Hybrid Fuzzy Adaptive Fault-Tolerant Control for a Class of Uncertain Nonlinear Systems With Unmeasured States

Zhang, Huaguang; Cui, Yang; Wang, Yingchun

doi:10.1109/tfuzz.2016.2593500

Cited by 53 publications

(28 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new variable is defined as δ =Ḡ −1 (d m + l * ) and based on Assumption 3, we introduce δ, θ and (30) into (29)…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…By introducing a backstepping technique to fault-tolerant control, an adaptive actuator fault compensation control was studied in [28] for a class of uncertain multi-input single-out discrete-time systems with triangular forms. In [29], hybrid fuzzy adaptive FTC was presented for a class of uncertain nonlinear systems with unmeasured states. In [30], an adaptive neural-fuzzy sliding-mode fault-tolerant control was developed for uncertain nonlinear systems to handle actuator effectiveness faults and input saturation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Zhang

Chen

2020

Applied Sciences

View full text Add to dashboard Cite

In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.

show abstract

“…A new variable is defined as δ =Ḡ −1 (d m + l * ) and based on Assumption 3, we introduce δ, θ and (30) into (29)…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Zhang

Chen

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Each NN consists of two parts including tuned NN weights and NN activation functions. In this section, we will provide another implementation tool called generalized fuzzy hyperbolic model (GFHM) [1,25,26]. It also has the property of the universal approximation, and can be finally converted into the similar form as NNs.…”

Section: Algorithm Implementation and Stability Analysismentioning

confidence: 99%

“…Lemma 1 [1,25] Let the membership functions be given by (17) and the generalized fuzzy hyperbolic rule base be described by Definition 1. Then, the GFHM can be derived by…”

Section: Algorithm Implementation and Stability Analysismentioning

confidence: 99%

See 1 more Smart Citation

A novel data-driven intelligent computing method for the secure control of a benchmark microgrid system

et al. 2020

View full text Add to dashboard Cite

Microgrid is a small-scale cyber-physical system, and it generally suffers from various uncertainties. In this paper, we investigate the secure control problem of a benchmark microgrid with system uncertainties by using data-driven edge computing technology. First, the state-space function of the benchmark microgrid system is formulated, and parameter uncertainties are taken into consideration. Second, a novel data-driven intelligent computing method is derived from the modelbased reinforcement learning algorithm, which only requires system data instead of system models. By utilizing this computing method, the optimal control policy can be obtained in the model-free environment. Third, the Lyapunov stability theory is employed to prove that the uncertain microgrid can be asymptotically stabilized under the optimal control policy. Finally, simulation results demonstrate the control performance can be improved by tuning the parameters in the performance index function.

show abstract

Adaptive prescribed performance tracking control for uncertain strict‐feedback Multiple Inputs and Multiple Outputs nonlinear systems based on generalized fuzzy hyperbolic model

Yang

ZongXue‐Jun

WangGuo‐Gang

2020

Adaptive Control & Signal

View full text Add to dashboard Cite

This article proposes an adaptive prescribed performance tracking control methodology for a class of strict-feedback Multiple Inputs and Multiple Outputs nonlinear systems. A combination of backstepping technique and the generalized fuzzy hyperbolic model was used in recursive design of adaptive controller. A novel performance constraint function guarantees the tracking control performance. Lyapunov stability analysis proves that the designed controller can ensure the predefined transient and all signals within the closed-loop systems are semiglobally uniformly ultimately bounded. In the end, simulation results illustrate the validity of the proposed approach.

show abstract

Hybrid Fuzzy Adaptive Fault-Tolerant Control for a Class of Uncertain Nonlinear Systems With Unmeasured States

Cited by 53 publications

References 45 publications

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

A novel data-driven intelligent computing method for the secure control of a benchmark microgrid system

Adaptive prescribed performance tracking control for uncertain strict‐feedback Multiple Inputs and Multiple Outputs nonlinear systems based on generalized fuzzy hyperbolic model

Contact Info

Product

Resources

About