Fuzzy Command Filtered Backstepping Control for Nonlinear System With Nonlinear Faults

Sheng, Ning; Zhang, Dian; Zhang, Qichun

doi:10.1109/access.2021.3074424

Cited by 7 publications

(4 citation statements)

References 35 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) For the integer-order backstepping controller, the command filter is given as (11), and the integer-order disturbance observer is defined as…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Nowadays, the command filter is the preferred choice to avoid the issue that the intermediate control law cannot be derived directly [10]. The backstepping control technology based on command filtering, which is proposed in [11], avoids the problem of direct derivation of intermediate control laws and eliminates the impact of command filtering errors by designing the auxiliary signal. In addition, most of the backstepping control is based on the infinite-time stability theory, which has the problem of slow convergence time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Backstepping Control with a Fractional-Order Command Filter and Disturbance Observer for Unmanned Surface Vehicles

Ma,

Chen,

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

In the paper, a backstepping control strategy based on a fractional-order finite-time command filter and a fractional-order finite-time disturbance observer is proposed for the trajectory tracking control of an unmanned surface vehicle. A fractional-order finite-time command filter is presented to estimate the derivatives of the intermediate control, which cannot be directly calculated, thereby reducing the chattering generated by the integer-order command filter. The fractional-order finite-time disturbance observer is presented to approximate and compensate for the model uncertainty and unknown external disturbances in the system. Subsequently, the globally asymptotically stable nature of the closed-loop system is proved based on the Lyapunov method. The effectiveness of the method is proven by simulation experiments on unmanned surface vehicles.

show abstract

“…(2) For the integer-order backstepping controller, the command filter is given as (11), and the integer-order disturbance observer is defined as…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Backstepping Control with a Fractional-Order Command Filter and Disturbance Observer for Unmanned Surface Vehicles

Ma,

Chen,

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…Also, several studies have proven “model-free” or no “prior assumptions on the plant” controllers to exhibit poor performance [ 18 – 21 ]. Intelligent control is the application of artificial or computer-aided intelligence techniques such as neural networks, evolutionary computation, fuzzy logic [ 22 ], machine or reinforcement learning to (usually) complex and non-trivial dynamical systems [ 23 , 24 ]. In [ 25 ], a full-state output-feedback adaptive fuzzy controller has been devised with with output constraint.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of structured H ∞ based looptune and LQR for a 4-DOF robotic manipulator

Asghar¹,

Iqbal

Khaliq

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

We explore looptune, a MATLAB-based structured H ∞ synthesis technique in the context of robotics. Position control of a 4 Degree of Freedom (DOF) serial robotic manipulator developed using Simulink is the problem under consideration. Three full state feedback control systems were developed, analyzed and compared for both steady-state and transient performance using the Linear Quadratic Regulator (LQR) and looptune. Initially, a single gain feedback controller was synthesized using LQR. This system was then modified by augmenting the state feedback controller with Proportional Integral (PI) and Integral regulators, thereby creating a second and third control system respectively. In both the second and third control systems, the LQR synthesized gain and additional gains were further tuned using looptune to achieve improvement in performance. The second and third systems were also compared in terms of tracking a time-dependent trajectory. Finally, the LQR and looptune synthesized controllers were tested for robustness by simultaneously increasing the mass of each manipulator link. In comparison to LQR, the second system consisting of Single Input Single Output (SISO) PI controllers and the state feedback matrix succeeded in meeting the control objectives in terms of performance, optimality, trajectory tracking, and robustness. The third system did not improve performance in contrast to LQR, but still showed robustness under mass variation. In conclusion, our results have shown looptune to have a comparatively better performance over LQR thereby highlighting its promising potential for future emerging control system applications.

show abstract

“…Since spiking neural P systems (SN P systems) do not have the ability to deal with fuzzy and uncertain data in fault diagnosis problems, the FRSN P systems integrate different fuzzy logics into SN P systems. Various fuzzy reasoning algorithms for fault diagnosis using FRSN P systems have been developed [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. An SN P system consists of a network of neurons connected together with synapses and can be regarded as the third generation of neural network models.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Yin

Liu

Sun

et al. 2022

Entropy

View full text Add to dashboard Cite

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.

show abstract

Fuzzy Command Filtered Backstepping Control for Nonlinear System With Nonlinear Faults

Cited by 7 publications

References 35 publications

Backstepping Control with a Fractional-Order Command Filter and Disturbance Observer for Unmanned Surface Vehicles

Backstepping Control with a Fractional-Order Command Filter and Disturbance Observer for Unmanned Surface Vehicles

Performance comparison of structured H ∞ based looptune and LQR for a 4-DOF robotic manipulator

Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Contact Info

Product

Resources

About