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The purpose of this paper is to present a sliding mode controller design method for a class of uncertain nonlinear systems with uncertainties and to demonstrate a recursive derivative estimation procedure for the derivatives of system outputs.

A recursive derivative estimation procedure for the derivatives of system outputs is demonstrated. Radial basis function (RBF) neural networks are used to approximate the uncertainties and filters are introduced to estimate the derivatives of system outputs step‐by‐step. The adaptive tuning rules of RBF neural network weight matrices are derived by the Lyapunov stability theorem, which guarantees filter errors and network weight errors are bounded and exponentially converge to a neighborhood of the origin globally. The sliding mode controller is designed based on the estimation for the derivatives of system outputs such that the sliding surface converges to zero and the system control input is bounded.

The sliding mode controller can make the system output track the desired output with arbitrarily small tracking error. The filter errors and network weight estimation errors can be made arbitrarily small, and all the system signals are bounded. The proposed method does not need the supper bounds of the unmatched uncertainties and their any order derivatives.

The system output and uncertainties are required to be sufficiently smooth in the proposed method. In practice, this condition is always satisfied generally.

This paper contains very useful advice for researchers on the sliding mode control and the use of neural networks.

The paper presents a new sliding mode controller design method based on recursive derivative estimation of system outputs using neural networks. The paper is aimed at theoretical researchers, especially those who have interest in sliding mode control, neural networks, adaptive techniques, and recursive estimation.

This paper aims at identifying the closed Michaelis‐Menten (M‐M) system. The system governs many phenomena that are based on “enzyme reactions”.

A linearization method is used. The system, however is not homogeneous and consequently earlier identification results obtained by the author cannot be applied. The approach is to show that the closed Michaelis‐Mentan system can be approximated by a sequence of open M‐M systems. The M‐M parameters are then obtained by continuity from the Cauchy's problem solution.

The main results – the identification of a closed M‐M system is described. The linearization of the closed M‐M compartmental system led to a linear nonhomogeneous system. Earlier classical methods could not applied but the “Reasonable Biological Hypothesis” of the author allowed further study.

This study takes a preferred theoretical aspect. A generalization to n, n≥3 is considered to be interesting because we can consider an open compartmental system having n compartments as a closed system with n+1 compartments. Further work on this generalization is contemplated.

This theoretical study will further increase the use compartmental analysis to modelling any phenomena depending only on time.

Provides a methodology for identification of M‐M system using a linear strategy.

This paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme.

To begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach.

Based on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed.

The output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

In the paper piecewise continuous analogues of Liapunov's functions have been introduced for systems of differential equations with impulses. They serve to study the problems for uniqueness, continuity and boundedness of the solutions of nonlinear systems with impulses. The asymptotic behaviour of the bounded solutions has also been considered.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

It is analytically difficult to derive the probability distribution function of waiting (or delay) time at the second or third queue in series of tandem queues. This paper presents a method by which approximation is done through a quasi‐isomorphic system which resembles the second queue in respect of one output, viz delay time. Through extensive simulation experiments these isomorphs have been derived. The procedure of getting a simple system to represent a part of a complex system is practised in cybernetics; this approach appears to have potentiality in studying intractable problems in communications and industrial management.

Fire safety management on offshore platforms has been a matter of major concern since the publication of the Cullen report into the Piper Alpha fire. In order to be able to achieve and maintain an acceptable level of fire risk it is desirable to consider the system as a “dynamic whole”. The intention in this research has been to construct a fire safety management system which is both efficacious and resilient. To this end a systemic approach to fire safety for an offshore platform has been pursued, employing the Viable System Model and the Failure Paradigm Method.