Path Following Control of a Mobile Robot Using Contractive Model Predictive Control

2013 ◽  
Vol 397-400 ◽  
pp. 1366-1372
Author(s):  
Kiattisin Kanjanawanishkul
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Path Following ◽  
Open Loop ◽  
System Stability ◽  
Bounded Control ◽  
Loop Control ◽  
Guarantee System ◽  
The Stability

In this paper, we propose a novel controller based on contractive model predictive control for the path following problem of a mobile robot. Besides dealing with path following, we also fulfill the following objectives: bounded control signals and optimal forward velocity. These three objectives are all achieved through integrating into our model predictive control framework as constraints. However, the major concern in the use of model predictive control is whether such an open-loop control scheme can guarantee system stability. In this case, we apply the idea of a contractive constraint to guarantee the stability of our MPC framework. To illustrate its effectiveness, several simulation scenarios have been conducted.

Wheel Slip Regulation for Heavy Commercial Road Vehicles Using Model Predictive Control Subsumed With Auto-Regressive Time-Series Modelling

2019 ◽  
Author(s):  
Pavel Vijay Gaurkar ◽  
Akhil Challa ◽  
Shankar C. Subramanian ◽  
Gunasekaran Vivekanandan ◽  
Sriram Sivaram
Keyword(s):  
Time Series ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Open Loop ◽  
Simulation Software ◽  
Control Input ◽  
Wheel Slip ◽  
Loop Control ◽  
Time Series Modelling ◽  
Auto Regressive

Abstract Wheel Slip Regulation (WSR) is one of the Active Vehicle Safety Systems (AVSSs) for maintaining vehicle stability and maneuverability during emergency braking An approach for wheel slip prediction is proposed in this paper, which involves Auto-Regressive (AR) Time-Series modelling of longitudinal vehicle acceleration. This technique allows the usage of linear longitudinal vehicle dynamics for wheel slip estimation. A wheel slip prediction model was developed considering measurements from accelerometer and wheel speed sensor. This modified the Model Predictive Control (MPC) formulation to a univariate control input problem, involving braking torque. The objective function was devised for solving a least-squares reference tracking problem. An analytical solution for the MPC optimization problem was derived and implemented towards WSR. The proposed framework was programmed in MATLAB Simulink® and co-simulated with IPG TruckMaker® (a vehicle dynamic simulation software). The algorithm was tested in a Hardware-in-Loop (HiL) setup consisting of a pneumatic air brake system interfaced with IPG TruckMaker®. Open loop studies from HiL led to the inclusion of Kalman filter for estimate tuning and PID inner loop control for brake pressure transients, which improved wheel slip regulation.

scholarly journals Model Predictive Control for the Process of MEA Absorption of CO2 Based on the Data Identification Model

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 183
Author(s):  
Qianrong Li ◽  
Wenzhao Zhang ◽  
Yuwei Qin ◽  
Aimin An
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Aspen Plus ◽  
Open Loop ◽  
Operating Conditions ◽  
Effective Control ◽  
Subspace Identification ◽  
Capture Process ◽  
The Stability ◽  
Intractable Problem

The absorption process of CO2 by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO2 chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO2 related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model combined with the state-space equation using Matlab/Simulink to analyze the changes of the system under two different disturbances. The simulation results show that the control performance of the MPC algorithm is significantly better than that of the traditional proportion integral differential (PID) system, with excellent setpoint tracking ability and robustness, which improve the stability and flexibility of the system.

scholarly journals Model Predictive Control of Active Suspension for an Electric Vehicle Considering Influence of Braking Intensity

2020 ◽  
Vol 11 (1) ◽  
pp. 52 ◽  
Author(s):  
Junjiang Zhang ◽  
Yang Yang ◽  
Minghui Hu ◽  
Chunyun Fu ◽  
Jun Zhai
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pitch Angle ◽  
Ride Comfort ◽  
Rear Wheel ◽  
Front Wheel ◽  
Mean Square ◽  
Vehicle Model ◽  
Loop Control ◽  
The Stability

In the process of vehicle braking, braking intensity has a significant impact on vehicle comfort, and studies on this aspect have been limited. Therefore, an equivalent 4-degree-of-freedom half-vehicle model including the braking intensity influence was established in this study. Subsequently, considering braking intensity as the interference quantity that is the uncontrollable input, a model predictive control (MPC) strategy in which the vertical velocities of front body, rear body, front wheel, and rear wheel are the control targets was proposed. Based on Lyapunov’s stability theory, the stability of the MPC system was proven. Finally, a dual-loop control (DLC) strategy was used for comparison to verify the superiority of the MPC strategy. The results indicate that compared with the DLC strategy under the gradual braking condition, the root mean square of the front and rear body vertical velocities, body pitch angle, and body pitch angle velocity under the MPC strategy were all reduced by more than 70%, thus improving the ride comfort of the vehicle.

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