Adaptive Fuzzy Tracking Control of Nonlinear Time-Delay Systems With Dead-Zone Output Mechanism Based on a Novel Smooth Model

Li, Zhi; Lai, Guanyu; Zhang, Yun; Chen, C. L. Philip

doi:10.1109/tfuzz.2015.2396075

Cited by 154 publications

(54 citation statements)

References 68 publications

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“…From Reference , a class of second order robotic manipulator system is described as

\{\begin{array}{l} {\overset{x}{true}}_{1} = x_{2} \\ {\overset{x}{true}}_{2} = \frac{1}{J} ((), u ((), v) + B x_{2} prefix- m g l normalsin x_{1}) \\ y = x_{1}, \end{array}

where J =1 denotes the rotation inertia which represents the damping coefficient. The mechanical arm mass is B =1, the gravity acceleration is m =0.1 kg, g =10 m/s 2 , and the centroid to joint length is l =1 m.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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Fuzzy adaptive two‐bits‐triggered control for nonlinear uncertain system with input saturation and output constraint

Chen

Wang

et al. 2020

Adaptive Control & Signal

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Summary In this work, a fuzzy adaptive two‐bits‐triggered control is investigated for the nonlinear uncertain systems with input saturation and output constraint. The considered systems are more widespread. Without sufficient transmission resources, how to resolve the constraint issues while guarantee the control performance is difficult and challenging. Then, hyperbolic tangent function and barrier Lyapunov function are integrated with the designed auxiliary system to solve input saturation and output constraint. Meanwhile, faced with the transmission resources limitation, this work both considers the triggering condition and the control signal transmission bits. A two‐bits‐triggered control is proposed to economize the transmission resources. Furthermore, improved fuzzy logic systems are established to further promote the control performance. It combines with the time‐varying approximation error for processing. The boundedness of all the system signals can be proved. Simulation results illustrate the validity of the proposed approach.

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“…From Reference , a class of second order robotic manipulator system is described as

\{\begin{array}{l} {\overset{x}{true}}_{1} = x_{2} \\ {\overset{x}{true}}_{2} = \frac{1}{J} ((), u ((), v) + B x_{2} prefix- m g l normalsin x_{1}) \\ y = x_{1}, \end{array}

Section: Simulation and Resultsmentioning

confidence: 99%

“…Give the uncertain nonlinear systems (1), virtual control law (19), (26), (37), (49) subject to two-bits-triggered strategy (43), (44) and adaptive laws (22), (31), (40), (52). With the help of Lemma1, Lemma 2, and Assumption 1, all the system signals' boundedness can be ensured.…”

Section: Stability Analysismentioning

confidence: 99%

Fuzzy adaptive two‐bits‐triggered control for nonlinear uncertain system with input saturation and output constraint

Chen

Wang

et al. 2020

Adaptive Control & Signal

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“…While the effects of dead-zone in the actuator is important, practical systems may also suffer from the output dead-zone nonlinearity. However, they receives relatively little attention until recently, an adaptive fuzzy tracking problem was studied in [36], which focused on strict-feedback systems with dead-zone output mechanism based on adaptive fuzzy backstepping technique. If the nonlinear systems appeared in nonstrict-feedback form are subject to unknown symmetric output dead-zone and are required to be stabilized by adaptive neural DSC-based controller, to the best of our knowledge, there are no such existing schemes, furthermore, when output tracking error cannot exceed the prescribed constraints, the control design becomes more complicated.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Effective methods to tune adaptation parameters are proposed in [3], [7], [8], [16], [17], [19]− [21], [31], [36] by using the norms of the NNs/FLSs weights. However, in our work, only one on-line estimation parameter needs to be updated for an n-order nonlinear nonstrict-feedback system.…”

Section: Introductionmentioning

confidence: 99%

“…(3) Compared with the existing results of tracking problem in [3], [8], [9], [13], [14], [17], [20], [34], [36]− [38] the control method in this paper only requires the known information of the desired trajectory and its first time derivative. Thus, the assumption on desired trajectory imposed in our paper is more reasonable for practical applications.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone

Shi

Lim

Shi

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

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This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.

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Prescribed performance adaptive fuzzy control for nonstrict‐feedback nonlinear systems with dead zone outputs

SuHang

ZhaoXiaoyu

Zhang

2020

Adaptive Control & Signal

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This paper presents an adaptive fuzzy control scheme for a class of nonstrict-feedback nonlinear systems with dead zone outputs and prescribed performance. By utilizing the monotonically increasing property of system bounding functions and the Nussbaum function, the design difficulties caused by the nonstrict-feedback structure and dead zone output are overcome. Combining backstepping technique with prescribed performance algorithm, a feasible adaptive fuzzy controller is designed to guarantee the boundedness of all signals of the closed-loop system and the prescribed tracking performance of the system. Finally, simulation results are depicted to illustrate the effectiveness of the proposed control approach. K E Y W O R D Sadaptive fuzzy control, backstepping technique, dead zone outputs, prescribed performance INTRODUCTIONBy utilizing fuzzy logic systems 1-4 or neural networks 5 to approximate the unknown nonlinear system functions, approximation-based adaptive fuzzy or neural control has received considerable attention in the past decades. In References 6-12, combining with backstepping technique, adaptive fuzzy or neural control was considered for strict-feedback nonlinear systems. In References 13-17, the authors extended the controlled nonlinear systems from deterministic systems to the uncertain stochastic nonlinear systems. However, the results obtained in References 6-17 cannot be directly applied to nonstrict-feedback nonlinear systems. For the strict-feedback systems, the nonlinear functions of the jth subsystem are independent of the latter state variables x i (j + 1 ≤ i ≤ n), and in the nonstrict-feedback systems, each subsystem function contains the whole state variables, which is the major difference between a strict-feedback nonlinear system and a nonstrict-feedback one. If the traditional backstepping control scheme is adopted in a nonstrict-feedback system, virtual control signal designed in the approach is the function of whole state vector, which makes the controller design difficult and leads to algebraic loop problem.Recently, many researchers have paid attention to adaptive fuzzy or neural control of nonstrict-feedback nonlinear systems. In Reference 18, the problem of observer-based adaptive neural output-feedback control was considered for stochastic nonstrict-feedback nonlinear system. In References 19-24, adaptive fuzzy or neural control was extended to nonstrict-feedback nonlinear systems with input nonlinearities such as time delays, 19 dead zones, 20,21 input saturation 22,23

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Adaptive Fuzzy Tracking Control of Nonlinear Time-Delay Systems With Dead-Zone Output Mechanism Based on a Novel Smooth Model

Cited by 154 publications

References 68 publications

Fuzzy adaptive two‐bits‐triggered control for nonlinear uncertain system with input saturation and output constraint

Fuzzy adaptive two‐bits‐triggered control for nonlinear uncertain system with input saturation and output constraint

Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone

Prescribed performance adaptive fuzzy control for nonstrict‐feedback nonlinear systems with dead zone outputs

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