Optimization of Sliding Mode Control to Save Energy in a SCARA Robot

Soriano, Luis Arturo; Rubio, José de Jesús; Orozco, Eduardo; Cordova, Daniel Andres; Ochoa, Genaro; Balcazar, Ricardo; Cruz, David Ricardo; Meda-Campaña, J. A.; Zacarías, Alejandro; Gutiérrez, Guadalupe Juliana

doi:10.3390/math9243160

Cited by 40 publications

(12 citation statements)

References 38 publications

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“…The majority of nonlinearities arise from nonlinear magnetization characteristics, shaft position, and velocity. For SMC controller design, the unknown disturbance d is assumed to be bound by value D. The value D is significant by selecting the SMC gain, which influences the chattering phenomena at the controller's output directly [22][23][24][25][26].…”

Section: Problem Formulationmentioning

confidence: 99%

Toward Embedded System Resources Relaxation Based on the Event-Triggered Feedback Control Approach

Sarjaš

Gleich

2022

Mathematics

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The paper describes an event-triggered nonlinear feedback controller design. Event triggering is a real-time controller implementation technique which reduces embedded system utilization and relaxes task scheduling of the real-time system. In contrast to classic time implementation techniques, the event-triggered execution is validated regarding the introduced triggering policy. The triggering rule is a boundary, where the last task value is preserved until the rule is violated. In the given paper, two different event-triggered strategies are designed for the class of dynamic systems with integral behavior. Both methods are based on sliding mode controller design, where the triggering rule of the first design involves only a partial state vector, which is a direct consequence of the triggering rule derivation throughout the Lyapunov stability analysis. In the second approach, the sliding mode controller is designed upon prior stabilized systems with the additional term, which enables derivation of the triggering rule based on the whole state vector. The second approach offers better closed-loop performance and higher relaxation of the system utilization. The selection of triggering boundary is related closely to the derived minimal inter-event time, which impacts the computational burden of the real-time system and closed-loop performance directly. The derived controllers are compared with the classic sample and hold implementation techniques. The real-time results are presented, and system performances are confirmed regarding embedded system task relaxation, lowering the computational intensity and preserving closed-loop dynamics.

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Section: Problem Formulationmentioning

confidence: 99%

Toward Embedded System Resources Relaxation Based on the Event-Triggered Feedback Control Approach

Sarjaš

Gleich

2022

Mathematics

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“…Designing a robust controller for uncertain systems that can accommodate such uncertainties has always been an important yet challenging problem. To retain the robustness property, various robust control methods have been developed against model uncertainties [3][4][5][6][7][8]. Conventional robust control methods, however, were studied mainly via offline means.…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning

2022

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This study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be resolved using the regulation control strategy. Then, the robust control problem of uncertain system can be represented as an optimal control problem of nominal system, which provides a new pathway to address the robust control problem. To realize the optimal control, the derived Hamilton–Jacobi–Bellman equation (HJBE) is solved by training a critic neural network (CNN). Finally, two innovative critic learning techniques are suggested to calculate the unknown NN weights, where the convergence of NN weights can be guaranteed. Simulations are carried out to demonstrate the effectiveness of the proposed method.

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“…Moreover, the SNSNS are widely used and reported in many physical systems. [8][9][10][11][12][13][14][15][16][17][18] It is significant and challenging to design the controller for such system.…”

Section: Introductionmentioning

confidence: 99%

“…For SNSNS, all the system functions contain the whole state variables, and there is no affine appearance of the variables to be utilized as controller design. Moreover, the SNSNS are widely used and reported in many physical systems 8‐18 . It is significant and challenging to design the controller for such system.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

Ren

Liu

2022

Adaptive Control & Signal

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Summary This article is concerned with the issue of event‐triggered adaptive fixed‐time control with sensor and actuator faults for stochastic nontriangular structure nonlinear system (SNSNS). The main difficulty in this article is the design of a novel and stable controller for SNSNS to compensate for the effect of sensor and actuator faults with event‐triggered control (ETC) in a unified framework. The mean value theorem (MVT) and ETC method are introduced to solve the questions of nonaffine structure and communication burden. Meanwhile, with the aid of adaptive neural network and fixed‐time theory, a novel controller is proposed by backstepping technology, which can ensure all the closed‐loop signals are bounded in probability within a fixed time as well avoid Zeno behavior. At last, the simulation is carried out to demonstrate the effectiveness of the proposed scheme.

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Optimization of Sliding Mode Control to Save Energy in a SCARA Robot

Cited by 40 publications

References 38 publications

Toward Embedded System Resources Relaxation Based on the Event-Triggered Feedback Control Approach

Toward Embedded System Resources Relaxation Based on the Event-Triggered Feedback Control Approach

Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

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