Neuroadaptive Cooperative Control Without Velocity Measurement for Multiple Humanoid Robots Under Full-State Constraints

Zhang, Zhirong; Song, Yongduan; Zhao, Kai

doi:10.1109/tie.2018.2844791

Cited by 42 publications

(40 citation statements)

References 25 publications

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“…In [9], the formation control for a group of nonlinear uncertain mechanical systems is achieved by the cooperative deterministic learning. In [10], the neuro-adaptive cooperative control for the multiple humanoid robots under full-state constraints is designed. In [11], human-guided multi-robot cooperative manipulation is accomplished.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the cooperative control of three quadrotors for transporting an unknown suspended load is accomplished. The abovementioned approaches are summarized as follows: (i) much computation time [1], [3]- [6], [9], [10], (ii) a necessity of state or disturbance observer [9], [12], [14], and (iii) only bounded tracking error [1], [3].…”

Section: Introductionmentioning

confidence: 99%

“…As the operating point is in the vivinity of the zero filtering tracking error, nonlinear filtering gains are increasing to accelerate its tracking ability such that robustness is enhanced [31]- [34]. Afterwards the mathematical model for the multiple arms of multiple (humanoid) robots [9], [10] is employed to design the cooperative task using the proposed AFTFTCC.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed adaptive finite-time fault-tolerant constrained control (AFTTFTCC) for the tracking control of robot manipulator and the cooperative control of multiple robots with grasped object includes the following advantageous features: (i) without the condition of skew-symmetric matrix (cf., Properties P2 and P3) as compared with some previous studies [3], [4], [21]; (ii) without an extra addition of persistent signal [26] to accelerate the parameter convergence and without the true force feedback [2] to design the cooperative control of multiple robots (see Remark 6); (iii) possessing nonlinear filtering tracking error to shape the system frequency response [34] together with the fault and constrained control torque [13]. (iv) a simplified on-line learning uncertainty compensation (see (15) and Lemma 2) as compared with fuzzy neural approximation [2]- [5], [10], [28] (see Lemma 3 for computational complexity); (v) possessing nonlinear filtering gains increasing to accelerate its tracking ability and to obtain a better noise-endured characteristic [9], [10], [31]- [34] as the operating point is in the vicinity of the zero nonlinear filtering tracking error; (vi) no requirement of state estimator [10], [12] or disturbance observer [14] to simplify the control scheme.…”

Section: Introductionmentioning

confidence: 99%

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Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Hwang

2020

IEEE Access

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At the outset, a nonlinear dynamic system for the generalized robots with input constraint is given to design an adaptive fixed-time fault-tolerant constraint control (AFTFTCC) for trajectory tracking. The proposed AFTFTCC includes a nonlinear filtering tracking error, which can shape the system response. As the operating point is in the neighborhood of the zero nonlinear filtering tracking error, nonlinear filtering gains are increasing to accelerate its tracking ability. The skew-symmetric matrix's condition for the timederivative of the inertia matrix and Coriolis and centrifugal force matrix is not required. The upper bound of uncertainties is learned to improve system performance. The stabilities of the closed-loop system are verified by the Lyapunov stability theory. Without true force feedback, the cooperative task of multiple robots with multiple arms is also addressed by the proposed AFTFTCC. Finally, two examples, including the trajectory tracking control of planar three-link robot arm and the cooperative control of dual planar three-link robot arms, are employed to validate the effectiveness and robustness of the proposed control. INDEX TERMS Adaptive fixed-time fault-tolerant constraint control, nonlinear filtering tracking error and gain, trajectory tracking of robot, cooperative control of multiple robots, Lyapunov stability theory.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Hwang

2020

IEEE Access

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“…Therefore, how to cope with state constraints of nonlinear systems has received great attention. Fruitful results on state constrains have been reported during the past decades (ie, References , to just name a few). The typical methods in the literature dealing with constraints include model predictive control (MPC) and reference governors (RGs), which rely on the optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Zhao

Lei

Lin

et al. 2020

Intl J Robust & Nonlinear

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In this work, we develop a robust adaptive fault-tolerant tracking control scheme for a class of input-quantized strict-feedback nonlinear systems in the presence of error/state constraints and actuation faults. The problem is rather complicated yet challenging if nonparametric uncertainties and unknown quantization parameters as well as time-varying yet completely undetectable actuation faults are involved in the considered systems. Compared with the most existing approaches in the literature, the proposed control exhibits several attractive advantages: (1) upon using a nonlinear decomposition for quantized input and employing the robust technique for actuation fault, not only the exact knowledge of quantization parameters are not required, but also the actuation fault can be easily compensated since neither fault detection and diagnosis/fault detection and identification nor controller reconfiguration is needed; (2) based on the error/state-dependent unified nonlinear function, the constraints on tracking error and system states are directly handled and the cases with or without constraints can also be addressed in a unified manner without changing the control structure; and (3) the utilization of unified nonlinear function-based dynamic surface control not only avoids the problem of the explosion of complexity in traditional backstepping design, but also bypasses the demanding feasibility conditions of virtual controllers. Furthermore, by using the Lyapunov analysis, it is ensured that all signals in the closed-loop systems are uniformly ultimately bounded. The effectiveness of the developed control algorithm is confirmed by numerical simulations. K E Y W O R D Serror/state constraints, fault-tolerant control, quantized input, robust adaptive control, strict-feedback nonlinear systems, unified nonlinear function INTRODUCTIONIn the past decade, networked control has drawn a great deal of attention as its advantages in low cost of installation and flexibility in system implementation. There are several communication constraints in the control of networked Int J Robust Nonlinear Control. 2020;30:3215-3233. wileyonlinelibrary.com/journal/rnc

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Adaptive finite‐time neural backstepping control for multi‐input and multi‐output state‐constrained nonlinear systems using tangent‐type nonlinear mapping

Yan

Zhou

Liang

et al. 2020

Intl J Robust & Nonlinear

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SummaryThis article focuses on the problem of adaptive finite‐time neural backstepping control for multi‐input and multi‐output nonlinear systems with time‐varying full‐state constraints and uncertainties. A tan‐type nonlinear mapping function is first proposed to convert the strict‐feedback system into a new pure‐feedback one without constraints. Neural networks are utilized to cope with unknown functions. To improve learning performance, a composite adaptive law is designed using tracking error and approximate error. A finite‐time convergent differentiator is adopted to avoid the problem of “explosion of complexity.” By theoretical analysis, all the signals of system are proved to be bounded, the outputs can track the desired signals in a finite time, and full‐state constraints are not transgressed. Finally, comparative simulations are offered to confirm the validity of the proposed control scheme.

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Neuroadaptive Cooperative Control Without Velocity Measurement for Multiple Humanoid Robots Under Full-State Constraints

Cited by 42 publications

References 25 publications

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Tracking and Cooperative Designs of Robot Manipulators Using Adaptive Fixed-Time Fault-Tolerant Constraint Control

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Adaptive finite‐time neural backstepping control for multi‐input and multi‐output state‐constrained nonlinear systems using tangent‐type nonlinear mapping

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