Adaptive Fuzzy Event-Triggered Control for Single-Link Flexible-Joint Robots With Actuator Failures

Diao, Shuzhen; Sun, Wei; Su, Shun‐Feng; Xia, Jianwei

doi:10.1109/tcyb.2021.3049536

Cited by 60 publications

(25 citation statements)

References 41 publications

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“…The constitution of the proposed hierarchical control procedure only requires the a priori identification of the systems linear state-space model and pre-calibrated weight-adjusting functions. Thus, apart from the self-stabilizing mechatronic platforms, the proposed control schemes can be easily extended to flexible-joint robotic manipulators and other classes on under-actuated systems as well [ 74 ].…”

Section: Comparative Performance Assessmentmentioning

confidence: 99%

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

2021

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This paper presents an experimental comparison of four different hierarchical self-tuning regulatory control procedures in enhancing the robustness of the under-actuated systems against bounded exogenous disturbances. The proposed hierarchical control procedure augments the ubiquitous Linear-Quadratic-Regulator (LQR) with an online reconfiguration block that acts as a superior regulator to dynamically adjust the critical weighting-factors of LQR’s quadratic-performance-index (QPI). The Algebraic-Riccati-Equation (ARE) uses these updated weighting-factors to re-compute the optimal control problem, after every sampling interval, to deliver time-varying state-feedback gains. This article experimentally compares four state-of-the-art rule-based online adaptation mechanisms that dynamically restructure the constituent blocks of the ARE. The proposed hierarchical control procedures are synthesized by self-adjusting the (i) controller’s degree-of-stability, (ii) the control-weighting-factor of QPI, (iii) the state-weighting-factors of QPI as a function of “state-error-phases”, and (iv) the state-weighting-factors of QPI as a function of “state-error-magnitudes”. Each adaptation mechanism is formulated via pre-calibrated hyperbolic scaling functions that are driven by state-error-variations. The implications of each mechanism on the controller’s behaviour are analyzed in real-time by conducting credible hardware-in-the-loop experiments on the QNET Rotary-Pendulum setup. The rotary pendulum is chosen as the benchmark platform owing to its under-actuated configuration and kinematic instability. The experimental outcomes indicate that the latter self-adaptive controller demonstrates superior adaptability and disturbances-rejection capability throughout the operating regime.

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Section: Comparative Performance Assessmentmentioning

confidence: 99%

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

2021

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“…To compensate the uncertainty of FJR systems, several effective approaches, such as extended state observer (ESO) [23]- [25], time-delay estimation [26]- [27], neural network (NN) [28]- [29], fuzzy logic system (FLS) [30]- [31], etc. To avoid the problem of "explosion of complexity" in the works [29]- [31], Joo et al [32] and Miao et al [33] introduced a first-order filter into the backstepping design such that the differential operation of virtual control law is replaced by the algebraic operation. The other implicit benefit of such an VOLUME 10, 2021 operation is that the input dimensionality of NN or FLS is reduced greatly [34].…”

Section: Introductionmentioning

confidence: 99%

“…For such an issue, the eventtriggered control (ETC) [44]- [45] method has its preponderance, in which the control commands are transmitted only if a certain event is triggered [46]- [49]. Based on this idea, the event-triggered control for FJR systems has attracted the attention of many scholars [50]- [51]. In [52], an eventtriggered adaptive asymptotically tracking control solution for a single-Link Robot was proposed, in which the transmitting and computation burdens of control commands are effectively reduced by using the DSC and ETC techniques.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation

2022

IEEE Access

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This paper investigates an event-triggered adaptive neural tracking control issue for flexiblejoint robot (FJR) systems subject to unknown dynamic and input saturation. To enable the backstepping design framework to be implemented, the input saturation nonlinearity is replace by a smooth function. In the control design, the dynamic surface control (DSC) and adaptive neural techniques are used to handle the "explosion of complexity" issue and unknown dynamics, respectively. Furthermore, to reduce the calculated burden caused by the adaptive neural reconstruction technique, three virtual parameters are updated by using the single-parameter-learning approach. To decrease the frequency of actuator response to the control command for reducing the mechanical wear of actuator, an event triggering mechanism is established between the control law and actuator. Finally, an event-triggered adaptive neural tracking control solution is proposed, which is endowed the advantages as: (1) it does not need any priori knowledge of FJR systems; 2) it only needs to update three unknown parameters; 3) it can reduce the transmission frequency of the control commands and the response rate of the actuator. Using the Lyapunov stability theory, the proposed event-triggered control solution ensures that all signals of the closed-loop tracking control system are bounded. Finally, the simulation results verify the effectiveness and superiority of the proposed control scheme.

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“…The ETC approach is also used in networked Euler-Lagrangian problem formulations. [15][16][17] An eventtriggered (ET) robust trajectory tracking control architecture is studied for a class of uncertain EL systems in Kumari et al 15 A fuzzy adaptive fault-tolerant ETC approach is studied in Diao et al 16 to satisfy the finitetime tracking of single-link flexible-joint robot systems. By utilizing an ETC approach in Bu et al, 17 the networked tracking problem is investigated for an RM subject to system uncertainties and external disturbance.…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing an ETC approach in Bu et al, 17 the networked tracking problem is investigated for an RM subject to system uncertainties and external disturbance. However, a single RM problem formulation is considered in Kumari et al, 15 Diao et al, 16 and Bu et al 17 and the cooperative performance of a networked MAS is not delved into.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered formation control of n-link networked stochastic robotic manipulators

Azarbahram

Pariz

Sistani

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes an event-triggered control framework to satisfy the tracking formation performance for a group of uncertain non-linear n-link robotic manipulators. The robotic manipulators are configured as a multi-agent system and they communicate over a directed graph (digraph). Furthermore, the non-linear robotic manipulator-multi-agent systems are subject to stochastic environmental loads. By introducing extra virtual controllers in the final step of the backstepping design, a total number of n event-triggering mechanisms are introduced independently for each link of all the robotic manipulator agents to update the control inputs in a fully distributed manner. More precisely, the actuator of each link of a particular agent is capable of being updated independent of other link actuator updates. A rigorous proof of the convergence of all the closed-loop signals in probability is then given and the Zeno phenomenon is excluded for the control event-triggered architectures. The simulation experiments finally quantify the effectiveness of proposed approach in terms of reducing the number of control updates and handling the stochastic environmental loads.

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Adaptive Fuzzy Event-Triggered Control for Single-Link Flexible-Joint Robots With Actuator Failures

Cited by 60 publications

References 41 publications

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

Event-Triggered Adaptive Neural Tracking Control of Flexible-Joint Robot Systems With Input Saturation

Event-triggered formation control of n-link networked stochastic robotic manipulators

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