scholarly journals Gain Adaptation in Sliding Mode Control Using Model Predictive Control and Disturbance Compensation with Application to Actuators

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 182 ◽  
Author(s):  
Benedikt Haus ◽  
Paolo Mercorelli ◽  
Harald Aschemann
Keyword(s):  
Model Predictive Control ◽  
Sliding Mode Control ◽  
Predictive Control ◽  
Sliding Mode ◽  
Disturbance Compensation ◽  
Electric Actuator ◽  
Mode Control ◽  
Gain Adaptation ◽  
Error Dynamics ◽  
High Control

In this contribution, a gain adaptation for sliding mode control (SMC) is proposed that uses both linear model predictive control (LMPC) and an estimator-based disturbance compensation. Its application is demonstrated with an electromagnetic actuator. The SMC is based on a second-order model of the electric actuator, a direct current (DC) drive, where the current dynamics and the dynamics of the motor angular velocity are addressed. The error dynamics of the SMC are stabilized by a moving horizon MPC and a Kalman filter (KF) that estimates a lumped disturbance variable. In the application under consideration, this lumped disturbance variable accounts for nonlinear friction as well as model uncertainty. Simulation results point out the benefits regarding a reduction of chattering and a high control accuracy.

scholarly journals Optimal Control Algorithm of Constrained Fuzzy System Integrating Sliding Mode Control and Model Predictive Control

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Chonghui Song
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Sliding Mode Control ◽  
Predictive Control ◽  
Fuzzy System ◽  
Time Horizon ◽  
Controller Design ◽  
Sliding Mode ◽  
Mode Control ◽  
Existence And Stability

The sliding mode control and the model predictive control are connected by the value function of the optimal control problem for constrained fuzzy system. New conditions for the existence and stability of a sliding mode are proposed. Those conditions are more general conditions for the existence and stability of a sliding mode. When it is applied to the controller design, the design procedures are different from other sliding mode control (SMC) methods in that only the decay rate of the sliding mode motion is specified. The obtained controllers are state-feedback model predictive control (MPC) and also SMC. From the viewpoint of SMC, sliding mode surface does not need to be specified previously and the sliding mode reaching conditions are not necessary in the controller design. From the viewpoint of MPC, the finite time horizon is extended to the infinite time horizon. The difference with other MPC schemes is that the dependence on the feasibility of the initial point is canceled and the control schemes can be implemented in real time. Pseudosliding mode model predictive controllers are also provided. Closed loop systems are proven to be asymptotically stable. Simulation examples are provided to demonstrate proposed methods.

scholarly journals Trajectory tracking control for four-wheel independent drive intelligent vehicle based on model predictive control and sliding mode control

2021 ◽  
Vol 13 (9) ◽  
pp. 168781402110451
Author(s):  
HaiDong Wu ◽  
ZiHan Li ◽  
ZhenLi Si
Keyword(s):  
Model Predictive Control ◽  
Sliding Mode Control ◽  
Predictive Control ◽  
Driving Force ◽  
Sliding Mode ◽  
Steering Wheel ◽  
Intelligent Vehicle ◽  
Motion Controller ◽  
Mode Control ◽  
Yaw Moment

For four-wheel independent drive intelligent vehicle, the longitudinal and lateral motion control of the vehicle is decoupled and a hierarchical controller is designed: the upper layer is the motion controller, and the lower layer is the control distributor. In the motion controller, the model predictive control (MPC) is used to calculate the steering wheel angle and the total yaw moment for lateral control, and the sliding mode control (SMC) is used to calculate the total driving force for longitudinal control. In order to improve the control algorithm adaptability and the tracking accuracy at high speed, the UniTire model that can accurately express the complex coupling characteristics of tire under different working conditions are used and the numerical partial derivative of the state equation is used in MPC controller to ensure the feasibility of the algorithm. The control distributor distributes the total yaw moment and driving force calculated by the motion controller of the four wheels through the objective optimization function, and the constraints on road adhesion condition and the constraints on actuators are considered at the same time. A co-simulation platform is built in the CarSim/Simulink environment and the MPC-SMC controller is compared with the previously established MPC controller.

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