205 research outputs found
Adaptive Fuzzy Proportional Integral Sliding Mode Control for Two-Tank Interacting System
This paper presents an adaptive fuzzy proportional integral sliding mode control (AFPISMC) for two-tank interacting system (TTIS). In order to maintain the desired liquid level of the TTIS and meet the reference values for attenuated chattering problems, this paper proposes a combination of a sliding mode control (SMC) with a proportional integral (PI) sliding surface and a fuzzy inference system. Fuzzy logic and the universal approximation theorem of fuzzy systems are used to approximate the uncertain function in the PISMC. The stability of the control system is proved by the Lyapunov theory. The simulation results of the proposed method in MATLAB/Simulink were compared to a fuzzy control, a sliding mode control with conditional integrals, a fuzzy-PID control, and a conventional PID control. The comparison results showed that the proposed controller was most effective when the rising time reached 0.2375 s, the percent of overshoot was 0%, the steady state error converged to zero, the settling time was 0.4612 s, and chattering was reduced
Adaptive Fuzzy Proportional Integral Sliding Mode Control for Two-Tank Interacting System
This paper presents an adaptive fuzzy proportional integral sliding mode control (AFPISMC) for two-tank interacting system (TTIS). In order to maintain the desired liquid level of the TTIS and meet the reference values for attenuated chattering problems, this paper proposes a combination of a sliding mode control (SMC) with a proportional integral (PI) sliding surface and a fuzzy inference system. Fuzzy logic and the universal approximation theorem of fuzzy systems are used to approximate the uncertain function in the PISMC. The stability of the control system is proved by the Lyapunov theory. The simulation results of the proposed method in MATLAB/Simulink were compared to a fuzzy control, a sliding mode control with conditional integrals, a fuzzy-PID control, and a conventional PID control. The comparison results showed that the proposed controller was most effective when the rising time reached 0.2375 s, the percent of overshoot was 0%, the steady state error converged to zero, the settling time was 0.4612 s, and chattering was reduced
Adaptive Sliding Mode Control Based on Fuzzy Logic and Low Pass Filter for Two-Tank Interacting System
An adaptive sliding mode control (SMC) based on fuzzy logic and low pass filter is designed in this research. The SMC is one of the most widely accepted robust control techniques. However, the main disadvantage of the SMC is chattering phenomena, which inhibits its usage in many practical applications. Fuzzy logic control has supplanted conventional techniques in many applications. A major feature of fuzzy logic is the ability to express the amount of ambiguity in individual perception and human thinking. In this study, a fuzzy inference system is applied to approximate the function in the SMC law. A low pass filter is used to reduce chattering phenomena around the sliding surface. The stability of the control system is proved by the Lyapunov theory. The proposed controller is tested to position tracking control for two-tank interacting system. This system has been applied in process industries like petroleum refineries, chemical, paper industries, water treatment industries. Simulation results in MATLAB/Simulink show that the proposed algorithm is more effective than the sliding mode control, sliding mode control using conditional integrators and fuzzy control without steady-state error, the overshoot is 0 (%), the rising time achieves 2.187 (s) and the settling time is about 3.9133(s)
Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation
We consider a stochastic individual-based model in continuous time to
describe a size-structured population for cell divisions. This model is
motivated by the detection of cellular aging in biology. We address here the
problem of nonparametric estimation of the kernel ruling the divisions based on
the eigenvalue problem related to the asymptotic behavior in large population.
This inverse problem involves a multiplicative deconvolution operator. Using
Fourier technics we derive a nonparametric estimator whose consistency is
studied. The main difficulty comes from the non-standard equations connecting
the Fourier transforms of the kernel and the parameters of the model. A
numerical study is carried out and we pay special attention to the derivation
of bandwidths by using resampling
Analysis of Corporate Governance Index Using ASEAN Balanced Score Card and Firm Performance
Governance plays a crucial role in most activities of socio-economic life. For any organizations, units, businesses or higher, a country, a community, the role of governance becomes more important than ever. For businesses, with globalization taking place and market becoming more competitive than ever, good Corporate Governance is now considered as a factor of success. Good corporate governance will help the company improve its ability to access to various sources of capital and operate more efficiently. However, in Vietnam particularly, Corporate Governance system still need to enhance and improve. Therefore, with the aim of surveying the current situation of Corporate Governance practices in Vietnam and giving the analysis of its impacts on businesses’ performance, this research is conducted from a sample of 60 listed-companies in both Construction and Food & Beverage industry for the time 2015, 2017 and 2018. By using the tool of ASEAN balanced scorecard to evaluate Vietnam Corporate Governance practices on 05 aspects: Rights of shareholders, Equitable treatment of shareholders, Roles of stakeholders, Disclosure and transparency and Board duties and responsibilities, the paper came to the low results of companies’ Corporate Governance practices in Vietnam. Based on this method, different relationships were found, one of which is the positive relationship between Corporate Governance Index and Tobin’s Q. Keywords: Asean Score Card, Corporate Governance Index, Performance DOI: 10.7176/RJFA/11-6-02 Publication date:March 31st 202
Considerations in Developing Blockchain-enabled Food Supply Chain Solutions: A Developer Perspective
There is currently considerable interest and activity in the adoption of blockchains in food supply chains (FSC) by both researchers and industry. Blockchain developers have to satisfy the requirements of multiple users and stakeholders as well as handle technical requirements from the underlying blockchain platform. Many prior studies are conceptual and theoretical and there is a need for both a deeper understanding of the problem space and also a need for insights from real-world perspectives, particularly in the FSC context. This study interviewed 14 experts from FSC blockchain application provider companies. The interviews were analysed using a Grounded Theory approach. The preliminary findings presented in this research-in-progress paper show that developers are concerned with: Technical aspects, cost-benefit aspects, management and operational aspects, end-user issues and societal aspects. These findings provide a deeper understanding of the issues involved in developing blockchain applications in FSC
Adaptive PID sliding mode control based on new Quasi-sliding mode and radial basis function neural network for Omni-directional mobile robot
This article designs a PID sliding mode controller based on new Quasi-sliding mode (PID-SMC-NQ) and radial basis function neural network (RBFNN) for Omni-directional mobile robot. This is holonomic vehicles that can perform translational and rotational motions independently and simultaneously. The PID-SMC is designed to ensure that the robot's actual trajectory follows the desired in a finite time with the error converges to zero. To decrease chattering phenomena around the sliding surface, in the controller robust term, this paper uses the tanh (hyperbolic tangent) function, so called the new Quasi-sliding mode function, instead of the switch function. The RBFNN is used to approximate the nonlinear component in the PID-SMC-NQ controller. The RBFNN is considered as an adaptive controller. The weights of the network are trained online due to the feedback from output signals of the robot using the Gradient Descent algorithm. The stability of the system is proven by Lyapunov's theory. Simulation results in MATLAB/Simulink show the effectiveness of the proposed controller, the actual response of the robot converges to the reference with the rising time reaches 307.711 ms, 364.192 ms in the x-coordinate in the two-dimensional movement of the robot, the steady-state error is 0.0018 m and 0.00007 m, the overshoot is 0.13% and 0.1% in the y-coordinate, and the chattering phenomena is reduced
Design And Performance Evaluation A Sliding Mode Control Based On The K Observer For A Three Tank Non-Interacting System
A sliding mode control (SMC) based on the K observer for a three tank non-interacting system is designed and
evaluated in this paper. This system is important in modern process control since it can potentially improve product quality and enhance economic benefits. The SMC controller is designed to ensure that the actual liquid level tracks the desired in a finite time. The K observer is used to estimate the speed and acceleration signal, and realize sliding mode control only by using the position signal. The Lyapunov theory is chosen to prove the stability of the control system. The simulation results of the proposed method in MATLAB/Simulink were compared to an optimal controller tuning of a PI controller based on Skogestad’s tuning method, Cohen coon based tuning and Internal Model Control, an optimal PIDA controller with model uncertainty by Cuckoo Search, a fuzzy logic controller, a PID-Fuzzy logic controller. The comparison results showed that the proposed controller was more effective when the rising time reached 2.1521s, the percent of overshoot was 0%, the steady state error converged to zero, the settling time was 3.8577s
Statistical deconvolution of the free Fokker-Planck equation at fixed time
We are interested in reconstructing the initial condition of a non-linear
partial differential equation (PDE), namely the Fokker-Planck equation, from
the observation of a Dyson Brownian motion at a given time . The
Fokker-Planck equation describes the evolution of electrostatic repulsive
particle systems, and can be seen as the large particle limit of correctly
renormalized Dyson Brownian motions. The solution of the Fokker-Planck equation
can be written as the free convolution of the initial condition and the
semi-circular distribution. We propose a nonparametric estimator for the
initial condition obtained by performing the free deconvolution via the
subordination functions method. This statistical estimator is original as it
involves the resolution of a fixed point equation, and a classical
deconvolution by a Cauchy distribution. This is due to the fact that, in free
probability, the analogue of the Fourier transform is the R-transform, related
to the Cauchy transform. In past literature, there has been a focus on the
estimation of the initial conditions of linear PDEs such as the heat equation,
but to the best of our knowledge, this is the first time that the problem is
tackled for a non-linear PDE. The convergence of the estimator is proved and
the integrated mean square error is computed, providing rates of convergence
similar to the ones known for non-parametric deconvolution methods. Finally, a
simulation study illustrates the good performances of our estimator
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