Recurrent CMAC Sliding Mode Adaptive Control for Flying Robot

2011 ◽  
Author(s):  
Qingwei Li ◽  
Hongun Duan
Keyword(s):  
Adaptive Control ◽  
Sliding Mode ◽  
Flying Robot

Function approximation-based sliding mode adaptive control

2008 ◽  
Vol 54 (3) ◽  
pp. 223-230 ◽  
Author(s):  
Yanyang Liang ◽  
Shuang Cong ◽  
Weiwei Shang
Keyword(s):  
Adaptive Control ◽  
Function Approximation ◽  
Sliding Mode

Unfalsified control design using a generalized cost function for a quadrotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Azam Hokmabadi ◽  
Mahdi Khodabandeh
Keyword(s):  
Adaptive Control ◽  
Cost Function ◽  
Sliding Mode ◽  
Control Design ◽  
Robust Adaptive Control ◽  
Operating Conditions ◽  
System Model ◽  
Reference Trajectory ◽  
Content Type ◽  
Generalized Cost

Purpose The purpose of this paper is to design a controller for a quadrotor only by using input–output data without a need for the system model. Design/methodology/approach Tracking control for the quadrotor is considered by using unfalsified control, which is one of the most recent strategies of robust adaptive control. The main assumption in unfalsified control design is that there is no access to the system model. Also, ideal path tracking and controlling the quadrotor are been paid attention to in the presence of external disturbances and uncertainties. First, unfalsified control method is introduced which is a data-driven and model-free approach in the field of adaptive control. Next, model of the quadrotor and unfalsified control design for the quadrotor are presented. Second, design of a control bank consisting of four proportional integral derivative controllers and a sliding mode controller is carried out. Findings A particular innovation on an unfalsified control algorithm in this paper is use of a generalized cost function in the hysteresis switching algorithm to find the best controller. Originality/value Finally, the performance and robustness of the designed controllers are investigated by simulation studies in various operating conditions including reference trajectory changes, facing to wind disturbance, uncertainty of the system and changes in payload, which show acceptable performances.

scholarly journals Indirect Adaptive Control for Higher Order Sliding Mode

2018 ◽  
Vol 51 (13) ◽  
pp. 591-596 ◽  
Author(s):  
Alexander Barth ◽  
Johann Reger ◽  
Jaime A. Moreno
Keyword(s):  
Adaptive Control ◽  
Sliding Mode ◽  
Higher Order ◽  
Indirect Adaptive Control

scholarly journals RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Pan Deng ◽  
Liangcai Zeng ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Sliding Mode ◽  
Model Simulation ◽  
Rbf Neural Network ◽  
Dynamic Pressure ◽  
Adaptive Controller ◽  
Pressure System ◽  
Output Pressure ◽  
Pressure Cylinder

According to the hydraulic principle diagram of the subgrade test device, the dynamic pressure cylinder electrohydraulic servo pressure system math model and AMESim simulation model are established. The system is divided into two parts of the dynamic pressure cylinder displacement subsystem and the dynamic pressure cylinder output pressure subsystem. On this basis, a RBF neural network backstepping sliding mode adaptive control algorithm is designed: using the double sliding mode structure, the two RBF neural networks are used to approximate the uncertainties in the two subsystems, provide design methods of RBF sliding mode adaptive controller of the dynamic pressure cylinder displacement subsystem and RBF backstepping sliding mode adaptive controller of the dynamic pressure cylinder output pressure subsystem, and give the two RBF neural network weight vector adaptive laws, and the stability of the algorithm is proved. Finally, the algorithm is applied to the dynamic pressure cylinder electrohydraulic servo pressure system AMESim model; simulation results show that this algorithm can not only effectively estimate the system uncertainties, but also achieve accurate tracking of the target variables and have a simpler structure, better control performance, and better robust performance than the backstepping sliding mode adaptive control (BSAC).

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