A Model-Free Control System Based on the Sliding Mode Control Method with Applications to Multi-Input-Multi-Output Systems

Tin, Fares El

doi:10.11159/cdsr17.119

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…A similar approach performed in [4], [5], [6], and [7] is used to derive the MFSMC for a quadcopter. The quadcopter is a 6 Degree-of-Freedom (DOF) 2 nd -order system.…”

Section: Model-free Sliding Mode Controllermentioning

confidence: 99%

“…A quadcopter is a 6 DOF system traveling linearly in the X, Y and Z direction and rotating about X (Roll, Ф), Y (Pitch, ϴ) and Z (Yaw, ψ). The dynamic model of the quadcopter used in this work is the same used in [6][7][8][9]…”

Section: Control Approach For a Quadcoptermentioning

confidence: 99%

“…The quadcopter model is an underactuated system since there are only four control inputs for six system states. El Tin used a transformation matrix to handle the underactuation of the quadcopter for simulation study [6]. Another approach to handle underactuation is to use the super twisting method suggested by Derafa [9] as was used in [2].…”

Section: Control Approach For a Quadcoptermentioning

confidence: 99%

“…Reis also suggested the tuning method of the control algorithm in the presence of noise and successfully implemented the algorithm on nonlinear and linear SISO systems. El Tin [6] further extended this work on Multi-Input-Multi-Output (MIMO) systems. He successfully used the control algorithm for quadrotor positon control and investigated the issue of actuator dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…The work developed in this paper is to successfully implement the MFSMC derived in [4], [5], [6], and [7] on a realtime quadcopter type Unmanned Aircraft System (UAS) and compare the tracking performance and power consumption to a traditional PID-type controller. Simulations characterising the quadcopter hardware is performed prior to targeting the realtime model.…”

Section: Introductionmentioning

confidence: 99%

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A Model-Free Control Algorithm Based On The Sliding Mode Control Method With Applications to Unmanned Aircraft Systems

Sreeraj¹,

Kaputa²,

Crassidis³

2019

International Conference of Control, Dynamic Systems, and Robotics

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Control methods require the use of a system model for the design and tuning of the controllers in meeting and/or exceeding the control system performance objectives. However, system models contain errors and uncertainties that also may be complex to develop and to generalize for a large class of systems such as those for unmanned aircraft systems. In particular, the sliding control method is a superior robust nonlinear control approach due to the direct handling of nonlinearities and uncertainties that can be used in tracking problems for unmanned aircraft system. However, the derivation of the sliding mode control law is tedious since a unique and distinct control law needs to be derived for every individual system and cannot be applied to general systems that may encompass all classifications of unmanned aircraft systems. In this paper, a model-free control algorithm based on the sliding mode control method is developed and generalized for all classes of unmanned aircraft systems used in robust tracking control applications. The model-free control algorithm is derived with knowledge of the system's order, state measurements, and control input gain matrix shape and bounds and is not dependent on a mathematical system model. The derived control law is tested using a high-fidelity simulation of a quadrotortype unmanned aircraft system and the results are compared to a traditional PID controller for tracking performance and power consumption. Realistic type hardware inputs from joysticks and IMUs was simulated for the analysis. Finally, the model-free control algorithm was implemented on a quadrotor-type unmanned aircraft system testbed used in real flight experimental testing. The experimental tracking performance and power consumption was analysed and compared to a traditional PID-type controller.

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“…A similar approach performed in [4], [5], [6], and [7] is used to derive the MFSMC for a quadcopter. The quadcopter is a 6 Degree-of-Freedom (DOF) 2 nd -order system.…”

Section: Model-free Sliding Mode Controllermentioning

confidence: 99%

Section: Control Approach For a Quadcoptermentioning

confidence: 99%

Section: Control Approach For a Quadcoptermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Model-Free Control Algorithm Based On The Sliding Mode Control Method With Applications to Unmanned Aircraft Systems

Sreeraj¹,

Kaputa²,

Crassidis³

2019

International Conference of Control, Dynamic Systems, and Robotics

Self Cite

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DC microgrid voltage stability by Model Free Super-Twisting Sliding Mode Control

Kassir

Doumiati

Machmoum

et al. 2021

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

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Model-Free Sliding Mode Control Algorithms including Application to a Real-World Quadrotor

Schulken¹,

Crassidis²

2018

International Conference of Control, Dynamic Systems, and Robotics

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Sliding mode control is a robust nonlinear control algorithm that has been used to implement tracking controllers for unmanned aircraft systems that are robust to modeling uncertainty and exogenous disturbances, thereby providing excellent performance for autonomous operation. A significant advance in the application of sliding mode control for unmanned aircraft systems would be adaptation of a model-free sliding mode control algorithm, since the most complex and time-consuming aspect of implementation of sliding mode control is the derivation of the control law with incorporation of the system model, a process required to be performed for each individual application of sliding mode control. The performance of various model-free sliding mode control algorithms was compared in simulation using a variety of aerial system models and real-world disturbances (e.g. the effects of discretization and state estimation). The two best performing algorithms were shown to exhibit very similar behavior. These two algorithms were implemented on a quadrotor (both in simulation and using real-world hardware) and the performance was compared to a traditional PID based controller using the same state estimation algorithm and control setup. Simulation results show the model-free sliding mode control algorithms exhibit similar performance to PID controllers without the tedious tuning process. Comparison between the two model-free sliding mode control algorithms showed very similar performance as measured by the quadratic means of tracking errors. Flight testing showed that while a model-free sliding mode control algorithm can control real-world hardware, further characterization and significant improvements are required before it is a viable alternative to conventional control algorithms. Large tracking errors were observed for both the model-free sliding mode control and PID based flight controllers and the performance was characterized as unacceptable for most applications. The poor performance of both controllers suggests tracking errors can be attributed to errors in state estimation. Further testing with improved state estimation would allow for more conclusions to be drawn.

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A Model-Free Control System Based on the Sliding Mode Control Method with Applications to Multi-Input-Multi-Output Systems

Cited by 4 publications

References 5 publications

A Model-Free Control Algorithm Based On The Sliding Mode Control Method With Applications to Unmanned Aircraft Systems

A Model-Free Control Algorithm Based On The Sliding Mode Control Method With Applications to Unmanned Aircraft Systems

DC microgrid voltage stability by Model Free Super-Twisting Sliding Mode Control

Model-Free Sliding Mode Control Algorithms including Application to a Real-World Quadrotor

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