, but also improves the convergence speed.

This paper is concerned with an iterative learning fault-tolerant control strategy for discrete-time nonlinear systems where actuator faults arbitrarily occur. First, the stochastic faults occurring in multiplicative and additive manner are considered. Then, statistical behaviors of both faults-corrupted control signals from the actuator to the plant and faults-free ones from the iterative learning controller to the actuator are analyzed. Meanwhile, sufficient conditions of convergence for the proposed strategy are established by resorting to the time-weighted norm technique. Finally, two numerical examples are provided to illustrate the effectiveness and reliability of the proposed results. Both theoretical analysis and simulations indicate that the developed strategy is satisfactory in preserving decent tracking accuracy of the addressed systems subject to actuator faults.

Cooperative output regulation problem with faults based on adaptive event-triggered mechanism