Adaptive fuzzy control for the ship steering problem

Journal of Artificial Intelligence and Soft Computing Research

2014

An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

Section: Introductionmentioning

confidence: 77%

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Journal of Artificial Intelligence and Soft Computing Research

2014

“…The new results come to extend the method presented in [31][32][33][34][35][36]. By showing that chaotic dynamical systems are differentially flat one it becomes possible to transform them to the linear canonical form.…”

Section: Introductionmentioning

confidence: 84%

“…It is known that neurofuzzy approximators can be used in indirect adaptive control schemes where their role is to identify online the unknown system dynamics and to provide the control with this information that is used for generating the control inputs [28][29][30]. Adaptive fuzzy control based on differential flatness theory extends the class of systems to which indirect adaptive fuzzy control can be applied [31][32][33][34][35][36]. This is important for the design of controllers, capable of efficiently compensating for modeling uncertainties and external disturbances in nonlinear dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

2016

Intell Ind Syst

The paper proposes a solution to the problem of control of nonlinear chaotic dynamical systems, which is based on differential flatness theory and on adaptive fuzzy control. An adaptive fuzzy controller is designed for chaotic dynamical systems, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the chaotic system's model into a linear form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neurofuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the adaptive fuzzy control method is checked through simulation experiments, using as case study the Lorenz chaotic oscillator.

“…Therefore, it is important to develop feedback control schemes for AUVs and submarines that will be little dependent on prior and exact knowledge of the associated dynamic model and will exhibit sufficient robustness to perturbation inputs [4][5][6][7][8][9][10][11]. To this end, in the recent years several research results have been presented, in particular on robust control [12][13][14][15] and on adaptive control of AUVs [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Flatness-Based Adaptive Fuzzy Control of Autonomous Submarines

2015

Intell Ind Syst

The paper proposes adaptive fuzzy control based on differential flatness theory for autonomous submarines. It is proven that the dynamic model of the submarine, having as state variables the vessel's depth and its pitch angle, is a differentially flat one. This means that all its state variables and its control inputs can be written as differential functions of the flat output and its derivatives. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, the Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.Electronic supplementary material The online version of this article