A Sufficient Asymptotic Stability Condition in Generalised Model Predictive Control to Avoid Input Saturation

2018 ◽  
pp. 251-257 ◽  
Author(s):  
Paolo Mercorelli
Keyword(s):  
Asymptotic Stability ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Stability Condition ◽  
Input Saturation ◽  
Asymptotic Stability Condition

scholarly journals Path following of underactuated surface ships based on model predictive control with neural network

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094595
Author(s):  
Ronghui Li ◽  
Ji Huang ◽  
Xinxiang Pan ◽  
Qionglei Hu ◽  
Zhenkai Huang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Input Saturation ◽  
Path Following ◽  
Environmental Disturbances ◽  
Control Approach ◽  
Underactuated Ship ◽  
Mmg Model ◽  
The Mathematical Model

A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

Robust model predictive control synthesis for state-delayed systems with randomly occurring input saturation nonlinearities

2016 ◽  
Vol 40 (1) ◽  
pp. 179-190 ◽  
Author(s):  
Langwen Zhang ◽  
Wei Xie ◽  
Zhaozhun Zhong ◽  
Jingcheng Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Controller Design ◽  
Input Saturation ◽  
Time Instant ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Model Parameters ◽  
Delayed Systems

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms.

Sign in / Sign up

Export Citation Format

Share Document