Xuhui Bu

H∞ iterative learning controller design for a class of discrete-time systems with data dropouts

In this paper, the issue of H∞ iterative learning controller design is considered for a class of discrete-time systems with data dropouts. With the super-vector formulation of iterative learning control (ILC), such a system can be formulated as a linear discrete-time stochastic system in the iteration domain, and then a sufficient condition guaranteeing both stability of the ILC process and the desired H∞ performance in the iteration domain is presented. The condition can be derived in terms of linear matrix inequalities that can be solved by using existing numerical techniques. A numerical simulation example is also included to validate the theoretical results.

Stability Analysis of High-Order Iterative Learning Control for a Class of Nonlinear Switched Systems

This paper considers the stability of high-order PID-type iterative learning control law for a class of nonlinear switched systems with state delays and arbitrary switched rules, which perform a given task repeatedly. The stability condition for the proposed high-order learning control law is first established, and then the stability is analyzed based on contraction mapping approach in the sense of norm. It is shown that the proposed iterative learning control law can guarantee the asymptotic convergence of the tracking error for the entire time interval through the iterative learning process. Two examples are given to illustrate the effectiveness of the proposed approach.

Model-Free Adaptive Control Algorithm with Data Dropout Compensation

The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.

Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control

This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent’s dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input–output data of each agent. The effectiveness of the proposed design is verified by simulation examples.

Iterative learning control for linear switched systems with arbitrary switched rules

In this paper, the problem of iterative learning control for a class of linear discrete-time switched systems with arbitrary switched rules is considered. It is assume that the considered switched systems are operated during a finite time interval repetitively, and then the iterative learning control scheme can be introduced. It is also shown that under some given conditions, the D-type iterative learning control law can guarantee the asymptotic convergence of the output error between the desired output and the actual output for the entire time interval through the iterative learning process. An example is given to illustrate the effectiveness of the proposed approach.

Adaptive Iterative Learning Control for Linear Systems With Binary-Valued Observations

This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iteration axis. It is shown that, even though the available system information is very limited and the desired trajectory is iteration-varying, the proposed adaptive ILC algorithm can guarantee the convergence of parameter estimation over a finite-time interval along the iterative axis; meanwhile, the tracking error is pointwise convergence asymptotically. Two examples are given to validate the effectiveness of the algorithm.

An intermittent iterative learning control design based on a 2D roesser system

In network-based iterative learning control (ILC) systems, intermittent measurement often occurs during the data packet transfers from the remote plant to the ILC controller. In this paper, measurement missing is modeled by stochastic variables satisfying the Bernoulli random binary distribution, and then the design of ILC law is transformed into a feedback control problem of a 2D stochastic system described by Roesser model. A sufficient condition for mean-square asymptotic stability is established by means of linear matrix inequality (LMI) technique, and formulas can be given for the control law design simultaneously. A numerical example illustrates the effectiveness of the proposed results.

Iterative Feedback Tunning for Boiler–Turbine Systems

This paper presents an application of iterative feedback tuning (IFT) based on PID controller to the boiler–turbine system. We first transfer the nonlinear boiler–turbine system into a linear system, and then the PID controller can be designed to solve the regulation problem. The PID controller parameters are online tuned based on IFT method. Compare to the fixed parameter PID controller, the IFT-tuned PID controllers can obtain better performance. Simulation shows the effective of the proposed approach.