Discrete‐time modified repetitive sliding mode control for uncertain linear systems

Kurniawan, Edi; Wang, Hai; Sirenden, Bernadus H.; Prakosa, Jalu A.; Adinanta, Hendra; Suryadi, Suryadi

doi:10.1002/acs.3316

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…The not optimal result would be achieved, but the worst outcome would be treated by excessive weighting. The control inputs in equation (12), for example in 0° point, should be observed further to analyze the cause of overshoot and unstable plant.…”

Section: Resultsmentioning

confidence: 99%

“…The application of the H-infinity optimal control [3] approach was analyzed to solve highorder unmodeled dynamics on a helicopter. Sliding Mode Control [11] was simulated and studied for a laboratory helicopter of 3 DOF [12]. Robust LQR attitude control [13], [14] was built for aggressive maneuvers on 3 DOF helicopters, both theory and experiment [15].…”

Section: Introductionmentioning

confidence: 99%

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Experimental studies of linear quadratic regulator (LQR) cost matrices weighting to control an accurate take-off position of bicopter unmanned aerial vehicles (UAVs)

Prakosa

Wang

Kurniawan

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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Controller design for airplane flight control is challenged to achieve an optimum result, particularly for safety purposes. The experiment evaluated the linear quadratic regulator (LQR) method to research the optimal gain of proportional-integral-derivative (PID) to hover accurately the bicopter model by minimizing error. The 3 degree of freedom (DOF) helicopter facility is a suitable bicopter experimental simulator to test its complex multiple input multiple output (MIMO) flight control model to respond to the challenge of multipurpose drone control strategies. The art of LQR setting is how to search for appropriate cost matrices scaling to optimize results. This study aims to accurately optimize take-off position control of the bicopter model by investigating LQR cost matrices variation in actual experiments. From the experimental results of weighted matrix variation on the bicopter simulator, the proposed LQR method has been successfully applied to achieve asymptotic stability of roll angle, although it yielded a significant overshoot. Moreover, the overshoot errors had good linearity to weighting variation. Despite that, the implementation of cost matrices is limited in the real bicopter experiment, and there are appropriate values for achieving an optimal accuracy. Moreover, the unstable step response of the controlled angle occurred because of excessive weighting.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental studies of linear quadratic regulator (LQR) cost matrices weighting to control an accurate take-off position of bicopter unmanned aerial vehicles (UAVs)

Prakosa

Wang

Kurniawan

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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“…Theorem 1. Considering the uncertain nonlinear systems (1), if recursive terminal sliding mode surface is designed as ( 5), (7), and control law is chosen as (13), the tracking error will converge to zero in a finite time.…”

Section: Assumptionmentioning

confidence: 99%

“…Therefore, it is necessary to study the robust control system of uncertain nonlinear systems. To facilitate such challenges, numerous researchers have made great efforts to find appropriate robust controllers for improving control performance, such as sliding mode control (SMC), 1,2 fuzzy control, 3 adaptive control, 4 back-stepping control 5 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Neural network based recursive terminal sliding mode control and its application to active power filters

Chu

Hou

et al. 2022

Adaptive Control & Signal

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Summary In recent times, research interests for the control design of uncertain nonlinear systems are observed in neuroscience‐inspired intelligent controllers. In this article, a recursive terminal sliding mode control scheme based on continuous radial basis emotional neural network (CRBENN) is proposed. First, a recursive terminal sliding mode controller is constructed. The sliding mode surface is composed of a fast non‐singular terminal sliding mode surface and a recursive integral terminal sliding mode surface, which not only ensures that the tracking error converges to zero in a finite time, but also can eliminate the reaching phase and achieve global robustness by setting the surface parameters with appropriate initial values. In addition, in order to effectively deal with the uncertainty, an adaptive CRBENN controller with online parameter adjustment capability is established, and its stability and convergence are analyzed using the Lyapunov method. The designed CRBENN, inspired by neuroscience, is simple in structure and fast in response. Simulation and experimental results on active power filter show that the control scheme has good current tracking ability and anti‐interference ability.

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“…In addition, many researchers have also made attempts to study this aspect. Kurniawan proposed an improved design strategy for repetitive sliding mode controllers, which can be used for heading control to accurately track reference signals [14]; Mashhad designed H ∞ using the linearized autonomous underwater vehicle (AUV) model Robust controller and simulation to verify its effectiveness for AUV heading control [15]; and Zhang Q. et al proposed a nonlinear fuzzy control algorithm for ship heading keeping based on feedback linearization using the approximation ability of the fuzzy system constructed by radial basis function neural networks [16]. The above research is only limited to the study of heading control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Heading Planning and Control Method of Unmanned Underwater Vehicles for Tunnel Detection

Xia

Cui²,

Chu

et al. 2023

JMSE

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To address the challenge of unmanned underwater vehicle (UUV) autonomous navigation in long-distance underwater tunnel detection tasks and improve the control performance of its heading control system, a method of autonomous heading planning and control based on sonar-ranging feedback control was proposed. This method combines UUV’s autonomous heading planning technology with the heading proportion-integral-derivative (PID) control algorithm, optimizing the acquisition method of controller input data, to impart specific adaptive characteristics to the controller. Using the ranging principle of ultrasonic spontaneous self-collection, it is possible to obtain the yaw direction and angle of the vehicle relative to the target heading in the tunnel and continuously adjust the control law to change the heading as the vehicle’s heading status changes during navigation. The effectiveness of the autonomous heading planning and control method is verified through pool experiments. The analysis and experimental results show that the proposed heading planning method achieves good control effect in UUV’s underwater tunnel detection heading control, and exhibits obvious advantages in long-distance closed tunnel environments. UUV can adaptively adjust the heading according to the tunnel environment and has a fast response and strong applicability in planning and controlling the heading.

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Discrete‐time modified repetitive sliding mode control for uncertain linear systems

Cited by 12 publications

References 29 publications

Experimental studies of linear quadratic regulator (LQR) cost matrices weighting to control an accurate take-off position of bicopter unmanned aerial vehicles (UAVs)

Experimental studies of linear quadratic regulator (LQR) cost matrices weighting to control an accurate take-off position of bicopter unmanned aerial vehicles (UAVs)

Neural network based recursive terminal sliding mode control and its application to active power filters

Autonomous Heading Planning and Control Method of Unmanned Underwater Vehicles for Tunnel Detection

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