scholarly journals Trajectory Planning in Time-Varying Adverse Weather for Fixed-Wing Aircraft Using Robust Model Predictive Control

Aerospace ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 68 ◽  
Author(s):  
Federico Mothes
Keyword(s):  
Time Varying ◽  
Robust Model Predictive Control ◽  
Moving Obstacles ◽  
Robust Model ◽  
Single Target ◽  
Adverse Weather ◽  
Relevant Task ◽  
Time Optimal ◽  
Predictive Controller ◽  
Nonholonomic Planning

The avoidance of adverse weather is an inevitable safety-relevant task in aviation. Automated avoidance can help to improve safety and reduce costs in manned and unmanned aviation. For this purpose, a straightforward trajectory planner for a single-source-single-target problem amidst moving obstacles is presented. The functional principle is explained and tested in several scenarios with time-varying polygonal obstacles based on thunderstorm nowcast. It is furthermore applicable to all kinds of nonholonomic planning problems amidst nonlinear moving obstacles, whose motion cannot be described analytically. The presented resolution-complete combinatorial planner uses deterministic state sampling to continuously provide globally near-time-optimal trajectories for the expected case. Inherent uncertainty in the prediction of dynamic environments is implicitly taken into account by a closed feedback loop of a model predictive controller and explicitly by bounded margins. Obstacles are anticipatory avoided while flying inside a mission area. The computed trajectories are time-monotone and meet the nonholonomic turning-flight constraint of fixed-wing aircraft and therefore do not require postprocessing. Furthermore, the planner is capable of considering a time-varying goal and automatically plan holding patterns.

scholarly journals Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjun Liu ◽  
Guang Chen ◽  
Alois Knoll
Keyword(s):  
Cost Function ◽  
Infinite Horizon ◽  
Control Law ◽  
Time Varying ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Robust Model Predictive Control ◽  
Time Varying Delay ◽  
Robust Model ◽  
Varying Delay

In this paper, we design a robust model predictive control (MPC) controller for vehicle subjected to bounded model uncertainties, norm-bounded external disturbances and bounded time-varying delay. A Lyapunov-Razumikhin function (LRF) is adopted to ensure that the vehicle system state enters in a robust positively invariant (RPI) set under the control law. A quadratic cost function is selected as the stage cost function, which yields the upper bound of the infinite horizon cost function. A Lyapunov-Krasovskii function (LKF) candidate related to time-varying delay is designed to obtain the upper bound of the infinite horizon cost function and minimize it at each step by using matrix inequalities technology. Then the robust MPC state feedback control law is obtained at each step. Simulation results show that the proposed vehicle dynamic controller can steer vehicle states into a very small region near the reference tracking signal even in the presence of external disturbances, model uncertainties and time-varying delay. The source code can be downloaded on https://github.com/wenjunliu999.

scholarly journals New Results on Robust Model Predictive Control for Time-Delay Systems with Input Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Qing Lu ◽  
Yiyong Sun ◽  
Qi Zhou ◽  
Zhiguang Feng
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Upper And Lower Bounds ◽  
Delay Systems ◽  
Input Constraints ◽  
New Model ◽  
Robust Model Predictive Control ◽  
Time Varying Delay ◽  
Robust Model ◽  
Predictive Controller

This paper investigates the problem of model predictive control for a class of nonlinear systems subject to state delays and input constraints. The time-varying delay is considered with both upper and lower bounds. A new model is proposed to approximate the delay. And the uncertainty is polytopic type. For the state-feedback MPC design objective, we formulate an optimization problem. Under model transformation, a new model predictive controller is designed such that the robust asymptotical stability of the closed-loop system can be guaranteed. Finally, the applicability of the presented results are demonstrated by a practical example.

scholarly journals Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2885
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran ◽  
Hany M. Hasanien ◽  
Rania A. Turky ◽  
Fahad Albalawi ◽  
...  
Keyword(s):  
Voltage Regulators ◽  
System Response ◽  
Trial And Error ◽  
Control Effort ◽  
Robust Model Predictive Control ◽  
Robust Model ◽  
Control Paradigm ◽  
Parameters Uncertainty ◽  
Predictive Controller ◽  
The Stability

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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