Research on application of boost converter based on data-driven full-format model-free adaptive controller

2016 ◽  
Author(s):  
He Lihong ◽  
Li Ke ◽  
Wang Hongyu
Keyword(s):  
Adaptive Controller ◽  
Boost Converter ◽  
Data Driven ◽  
Model Free ◽  
Research On Application

scholarly journals Data-Driven Model-Free Adaptive Control of Z-Source Inverters

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7438
Author(s):  
Yasin Asadi ◽  
Amirhossein Ahmadi ◽  
Sasan Mohammadi ◽  
Ali Moradi Amani ◽  
Mousa Marzband ◽  
...  
Keyword(s):  
State Feedback ◽  
Adaptive Controller ◽  
Green Energy ◽  
Data Driven ◽  
Control Action ◽  
Linear Quadratic ◽  
Model Based ◽  
Model Free ◽  
Quadratic Integral ◽  
Lyapunov Sense

The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

Research on Application of Buck Converter Based on Data-Driven Model-Free Adaptive Controller

2013 ◽  
Vol 427-429 ◽  
pp. 1044-1047
Author(s):  
Li Hong He ◽  
Hong Yu Wang ◽  
Jin Jin Wang ◽  
Jian Hua Wu
Keyword(s):  
State Feedback ◽  
Adaptive Controller ◽  
Linearization Method ◽  
Buck Converter ◽  
The State ◽  
Data Driven ◽  
Circuit Parameter ◽  
Exact Linearization ◽  
Control Effect ◽  
Model Free

State feedback exact linearization method in Buck converters have been widely recognized in the domestic, and it achieved a good control effect, but because the method is based on the accurate modeling, so when the model of controlled object is not accurate enough or circuit parameters in the application are changed, state feedback exact linearization method will be difficult to achieve the ideal control effect. In this paper we use the based on data-driven model-free adaptive controller (MFAC) on Buck Converter Applications for the first time. We analyzed the output characteristic of model-free adaptive control and the state feedback when load changes by simulation. Through the experimental platform of practical, we compared MFAC and the state feedback. Results show that, when the circuit parameter varies in a large range, MFAC has better robustness and resistance mismatch capability, high steady accuracy.

Data-driven tracking consensus for a class of unknown nonlinear multi-agent systems

2021 ◽  
pp. 107754632110340
Author(s):  
Jia Wu ◽  
Ning Liu ◽  
Wenyan Tang
Keyword(s):  
Control Method ◽  
Sufficient Conditions ◽  
Data Driven ◽  
The Novel ◽  
Multi Agent Systems ◽  
Tracking Errors ◽  
Agent Systems ◽  
Model Free ◽  
Strongly Connected ◽  
Multi Agent

This study investigates the tracking consensus problem for a class of unknown nonlinear multi-agent systems A novel data-driven protocol for this problem is proposed by using the model-free adaptive control method To obtain faster convergence speed, one-step-ahead desired signal is introduced to construct the novel protocol Here, switching communication topology is considered, which is not required to be strongly connected all the time Through rigorous analysis, sufficient conditions are given to guarantee that the tracking errors of all agents are convergent under the novel protocol Examples are given to validate the effectiveness of results derived in this article

scholarly journals Adaptive PD Sliding Mode Control For 4 DOF SCARA Variant

2021 ◽  
Author(s):  
Manjeet Tummalapalli
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
High Speed ◽  
Sliding Mode ◽  
Adaptive Controller ◽  
Degree Of Freedom ◽  
Tracking Performance ◽  
Advantages And Disadvantages ◽  
Model Free ◽  
Adaptive Law

This project proposes a new SCARA variant with 4 degree of freedom. The proposed variant is achieved by swapping joint 2 and joint 3 of the standard SCARA robots. An adaptive controller is defined based on the advantages and disadvantages of PD, and SMC controllers.The purpose of the project is to understand the dynamics of the variant and to track the performance for trajectories. Simulations for tracking performance are carried under linear and circular trajectories. The variant is studied over the three controllers; PD, PD-SMC and A-PD-SMC. The variant under the adaptive controller is most efficient in terms of tracking performance and the control inputs to the system. The system is simulated under high speed and with the influence of friction at the joints. The control gains are held constant for both the trajectories and hence the controller is able to perform good under changing trajectories. Due to the use of the adaptive law, the system is at the ease of implementation and since no priori knowledge if the system is needed, it is model free. Therefore, the proposed adaptive PD-SMC has proven to provide good, robust trajectory tracking.

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