Data-Driven Event-Triggered Control for Switched Systems Based on Neural Network Disturbance Compensation

2021 ◽  
Author(s):  
Yiwen Qi ◽  
Xiujuan Zhao ◽  
Jie Huang
Keyword(s):  
Neural Network ◽  
Switched Systems ◽  
Data Driven ◽  
Disturbance Compensation ◽  
Event Triggered

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

Output regulation for networked switched systems with alternate event-triggered control under transmission delays and packet losses

Automatica ◽  
2021 ◽  
Vol 131 ◽  
pp. 109716
Author(s):  
Lili Li ◽  
Jun Fu ◽  
Yu Zhang ◽  
Tianyou Chai ◽  
Linyang Song ◽  
...  
Keyword(s):  
Switched Systems ◽  
Output Regulation ◽  
Packet Losses ◽  
Transmission Delays ◽  
Event Triggered

Adaptive event-triggered control for master-slave switched systems subject to attacked state-dependent switching law

2021 ◽  
pp. 014233122098594
Author(s):  
Lingcong Nie ◽  
Xindi Xu ◽  
Yan Li ◽  
Weiyu Jiang ◽  
Yiwen Qi ◽  
...  
Keyword(s):  
Time Delay ◽  
Switched Systems ◽  
System Performance ◽  
Actuator Saturation ◽  
Sufficient Conditions ◽  
Switched System ◽  
Switching Law ◽  
Variable Threshold ◽  
Event Triggering ◽  
Event Triggered

This paper investigates adaptive event-triggered [Formula: see text] control for network-based master-slave switched systems subject to actuator saturation and data injection attacks. It is an important and unrecognised issue that the switching signal is affected from both event-triggering scheme and network attacks. An adaptive event-triggering scheme is proposed that can adjust the triggering frequency through a variable threshold based on system performance. Furthermore, considering the impacts of transmission delays and actuator saturation, an event-triggered time-delay error switched system is developed. Subsequently, by utilizing piecewise Lyapunov functional technique, sufficient conditions are derived to render the time-delay error switched system to have an [Formula: see text] performance level. In particular, the coupling between switching instants and data updating instants is analyzed during the system performance analysis. Moreover, sufficient conditions for the desired state-feedback controller gains and event-triggering parameter are presented. Finally, a numerical example is given to verify the effectiveness of the proposed method.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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