Adaptive Kalman Filtering Model Predictive Controller Design for Stabilizing and Trajectory Tracking of Inverted Pendulum

2020 ◽  
Vol 101 (6) ◽  
pp. 677-688 ◽  
Author(s):  
Akshaya Kumar Patra
Keyword(s):  
Kalman Filtering ◽  
Trajectory Tracking ◽  
Inverted Pendulum ◽  
Controller Design ◽  
Model Predictive Controller ◽  
Predictive Controller

Multi-objective adaptive cruise controller design using nonlinear predictive controller with the objective function with variable weights determined by fuzzy logic controller

2021 ◽  
pp. 095440702110147
Author(s):  
Behzad Samani ◽  
Amir H. Shamekhi
Keyword(s):  
Objective Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Hierarchical Control ◽  
Control Input ◽  
Cruise Control ◽  
Model Predictive Controller ◽  
Control Step ◽  
Predictive Controller

In this paper, an adaptive cruise control system with a hierarchical control structure is designed. The upper-level controller is a model predictive controller (MPC) that by minimizing an objective function in the presence of the constraints, calculates the desired acceleration as control input and sends it to the lower-level controller. So the lower-level controller, which is a fuzzy controller, determines the amount of throttle valve opening or brake pressure to get the car to this desired acceleration. The model predictive controller performs optimization at each control step to minimize the objective function and achieve the reference values. Usually, the objective function has predetermined and constant weights to meet objectives such as maintain the driver’s desired speed and increase safety and in some cases increase comfort and reduce fuel consumption. In this paper, it is suggested that instead of using constant weights in the objective function, these weights should be determined by a fuzzy controller, depending on the different conditions in which the car is placed. The simulation results show that the variability of the weights of the objective function achieves control objectives much better than the optimization of the objective function with constant weights.

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

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