Adaptive fuzzy robust tracking controller design via small gain approach and its application

Yang, Yiling; Ren, Junsheng

doi:10.1109/tfuzz.2003.819837

Cited by 163 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose to extend the running time to 60 s and found that the entire system maintain stable, the curve of unmodeled dynamics is shown in Figure 5. The simulation shows that the proposed control strategy can effectively deal with the system with uncertain parameters, unmodeled dynamics and white noise in (45). F I G U R E 5 Curve of unmodeled dynamics.…”

Section: Simulation Examplementioning

confidence: 97%

“…According to the literature 43 Lemma 3, we know that the smooth nonnegative functions 𝜑 i1 and 𝜑 i2 exist. Noting that 𝜑 i1 is related to x i , and is not related to the z-subsystem, then we can assume that 𝜑 i1 is bounded, for example, |𝜑 i1 | ≤ 𝜑 i0 (For systems without unmodeled dynamics, many literatures have made such an assumption, such as References 44,45), where 𝜑 i0 are unknown constants, thus…”

Section: Notationsmentioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered adaptive stabilization control of stochastic nonlinear systems with unmodeled dynamics

Chen

Liu

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this paper, we present a novel controller design method for stochastic nonlinear systems with unmodeled dynamics, uncertain parameters, and unknown covariance noise. In order to deal with these uncertainties, a new event‐based small gain controller is designed. The event‐triggered scheme can reduce the computational burden caused by disturbance and noise. By combining the technique of changing supply rate with small gain condition, a stochastic input‐to‐state practically stability Lyapunov function is obtained for the subsystem. Simultaneously, the changing supply function is used to treat with unmodeled dynamics. Based on backstepping process and adaptive control approach, the influence of unknown covariance noise is overcome. It is proved that the designed state‐feedback controller can ensure the overall system is stochastic input‐to‐state practically stability.

show abstract

Section: Simulation Examplementioning

confidence: 97%

Section: Notationsmentioning

confidence: 99%

Event‐triggered adaptive stabilization control of stochastic nonlinear systems with unmodeled dynamics

Chen

Liu

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The transfer function of a ship as a course control object can be defined in a simplified form as a first-order Nomoto model. The relationship between the rudder angle δ and the ship's heading angle φ is expressed as follows [25]:…”

Section: The Manoeuvre Of Course Alterationmentioning

confidence: 99%

“…The fitness function of a chromosome measures the cost of the trajectory it represents. As for the two-ship encounter, the function should consider three different optimisation factors: (1) Satisfy the clearance requirements, (2) maintain a smooth trajectory, and (3) guarantee less distance travelled; a linear combination of these factors is shown Equation (25).…”

Section: Fitness Function Modelmentioning

confidence: 99%

Ship Manoeuvrability-Based Simulation for Ship Navigation in Collision Situations

Liu

Cai

2019

JMSE

View full text Add to dashboard Cite

In this article, a ship manoeuvrability-based simulation for ship navigation in collision situations is established. Under the general requirement from the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) and good seamanship, the determination of encounter situations is quantified to reduce navigators’ intervention. Meanwhile, the action manner by course alteration or changing speed in some typical encounter situations is graphically analysed for both the give-way and stand-on vessels. Then, the multiple genetic algorithm and linear extension algorithm are adopted to perform trajectory planning for collision avoidance. To improve the reliability of the simulation system, the mathematical model of ship motion and ship manoeuvring control mechanism are adopted, which can eliminate the insufficiency of neglect of ship manoeuvrability in the process of collision avoidance. Meanwhile, the course encoding technique is adopted to fit the ship manoeuvring control mechanism. Finally, a set of traffic scenarios emulating different encounter situations are applied to demonstrate the effectiveness, consistency, and practicality of this system.

show abstract

“…Great effort has been devoted to this topic and many significant results have been gained. For example, some ideas have emerged to tackle the ship steering problem, such as backstepping [4], adaptive control [3] and fuzzy control [5], dynamic surface control [6]. However, the controllers previously mentioned suffered from the explosion of learning parameters and computational burden.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Design of Ship's Autopilot with Input Saturation

Yu¹

2013

2013 International Conference on Information Technology and Applications

View full text Add to dashboard Cite

This paper describes an adaptive fuzzy method used for ship's autopilot with input saturation. The ship model is described by a third order nonlinear model with unknown parameters and unknown virtual control gain function. The Takagi-Sugeno (T-S) type fuzzy systems are used to approximate unknown system functions, the adaptive fuzzy tracking controller is constructed by combining dynamic surface control (DSC) technique and the minimal-learningparameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains is conducted employing an auxiliary design system. With only one learning parameter and reduced computation load, the proposed algorithm can avoid both problem of "explosion of complexity" in the conventional backstepping method and singularity problem. In addition, the boundedness stability of the closedloop system is guaranteed and tracking error can be made arbitrary small. The effectiveness of the presented autopilot has been demonstrated in the simulation.

show abstract

Adaptive fuzzy robust tracking controller design via small gain approach and its application

Cited by 163 publications

References 39 publications

Event‐triggered adaptive stabilization control of stochastic nonlinear systems with unmodeled dynamics

Event‐triggered adaptive stabilization control of stochastic nonlinear systems with unmodeled dynamics

Ship Manoeuvrability-Based Simulation for Ship Navigation in Collision Situations

Adaptive Fuzzy Design of Ship's Autopilot with Input Saturation

Contact Info

Product

Resources

About