Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Radu-Emil Precup ◽  
Raul-Cristian Roman ◽  
Elena-Lorena Hedrea ◽  
Emil M. Petriu ◽  
Claudia-Adina Bojan-Dragos
Keyword(s):  
Fuzzy Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Data Driven ◽  
Control Algorithms ◽  
Control Law ◽  
Control Structures ◽  
Mode Control ◽  
Model Free ◽  
Model Free Control

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment.

scholarly journals Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons

Algorithms ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 2
Author(s):  
Emanuel Chereji ◽  
Mircea-Bogdan Radac ◽  
Alexandra-Iulia Szedlak-Stinean
Keyword(s):  
Sliding Mode Control ◽  
Process Model ◽  
Sliding Mode ◽  
Superior Performance ◽  
Control Algorithms ◽  
Sliding Mode Controller ◽  
Tuning Parameters ◽  
Mode Control ◽  
Model Free ◽  
Performance Comparisons

This paper presents the performance of two sliding mode control algorithms, based on the Lyapunov-based sliding mode controller (LSMC) and reaching-law-based sliding mode controller (RSMC), with their novel variants designed and applied to the anti-lock braking system (ABS), which is known to be a strongly nonlinear system. The goal is to prove their superior performance over existing control approaches, in the sense that the LSMC and RSMC do not bring additional computational complexity, as they rely on a reduced number of tuning parameters. The performance of LSMC and RSMC solves the uncertainty in the process model which comes from unmodeled dynamics and a simplification of the actuator dynamics, leading to a reduced second order process. The contribution adds complete design details and stability analysis is provided. Additionally, performance comparisons with several adaptive, neural networks-based and model-free sliding mode control algorithms reveal the robustness of the proposed LSMC and RSMC controllers, in spite of the reduced number of tuning parameters. The robustness and reduced computational burden of the controllers validated on the real-world complex ABS make it an attractive solution for practical industrial implementations.

Data-driven model-free adaptive fractional-order sliding mode control for the SMA actuator with prescribed performance

2022 ◽  
pp. 095440622110597
Author(s):  
Hongshuai Liu ◽  
Lina Hao ◽  
Mingfang Liu ◽  
Zhirui Zhao
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Data Driven ◽  
Sliding Mode Controller ◽  
Prescribed Performance ◽  
Sma Actuators ◽  
Mode Control ◽  
Model Free ◽  
Sma Actuator

In this paper, a novel data-driven model-free adaptive fractional-order sliding mode controller with prescribed performance is proposed for the shape memory alloy (SMA) actuator. Due to the strong asymmetric saturated hysteresis nonlinear characteristics of the SMA actuators, it is not easy to establish an accurate model and develop an effective controller. Therefore, we present a controller without using the model of the SMA actuators. In other words, the proposed controller depends merely on the input/output (I/O) data of the SMA actuators. To obtain the reasonable compensation for hysteresis, enhance the noise robustness of the controller, and reduce the chattering, a fractional-order sliding mode controller with memory characteristics is employed to improve the performance of the controller. In addition, the prescribed performance control (PPC) strategy is introduced in our work to guarantee the tracking errors converge to a sufficiently small boundary and the convergence rate is not less than a predetermined value which are significant and considerable in practical engineering applications of the SMA actuator. Finally, experiments are carried out, and results reveal the effectiveness and success of the proposed controller. Comparisons with the classical Proportional Integral Differential (PID), model-free adaptive control (MFAC), and model-free adaptive sliding mode control (MFAC-SMC) are also performed.

scholarly journals Data Driven Model-Free Adaptive Control Method for Quadrotor Formation Trajectory Tracking Based on RISE and ISMC Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1289
Author(s):  
Dongdong Yuan ◽  
Yankai Wang
Keyword(s):  
Adaptive Control ◽  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Sliding Mode ◽  
Data Driven ◽  
Trajectory Tracking Control ◽  
Mode Control ◽  
Model Free

In order to solve the problems of complex dynamic modeling and parameters identification of quadrotor formation cooperative trajectory tracking control, this paper proposes a data-driven model-free adaptive control method for quadrotor formation based on robust integral of the signum of the error (RISE) and improved sliding mode control (ISMC). The leader-follower strategy is adopted, and the leader realizes trajectory tracking control. A novel asymptotic tracking data-driven controller of quadrotor is used to control the system using the RISE method. It is divided into two parts: The inner loop is for attitude control and the outer loop for position control. Both use the RISE method in the loop to eliminate interference and this method only uses the input and output data of the unmanned aerial vehicle(UAV) system and does not rely on any dynamics and kinematics model of the UAV. The followers realize formation cooperative control, introducing adaptive update law and saturation function to improve sliding mode control (SMC), and it eliminates the general SMC algorithm controller design dependence on the mathematical model of the UAV and has the chattering problem. Then, the stability of the system is proved by the Lyapunov method, and the effectiveness of the algorithm and the feasibility of the scheme are verified by numerical simulation. The experimental results show that the designed data-driven model-free adaptive control method for the quadrotor formation is effective and can effectively realize the coordinated formation trajectory tracking control of the quadrotor. At the same time, the design of the controller does not depend on the UAV kinematics and dynamics model, and it has high control accuracy, stability, and robustness.

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