Optimal control problem via neural networks

This paper attempts to propose a new method based on capabilities of artificial neural networks, in function approximation, to attain the solution of optimal control problems. To do so, we try to approximate the solution of Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. In the proposed error function, we used trial solutions for the trajectory function, control function and the Lagrange multipliers. These trial solutions are constructed by using neurons. Then, we minimize the error function that contains just the weights of the trial solutions. Substituting the optimal values of the weights in the trial solutions, we obtain the optimal trajectory function, optimal control function and the optimal Lagrange multipliers.     

A comparative study between the augmented Lagrangian method and the complementarity approach for modeling the contact problem

The increasing demand for real-time high-fidelity multibody dynamics simulations in several modern fields such as robotics and computer game industries has motivated many researches to propose novel approaches to model multibody systems with several contacts. The possibility of different contact conditions in a system with several contacts yields a combinatorial problem of potentially large size. Rigid contact model which is the most common model used for real-time simulations yields a non-smooth dynamic formulation. The solution of such a system can be governed using different methods. In this paper a comparison between the complementarity approaches and the augmented Lagrangian based formulations to deal with non-smooth contact models is presented via numerical examples, and the advantages and shortcomings of each method are discussed.     

Intelligent control of a nonlinear tank reactor

In this paper, intelligent control of a particular Catalytic Continuous Stirred Tank Reactor (CSTR) is addressed. Control command is the sum of two components: steady state and transient commands. A fuzzy controller generates a transient control command that pushes the system towards the setpoint. A steady state control command is generated to maintain the steady state situation at the setpoint (based on the concept of ‘control equilibrium point’). For comparison, a case study was also controlled by the neuro‐predictive algorithm. The studied CSTR is known as a good example of a neuro‐predictive control application; however, the newly proposed hybrid intelligent method leads to much better setpoint tracking as well as less change in control command (which is very important in implementation of the control system).     

A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.     

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

A study on the efficiency of hardware Trojan detection based on path-delay fingerprinting

Hardware Trojan horses (HTHs) are among the most challenging treats to the security of integrated circuits. Path-delay fingerprinting has shown to be a promising HTH detection approach. However, previous work in this area incurs a large hardware cost or requires expensive testing techniques. Moreover, the relation between technology mapping and the efficiency of delay-based HTH detection have not yet been studied. In this paper, we present a HTH detection method which uses an effective test-vector selection scheme and a path-delay measurement structure. Furthermore, we demonstrate the large impact of technology mapping on the effectiveness of delay-based HTH detection. We also show that delay-based detection methods are highly scalable. In case of choosing an area-driven design strategy, the average HTH detection probability of our approach is about 63%, 78% and 90% if false alarm rate is 0%, 2% and 16%, respectively. However, with modifications in the technology mapping, the results show improvements to 85%, 94% and 99%, at the cost of about 20% area overhead. In addition, the efficiency of our method would not decrease for large benchmarks with thousands of gates.     

Assessing the authenticity of subjective information in the blockchain: a survey and open issues

World Wide Web - Blockchain, with its ever-increasing maturity and popularity, is being used in many different applied computing domains. To document the advancements made, researchers have...     

Analysis,modeling, and design of cascaded defected microstrip structure for planar circuits

In this article, two cascaded defected microstrip structures (CDMSs) are proposed to increase the effective inductance. So the cutoff frequencies of filters made by CDMSs can be controlled. The proposed CDMSs have wider stopband characteristic and sharper transition from passband to stopband than defected microstrip structure (DMS). To show the behavior variations of the effective inductance, the DMS and CDMSs have been analyzed, compared, and their design parameters are formulated. These parameters are necessary for a fast filter design procedure. Also, the models of DMS and CDMSs using the very simple lumped element forms are presented according to the physical parameters. The results of the proposed models have been compared with those obtained by full wave analysis. To verify the proposed DMS model, the comparison in response not only has been considered in amplitude but also in phase of all S‐parameters of proposed model and full wave analysis. It is obvious that the stopband is enhanced by periodically cascading the DMS. Surviving of EMC aspects between DMS, DGS, and CDMS is considered and compared. Finally, the theoretical results are validated by comparison with simulations of the structures and experimental results. The simulations from modeling results are found to be in good agreement with the full wave analysis and measurement results in the range of 0.5–35 GHz. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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