Adaptive Robust Control for a Spatial Flexible Timoshenko Manipulator Subject to Input Dead-Zone

Chen, Shouyan; Zhao, Zhijia; Zhu, Dachang; Zhang, Chunliang; Li, Han-Xiong

doi:10.1109/tsmc.2020.3020326

Cited by 17 publications

(12 citation statements)

References 49 publications

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“…The robot joints are actuated by DC motors. This system is dynamically modeled as (8), and the details are as follows [42]:…”

Section: Results and Analysismentioning

confidence: 99%

“…To eliminate the drawbacks of model-based methods, model-free approaches for controlling robotic arms have been proposed. These controllers are mainly designed based on adaptive methods, fuzzy logic, or neural networks [4, 5, 6, 7, 8]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…In ref. [8], an adaptive torque-based controller was developed for the Timoshenko arm subject to input dead zone. The uniformly ultimately stability of the controlled system was assured using the Lyapunov stability theorem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

2022

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This article proposes a robust and adaptive controller for industrial robot arms with multiple degrees of freedom without the need for velocity measurement. Many of the controllers designed for manipulators are model-based and require detailed knowledge of the system model. In contrast to these methods, this paper proposes a model-free controller using the Philips q-Bernstein operator as universal approximator. The designed controller can approximate uncertainties including external disturbances and unmodeled dynamics based on its universal approximation capability. Besides, most of the controllers revealed for robot arms are torque-based, which is not a realistic presumption from a practical point of view. In the proposed control method, the voltage applied to the actuator is considered as the control signal. However, unlike many voltage-based methods, the need to know the exact models of the system and the actuator has been eliminated in the presented method. Also, adaptive rules are extracted during the Lyapunov analysis to ensure system stability. Finally, to analyze the performance of the presented controller, this method is simulated for an industrial robot arm, and the results are analyzed. The proposed methodology is also compared to those of a strong state-of-the-art approximator, the Chebyshev neural network.

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“…The robot joints are actuated by DC motors. This system is dynamically modeled as (8), and the details are as follows [42]:…”

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

2022

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“…26 As another boundary control-based approach, Chen et al regulated the angular position and suppressed the oscillations of a flexible space Timoshenko manipulator subjected to input dead-zone using an adaptive robust control method. 27 In control method formulation, the actuators are usually considered ideal elements, while each actuator depending on its structure can suffer from some imperfections such as backlash, hysteresis, bandwidth limitation, and control input saturation. Zhao et al considered the actuator failures and backlash-like hysteresis in the problem of angular position regulation and vibration suppression of a flexible Timoshenko manipulator subjected to external disturbances using an adaptive boundary control method.…”

Section: Introductionmentioning

confidence: 99%

Feedback control of actuation‐constrained moving structure carrying Timoshenko beam

Homaeinezhad

Gavari

2022

Intl J Robust & Nonlinear

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This article deals with tracking control and active vibration suppression of a hybrid rigid‐elastic vibrational mobile system with a nonlinear elastic foundation in the presence of actuator bandwidth limitation and control input saturation. The system consists of a rigid rectilinearly mobile frame on an elastic foundation, and a flexible Timoshenko beam clamped to it with a heavy attached concentrated mass. The hybrid partial differential equation (PDE)‐ordinary differential equation (ODE) system of the plant is obtained using Hamilton's principle and then is converted to a system of ODEs using the assumed mode method (AMM). Next, the model will be discretized in the time domain using the two‐step Adams‐Bashforth technique. Considering the first‐order dynamics to cover the bandwidth limitation of the actuator, a discrete‐time sliding mode controller (DSMC) is designed based on a positive definite Lyapunov functional. Following that, an innovative optimal predictive algorithm is proposed to compensate for the effects of input saturation. The proposed algorithm is based on a positive definite cost function and using an intelligent regime of optimizations and decisions, minimizes the cost function in a predefined prediction horizon. The vibratory behavior of the system also is included in the mentioned cost function. Consequently, the overall control scheme called optimal rigid‐elastic interaction control (OREIC) will be able to follow the desired tracking trajectory, suppress the beam vibrations, and compensate for the effect of bandwidth limitation as well as the effect of input saturation. The performance of the OREIC algorithm is compared with a saturated DSMC controller and the simulation results demonstrate the efficiency of the method in vibration control of mobile rigid‐elastic systems subjected to actuator bandwidth frequency and saturation limitations.

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“…Chen et al proposed an adaptive robust spatial vibration control for a flexible Timoshenko manipulator subject to input dead-zone nonlinearity characteristic. 3 However, adaptive techniques do not exhibit robustness against unstructured uncertainties. The same criticism applies to the studies conducted in.…”

Section: Introductionmentioning

confidence: 99%

Tracking control of moving flexible system by incorporation of feedback-based vibrational energy sink in model predictive control algorithm

Homaeinezhad

FotoohiNia

2022

Proceedings of the Institution of Mechanical Engineers, Part K:

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This paper proposes a new approach towards the nonlinear problem of tracking control for mobile multivariable vibrational structure featuring an Euler-Bernoulli vibrational beam. The control scheme involves obtaining interactions between flexural and rigid motions of the multivariable continuum mechanics system with actuator force limitation both regarding domain and frequency bandwidth. To this end, the boundary control inputs are selected prioritizing two tasks, satisfying actuator limitations and obtaining minimum possible tracking error. The former is employed as hard constraint in model predictive control (MPC) scheme while the latter is considered as soft constraint. The control scheme is adjusted such that when actuator limits are exceeded, the soft constraints are relaxed to maintain stability. In order to provide smooth tracking, Euler-Bernoulli transverse vibration is attenuated by incorporating Feedback Vibrational Energy Sink (FVES) method in obtaining control law. Hence, actuators are manipulated such that total vibrational energy of flexible beam is damped without using passive or conventional active dampers. The accuracy of the proposed method has been verified via FEA-based simulations.

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Adaptive Robust Control for a Spatial Flexible Timoshenko Manipulator Subject to Input Dead-Zone

Cited by 17 publications

References 49 publications

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

Feedback control of actuation‐constrained moving structure carrying Timoshenko beam

Tracking control of moving flexible system by incorporation of feedback-based vibrational energy sink in model predictive control algorithm

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