Wallace M. Bessa

Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller

Sliding mode control, due to its robustness against modelling imprecisions and external disturbances, has been successfully employed to the dynamic positioning of remotely operated underwater vehicles. In order to improve the performance of the complete system, the discontinuity in the control law must be smoothed out to avoid the undesirable chattering effects. The adoption of a properly designed thin boundary layer has proven effective in completely eliminating chattering, however, leading to an inferior tracking performance. This paper describes the development of a depth control system for remotely operated underwater vehicles. The adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm for uncertainty/disturbance compensation. The stability and convergence properties of the closed-loop system are analytically proved using Lyapunov stability theory and Barbalats lemma. Numerical results are presented in order to demonstrate the control system performance.

An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles

Sliding mode control is a very attractive control scheme because of its robustness against both structured and unstructured uncertainties as well as external disturbances. In this way, it has been widely employed for the dynamic positioning of remotely operated underwater vehicles. Nevertheless, in such situations the discontinuities in the control law must be smoothed out to avoid the undesirable chattering effects. The adoption of properly designed boundary layers has proven effective in completely eliminating chattering, however, leading to an inferior tracking performance. This work describes the development of a dynamic positioning system for remotely operated underwater vehicles. The adopted approach is primarily based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm for uncertainty/disturbance compensation. Using the Lyapunov stability theory and Barbalats lemma, the boundedness and convergence properties of the closed-loop signals are analytically proven. The performance of the proposed control scheme is also evaluated by means of numerical simulations.

Sliding Mode Control with Adaptive Fuzzy Dead-Zone Compensation of an Electro-hydraulic Servo-System

Electro-hydraulic servo-systems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electro-hydraulic systems, due to their inherent nonlinear characteristics, cannot be easily obtained with conventional linear controllers. Most flow control valves can also exhibit some hard nonlinearities such as dead-zone due to valve spool overlap. This work describes the development of an adaptive fuzzy sliding mode controller for an electro-hydraulic system with unknown dead-zone. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov stability theory and Barbalat’s lemma. Numerical results are presented in order to demonstrate the control system performance.

Adaptive fuzzy sliding mode control of uncertain nonlinear systems

This paper presents a detailed discussion about the convergence properties of a variable structure controller for uncertain single-input-single-output nonlinear systems (SISO). The adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies and external disturbances that can arise. The boundedness of all closed-loop signals and the convergence properties of the tracking error are analytically proven using Lyapunovs direct method and Barbalats lemma. This result corrects flawed conclusions previously reached in the literature. An application of this adaptive fuzzy sliding mode controller to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance.

Dynamic Positioning of Underwater Robotic Vehicles with Thruster Dynamics Compensation

The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation of thruster dynamics. Without compensation, the closed-loop positioning system can exhibit limit cycles. This undesired behaviour may compromise the overall system stability. In this work, a fuzzy sliding-mode compensation scheme is proposed for electrically actuated bladed thrusters, which are commonly employed in the dynamic positioning of underwater vehicles. The boundedness and convergence properties of the tracking error are analytically proven. The numerical results suggest that this approach shows a greatly improved performance when compared with an uncompensated counterpart.

An adaptive fuzzy dead-zone compensation scheme and its application to electro-hydraulic systems

The dead-zone is one of the most common hard nonlinearities in industrial actuators and its presence may drastically compromise control systems stability and performance. Due to the possibility to express specialist knowledge in an algorithmic manner, fuzzy logic has been largely employed in the last decades to both control and identification of uncertain dynamical systems. In spite of the simplicity of this heuristic approach, in some situations a more rigorous mathematical treatment of the problem is required. In this work, an adaptive fuzzy controller is proposed for nonlinear systems subject to dead-zone input. The boundedness of all closed-loop signals and the convergence properties of the tracking error are proven using Lyapunov stability theory and Barbalats lemma. An application of this adaptive fuzzy scheme to an electro-hydraulic servo-system is introduced to illustrate the controller design method. Numerical results are also presented in order to demonstrate the control system performance.

Controlling a Shape Memory Alloy Two-Bar Truss Using Delayed Feedback Method

This work discusses the use of chaos control in smart structures. An archetypal model of a shape memory alloy (SMA) two-bar truss is treated. This system exhibits both constitutive and geometrical nonlinearities presenting a complex nonlinear dynamics response including either the snap-through or the chaotic behaviors. A constitutive model that presents a close agreement with experimental data is employed to describe the themomechanical SMA behavior. A variation of the continuous time-delayed feedback method is employed as a control strategy. This variable structure controller is applied to the stabilization of unstable periodic orbits of the SMA structure avoiding the snap-through behavior.

Design and Adaptive Depth Control of a Micro Diving Agent

This letter presents the depth control of an autonomous micro diving agent called autonomous diving agent (ADA). ADA consists of off-the-shelf components and features open-source hardware and firmware. It can be deployed as a testbed for depth controllers, as well as a mobile sensor platform for research or in industrial tanks. We introduce a control law that is based on the feedback linearization method and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies. The proposed depth controller is computationally light enough to run on ADAs embedded hardware. In experiments performed in a wave tank, the adaptive fuzzy scheme shows the ability to deal with both depth regulation and depth profile tracking. ADA is even able to hold on to dynamic isobars despite external disturbances. We demonstrate that under the influence of waves, ADA describes orbital motions similar to water particles.

Feedback linearization with a neural network based compensation scheme

This paper presents a nonlinear controller for uncertain single-input---single-output (SISO) nonlinear systems. The adopted approach is based on the feedback linearization strategy and enhanced by a Radial Basis Function neural network to cope with modeling inaccuracies and external disturbances that can arise. An application of this nonlinear controller to an electro-hydraulic actuated system subject to an unknown dead-zone input is also presented. The obtained numerical results demonstrate the improved control system performance.

FEEDBACK LINEARIZATION WITH A FUZZY COMPENSATION SCHEME

Resumo: This paper presents a nonlinear controller for uncertain single-input–single-output nonlinear systems (SISO). The adopted approach is based on the feedback linearization strategy and enhanced by a fuzzy inference algorithm to cope with modeling inaccuracies and external disturbances that can arise. An application of this nonlinear controller to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance.