Harnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water ...
Harnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has introduced a groundbreaking concept of hybrid offshore platforms, offering immense potential for energy absorption, reduced dynamic response, load mitigation, and improved cost efficiency. The primary goals of this study revolve around two key objectives: (i) the development of a regression-based modeling technique for the hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and (ii) the implementation of a customized fuzzy-based control mechanism to ensure platform stability. To achieve these objectives, computational Machine Learning (ML) tools, specifically Artificial Neural Networks (ANNs), are utilized to replicate the behavior of the detailed numerical model of the FOWTs integrated with OWCs. Subsequently, a Fuzzy Logic control (FLC) scheme is employed to establish a structural controller that effectively mitigates unwanted vibrations. The experimental results confirm the potential of ANN-based modeling as a simpler yet effective alternative to complicated nonlinear NREL-5MW FOWT dynamical models. Furthermore, the use of the FLC system enhances platform stability in a variety of wind and wave conditions.
Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a fir...
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We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is propo...
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We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a poster...
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An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian ...
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There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the co...
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Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically fe...
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In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a diffic...
In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.
作者:
A. IrawanM. I. Putra AzaharM. A. HashimiRobotics
Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pahang Malaysia
The paper presents the proposed sensorless force estimator design for pneumatic robot fingertip by using gravitational compensation and pressure changed in pneumatic cylinder piston. The approach is done to replace th...
The paper presents the proposed sensorless force estimator design for pneumatic robot fingertip by using gravitational compensation and pressure changed in pneumatic cylinder piston. The approach is done to replace the commercial force sensor that may be expensive for heavy-duty configuration. The formulation was done by considering the torque of robot's finger joint, finger dimension as well as its actuator and the different pressures in cylinder piston. The gravitational force is calculated from the geometry of the robot's finger as dynamic gain for the force of pneumatic cylinder. The proposed method is validated on a heay-duty pneumatic Tri-grasper Robot with the simple basic movement and blocked randomly by human barehand. The results show that the force output by the estimator is almost identical to the loadcell sensor that attached on the fingertip at about 2% error in average. The sensitivity is a bit low for small and fragile material but enough for heavy-duty application that generally with hard and rough surfaces.
The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow ...
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