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The purpose of this paper is to investigate the relationship between capabilities and renewal of organizational and business know-how in franchise networks.

This work uses a comparative case study of adaptive, absorptive and innovative capabilities to investigate knowledge renewal in 16 franchise networks.

The findings show that adaptive and innovative capabilities complement each other to foster know-how renewal. Furthermore, networks without internal R&D need to mobilize adaptive, absorptive and innovative capabilities to renew both organizational and business know-how. The findings also highlight that the three capabilities are interconnected.

The results of this research could provide insights for franchise networks to regenerate their knowledge base and ensure their long-term survival.

The underlying capabilities that explain organizational and business know-how renewal in franchises have not been investigated.

In this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.

In the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.

The simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.

The research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.

This paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

This paper aims to propose an adaptive unstructured finite volume procedure for efficient prediction of propellant feedline dynamics in fluid network.

The adaptive strategy is based on feedback control of errors defined by changes in key variables in two subsequent time steps.

As an evaluation of the proposed approach, two feedline dynamics problems are formulated and solved. First problem involves prediction of pressure surges in a pipeline that has entrapped air and the second is a conjugate heat transfer problem involving prediction of chill down of cryogenic transfer line. Numerical predictions with the adaptive strategy are compared with available experimental data and are found to be in good agreement. The adaptive strategy is found to be efficient and robust for predicting feedline dynamics in flow network at reduced CPU time.

This study uses an adaptive reduced-order network modeling approach for fluid network.

The purpose of this paper is to conceptualize how information technology (IT) enables supply chain (SC) network capabilities, which is to capitalize on SC’s existing set of resources and, at the same time, manage new combinations of SC resources to meet future market needs. The paper also develops SCM framework associated with IT-enabled SC network capabilities.

Through a case study of a leading Taiwanese petrochemical corporation, qualitative data were gathered on the IT-related SC management practices, in terms of network resource mobilizing and adaptive co-management arrangements to enable SC network capability. This research is based primarily on the interviews of the case company, supplemented by archived documents, published books, and in-depth observations.

Based on the evidence from the case, this study inductively develops a model that includes the operating processes with IT-enabled activities to achieve ambidextrous SC network capability, and the relevant framework functions in network resources and co-management activities include information co-governance, information interoperability, community engagement strategy, cyber-physical dexterity, and control enactment, which lead the SC alliances improvements for dynamic environmental changes.

Practitioners may derive strategies and tactics from the current findings to help them implement innovative information technologies and setup SC framework, during SC network capability development, to achieve SC’s sustainable competence in a dynamic market.

Researchers and practitioners may obtain a more complete view of IT-enabled SC network capability development. The proposed model reveals that developing IT-enabled SC network capabilities is a dynamic process whereby an organization’s major SC managerial activities are divided into specific network resource mobilizing and adaptive co-management arrangements.

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.

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

This paper aims to propose an empirically grounded governance framework based on complex adaptive systems (CAS) principles to facilitate formation of well-connected regional supply chains that foster economic development, adaptability and resilience of mining regions.

This study is an exploratory case study of the South Australian (SA) mining industry that includes 38 semi-structured interviews with the key stakeholders and structural analysis of the regional supply network (RSN).

Findings demonstrate the applicability of the CAS framework as a structured approach to the governance of the mining industry regional supply chains. In particular, the findings exemplify the relationship between RSN governance, its structure and interconnectivity and their combined impact on the adaptability and resilience of mining regions.

The data set analysed in the current study is static. Longitudinal data would permit a deeper insight into the evolution of the RSN structure and connectivity. The validity of the proposed framework could be further strengthened by being applied to other industrial domains and geographical contexts.

The proposed framework offers a novel insight for regional policy-makers striving to create an environment that facilitates the formation of well-integrated regional supply chains in mining regions through more focussed policy and strategies.

The proposed framework is one of the first attempts to offer a holistic structured approach to governance of the regional supply chains based on CAS principles. With the current transformative changes in the global mining industry, policy-makers and supply chain practitioners have an urgent need to embrace CAS and network paradigms to remain competitive in the twenty-first century.

The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (D_O) concentration and nitrate nitrogen (S_No) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.

By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.

The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system.

In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft.

The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied.

For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage.

In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.