A New Dynamic Event-Triggered Mechanism Based Model Predictive Control for Systems with Polytopic Uncertainties

2021 ◽  
Author(s):  
Fan Wei ◽  
Xiongbo Wan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Event ◽  
Polytopic Uncertainties ◽  
Event Triggered

Event-triggered model predictive control of positive systems with random actuator saturation

2021 ◽  
Author(s):  
Junfeng Zhang ◽  
Suhuan Zhang ◽  
Peng Lin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Actuator Saturation ◽  
Domain Of Attraction ◽  
Programming Approach ◽  
Interval Estimate ◽  
Positive Systems ◽  
Predictive Algorithm ◽  
Controller Gain ◽  
Event Triggered

Abstract This paper investigates the event-triggered model predictive control of positive systems with actuator saturation. Interval and polytopic uncertainties are imposed on the systems, respectively. First, a new model with actuator saturation obeying Bernoulli distribution is established, which is more general and powerful for describing the saturation phenomenon than the saturation in a certain way. Then, a linear event-triggering condition is constructed based on the state and error signal. An interval estimate approach is presented to reach the positivity and stability of the systems. The saturation part in the controller is technically transformed into a non-saturation part. Thus, a linear programming approach is proposed to compute the event-triggered controller gain and the corresponding domain of attraction gain. A predictive algorithm is introduced for the computation of the event-triggered controller parameters. Finally, an example is provided to illustrate the validity of the design.

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