Mixed PD-Type Iterative Learning Control Algorithm for a Class of Parabolic Singular Distributed Parameter Systems

Dai, Xisheng; Zhou, Xingyu

doi:10.1109/access.2021.3050486

Cited by 14 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For ILC systems, good control performance may be achieved even where it is difficult to carry out system modeling. In the earlier stage of ILC research, ILC techniques are mainly developed on the basis of contraction mapping method (contraction-mapping ILC), which leads to the in-depth study and application of Dtype ILC [24], [25], P-type ILC [26]- [30], PD-type ILC [31], [32] and PID-type ILC [33]. Later on, with the continuous development of ILC technique [34], [35], some other ILC design methods have been widely investigated.…”

Section: Fig 1 the Prototype Of A Pammentioning

confidence: 99%

Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Yang¹,

Wang

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel angle error-track adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism with nonzero initial errors. Lyapunov synthesis is used to design the adaptive learning controller and analyze the stability of closed-loop PAM system. Firstly, the system modeling for the PAM-actuated mechanism is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the initial position problem of iterative learning control. The parametric uncertainties in the controlled system are estimated by using difference learning method. Robust control strategy is used to deal with nonparametric uncertainties and disturbances. By making the system error follow the desired error trajectory over the whole time interval as the iteration number increases, we derive the accurate tracking from the system state to the reference trajectory during the predetermined part time interval. Simulation results show the effectiveness of the propose angle error-tracking adaptive ILC scheme.

show abstract

Section: Fig 1 the Prototype Of A Pammentioning

confidence: 99%

Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Yang¹,

Wang

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…That means GT G > 0. By the definition of (L 2 , λ) norm [33] and let Assumptions 1 to 2 be satisfied, it becomes…”

Section: Convergence Analysismentioning

confidence: 99%

“…At the same time, the system has static and dynamic characteristics, so the control problem of the system has complexity [30]. At present, there are few theoretical achievements on singular distributed parameter systems, which mainly involve studying the properties of the system itself [31][32] and the study on the control of the system as a controlled object [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

D-Type Iterative Learning Control for Open Container Motion System With Sloshing Constraints

Li¹,

Hou

Yong³

2021

IEEE Access

View full text Add to dashboard Cite

In this paper, the iterative learning control (ILC) problem is investigated for the motion system of an open container with sloshing constraints in industrial fluid packaging. Initially, a broader class of second-order in space and first-order in time linear time-invariant singular distributed parameter system is decomposed by analyzing the motion system of an open container with sloshing constraints. Meanwhile, to eliminate the influence of singular terms on the system, a closed-loop D-type ILC algorithm is designed, and the corresponding convergence conditions are manifested. Then the convergence of the control algorithm is proved strictly. The resulting tracking error of systems can converge to any small tracking accuracy. Finally, a numerical example is given to verify the convergence and effectiveness of the closed-loop D-type ILC algorithm.

show abstract

“…The iterative learning control method has these advantages of not relying on a precise mathematical model of the system, strong applicability and easy implementation. Therefore, starting from Arimoto [14], iterative learning control has been widely concerned and studied by scholars in the field of control and achieved fruitful research results [15][16][17][18][19][20] . The tracking error of the traditional control method converges along the time axis asymptotically, so it takes time to obtain the desired tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Consensus Control via Iterative Learning for Singular Multi-Agent Systems With Switching Topologies

2021

View full text Add to dashboard Cite

For the state consensus tracking of the singular multi-agent system, under the condition of the communication topology randomly switching only along time axis but unchanging along iteration axis, a distributed iterative learning control protocol is proposed. By singular value decomposition method, the singular multi-agent system is transformed into differential algebra system, and thus the state of the system is accordingly divided into two parts. Then applying the derivative of the tracking error for the first part of the state and the tracking error of the second part of the state and combining the switching topology graph, the distributed iterative learning control protocol is constructed. Furthermore, the convergence of the proposed algorithm is proved by the compression mapping method, and the convergence conditions of the algorithm are obtained. The proposed algorithm can make the state gradually approach the desired state with the increase of iterations. When the number of iterations is sufficient large, the state of each agent can completely track the desired state over a finite time interval. Finally, the simulation examples are given to further validate the effectiveness of the proposed algorithm.INDEX TERMS Singular multi-agent systems, iterative learning control, switching topologies, consensus tracking

show abstract

Mixed PD-Type Iterative Learning Control Algorithm for a Class of Parabolic Singular Distributed Parameter Systems

Cited by 14 publications

References 47 publications

Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

D-Type Iterative Learning Control for Open Container Motion System With Sloshing Constraints

Consensus Control via Iterative Learning for Singular Multi-Agent Systems With Switching Topologies

Contact Info

Product

Resources

About