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.

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.

Fuzzy model predictive control for 2-DOF robotic arms

2018 ◽  
Vol 38 (5) ◽  
pp. 568-575 ◽  
Author(s):  
Weilin Yang ◽  
Wentao Zhang ◽  
Dezhi Xu ◽  
Wenxu Yan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Robotic Arm ◽  
Real Time System ◽  
Content Type ◽  
Loop Control ◽  
Robotic Arms ◽  
Intrinsic Nonlinearity ◽  
Robotic Arm Control

Purpose Robotic arm control is challenging due to the intrinsic nonlinearity. Proportional-integral-derivative (PID) controllers prevail in many robotic arm applications. However, it is usually nontrivial to tune the parameters in a PID controller. This paper aims to propose a model-based control strategy of robotic arms. Design/methodology/approach A Takagi–Sugeno (T-S) fuzzy model, which is capable of approximating nonlinear systems, is used to describe the dynamics of a robotic arm. Model predictive control (MPC) based on the T-S fuzzy model is considered, which optimizes system performance with respect to a user-defined cost function. Findings The control gains are optimized online according to the real-time system state. Furthermore, the proposed method takes into account the input constraints. Simulations demonstrate the effectiveness of the fuzzy MPC approach. It is shown that asymptotic stability is achieved for the closed-loop control system. Originality/value The T-S fuzzy model is discussed in the modeling of robotic arm dynamics. Fuzzy MPC is used for robotic arm control, which can optimize the transient performance with respect to a user-defined criteria.

scholarly journals Analysis of the Influence of Suspension Actuator Limitations on Ride Comfort in Passenger Cars Using Model Predictive Control

Actuators ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 77 ◽  
Author(s):  
Erik Enders ◽  
Georg Burkhard ◽  
Nathan Munzinger
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Ride Comfort ◽  
Rate Of Change ◽  
High Rate ◽  
Passenger Cars ◽  
Wheel Load ◽  
Country Road ◽  
Necessary Constraints ◽  
Vehicle Dynamics Control

Active suspension systems help to deliver superior ride comfort and can be used to resolve the objective conflict between ride comfort and road-holding. Currently, there exists no method for analyzing the influence of actuator limitations, such as maximum force and maximum rate of change, on the achievable ride comfort. This research paper presents a method that is capable of doing this. It uses model predictive control to eliminate the influence of feedback controller performance and to integrate both actuator limitations and necessary constraints on dynamic wheel-load variation and suspension travel. Various scenarios are simulated, such as driving over a speed bump and inner city driving, as well as driving on a country road and motorway driving, using a state-of-the-art quarter-car model, parameterized for a luxury class vehicle. It is analyzed how comfort, or in one scenario road-holding, can be improved with consideration for the actuator limitations. The results indicate that actuator rate limitation has a strong influence on vertical vehicle dynamics control system performance, and that relatively small maximum forces of around 1000 to 2000 N are sufficient to successfully reject disturbances from road irregularities, provided the actuator is capable of supplying the forces at a sufficiently high rate of change.

scholarly journals Closed-loop control with unannounced exercise for adults with type 1 diabetes using the Ensemble Model Predictive Control

2019 ◽  
Vol 80 ◽  
pp. 202-210 ◽  
Author(s):  
Jose Garcia-Tirado ◽  
John P. Corbett ◽  
Dimitri Boiroux ◽  
John Bagterp Jørgensen ◽  
Marc D. Breton
Keyword(s):  
Type 1 Diabetes ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Ensemble Model ◽  
Loop Control

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

2020 ◽  
Vol 234 (9) ◽  
pp. 2330-2341
Author(s):  
Jiaxing Yu ◽  
Xiaofei Pei ◽  
Xuexun Guo ◽  
JianGuo Lin ◽  
Maolin Zhu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Stability Control ◽  
Robust Model Predictive Control ◽  
Direct Yaw Moment Control ◽  
Robust Model ◽  
Yaw Moment Control ◽  
Moment Control ◽  
The Stability ◽  
Yaw Moment

This paper proposes a framework for path tracking under additive disturbance when a vehicle travels at high speed or on low-friction road. A decoupling control strategy is adopted, which is made up of robust model predictive control and the stability control combining preview G-vectoring control and direct yaw moment control. A vehicle-road model is adopted for robust model predictive control, and a robust positively invariant set calculated online ensures state constraints in the presence of disturbances. Preview G-vectoring control in stability control generates deceleration and acceleration based on lateral jerk, later acceleration, and curvature at preview point when a vehicle travels through a cornering. Direct yaw moment control with additional activating conditions provides an external yaw moment to stabilize lateral motion and enhances tracking performance. A comparative analysis of stability performance of stability control is presented in simulations, and furthermore, many disturbances are considered, such as varying wind, road friction, and bounded state disturbances from motion planning and decision making. Simulation results show that the stability control combining preview G-vectoring control and direct yaw moment control with additional activating conditions not only guarantees lateral stability but also improves tracking performance, and robust model predictive control endows the overall control system with robustness.

scholarly journals Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control

2017 ◽  
Vol 19 (6) ◽  
pp. 331-339 ◽  
Author(s):  
Lauren M. Huyett ◽  
Trang T. Ly ◽  
Gregory P. Forlenza ◽  
Suzette Reuschel-DiVirgilio ◽  
Laurel H. Messer ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Zone Model ◽  
Closed Loop Control ◽  
Moderate Exercise ◽  
Loop Control

Model Predictive Control Application to Flexible-Bodied Railway Vehicles for Vibration Suppression

2013 ◽  
Vol 10 ◽  
pp. 25-35
Author(s):  
P.E. Orukpe
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Speed ◽  
Ride Comfort ◽  
The Body ◽  
Ride Quality ◽  
Linear Matrix ◽  
Railway Vehicle ◽  
Railway Vehicles ◽  
Effective Manner

In this paper, we apply model predictive control (MPC) based on mixed H2/H to active vibration control of the flexibility of railway vehicle to improve ride quality. However, the flexibility in the body of high-speed railway vehicles creates difficulties which in practice may result in the body structure being heavier than what it is supposed to be. The use of active suspension helps to model the vehicle and its flexibility in an effective manner. Conventional control approaches are compared with linear matrix inequality MPC technique using flexible-bodied railway vehicle as an example. The result indicates that the MPC technique performs better in improving ride comfort compared to the passive and classical techniques when flexible modes are present.

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