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This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.

This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.

This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.

This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.

This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor.

The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations.

In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller’s performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor.

The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor.

This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online.

The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

The purpose of this paper is to propose a novel concept of model prediction control (MPC) parameter optimization method, which is based on pigeon-inspired optimization (PIO) algorithm, with the objective of optimizing the unmanned air vehicles (UAVs) controller design progress.

The PIO algorithm is proposed for parameter optimization in MPC, which provides a new method to get the optimal parameter.

The PIO algorithm is a new swarm optimization method, which consists of two operators, so it can be better adapted for the optimal problems. The comparative consequences results with the particle swarm optimization (PSO) demonstrate the effectiveness of the PIO algorithm, and the superiority for global search is also verified in various cases.

PIO algorithm can be easily applied to practice and help the parameter optimization of the MPC.

In this paper, we first present the concept of using the PIO algorithm for parameter optimization in MPC so as to achieve the global best optimization. By using the PIO algorithm, the choice of the parameter could be easier and more effective. The authors also applied the algorithm to the designing of the MPC controller to optimize the response of the pitch rate of UAV.

This paper aims to identify and appropriately respond to any socio-technical gaps within organisational information and cybersecurity practices. This culminates in the equal emphasis of both the social, technical and environmental factors affecting security practices.

The socio-technical systems theory was used to develop a conceptual process model for analysing organisational practices in terms of their social, technical and environmental influence. The conceptual process model was then applied to specifically analyse some selected information and cybersecurity frameworks. The outcome of this exercise culminated in the design of a socio-technical systems cybersecurity framework that can be applied to any new or existing information and cybersecurity solutions in the organisation. A framework parameter to help continuously monitor the mutual alignment of the social, technical and environmental dimensions of the socio-technical systems cybersecurity framework was also introduced.

The results indicate a positive application of the socio-technical systems theory to the information and cybersecurity domain. In particular, the application of the conceptual process model is able to successfully categorise the selected information and cybersecurity practices into either social, technical or environmental practices. However, the validation of the socio-technical systems cybersecurity framework requires time and continuous monitoring in a real-life environment.

This research is beneficial to chief security officers, risk managers, information technology managers, security professionals and academics. They will gain more knowledge and understanding about the need to highlight the equal importance of both the social, technical and environmental dimensions of information and cybersecurity. Further, the less emphasised dimension is posited to open an equal but mutual security vulnerability gap as the more emphasised dimension. Both dimensions must, therefore, equally and jointly be emphasised for optimal security performance in the organisation.

The application of socio-technical systems theory to the information and cybersecurity domain has not received much attention. In this regard, the research adds value to the information and cybersecurity studies where too much emphasis is placed on security software and hardware capabilities.

This paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO).

The production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation.

The results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions.

This research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level.

PSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

The purpose of this paper is to apply some surrogate-assisted optimization techniques in order to improve the performances of a five-phase permanent magnet machine in the context of a complex model requiring computation time.

An optimal control of four independent currents is proposed in order to minimize the total losses with the respect of functioning constraints. Moreover, some geometrical parameters are added to the optimization process allowing a co-design between control and dimensioning.

The optimization results prove the remarkable effect of using the freedom degree offered by a five-phase structure on iron and magnets losses. The performances of the five-phase machine with concentrated windings are notably improved at high speed (16,000 rpm).

The effectiveness of the method allows solving the challenge which consists in taking into account inside the control strategy the eddy-current losses in magnets and iron. In fact, magnet losses are a critical point to protect the machine from demagnetization in flux-weakening region.

Taking into account the factors of torque saturation and angular velocity limitation during the actual attitude maneuver of the satellite, as well as the difficulty of parameter selection in the design of attitude control algorithm, the purpose of this paper is to propose a satellite magnetic/momentum wheel attitude control technology based on pigeon-inspired optimization (PIO) cascade-saturation control law optimization.

The optimal parameters are calculated through the PIO algorithm and then the parameters are used in the cascade-saturation control law to control the actuator findings-mathematical simulation results show that the cascade-saturation control law optimization algorithm based on PIO can shorten the adjustment time and reduce the steady-state error.

Compared with traditional attitude maneuver control with given parameters, the PIO algorithm can accurately calculate the optimal parameters needed to achieve the control objective and this method has better stability and higher accuracy.

The innovative PIO algorithm is used to calculate the optimal parameters, and the cascade saturation control law is used to control the actuator. Compared with the traditional algorithm, the regulation time is shortened and the steady-state error is reduced.

In the need to optimize the energy efficiency, control structures can have a positive effect by tracking the optimal operating point according to the speed and mechanical load of the motor. The purpose of this paper is to present an energy-efficient scalar control for squirrel-cage induction motors (IMs), taking into consideration the effect of core losses.

The proposed technique is based on the modification of the stator flux reference, to track the best efficiency point. The optimal flux values are computed through an improved model of the IM including core losses, then stored in a look-up table.

Simulations of the proposed scalar control are carried out, and results show the efficiency improvement when the flux is optimized especially at low load cases. Results were validated experimentally on two motors compliant with different efficiency standards.

The proposed approach can be used in several fields and applications using the scalar-controlled IM with a proper implementation in variable speed drives, as in the cases of pumps, compressors and blowers.

The proposed technique is compared to existing optimization methods in literature, and the results show an improvement in the dynamic performance and in the response delays. The approach is also compared to an optimization technique used in industries like Leroy-Somer for variable speed drives, and efficiency improvements are shown.