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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The purpose of this paper is to verify that improved immune network can be used to design new controller for engineering.

First, the definition of artificial immune controller is given out. Second, the disadvantage of Varela immune network which is not fit for control system is pointed out. Third, based on the analysis, the Varela immune network is modified for the purpose of designing controller with the mechanisms of immune network. And an immune controller based on improved Varela immune network (improved Varela immune network model (IVINM)‐AIC) is designed out. Its theoretic background is described in detail.

Based on the theoretic analysis and experiment of motor speed control, it is found that Varela immune work can be used to design immune controller. The experiments results show that IVINM‐AIC is much more robust, stable and anti‐delay and less overshoot than classical proportion, integration, and differentiation controller. It is good at controlling nonlinear system which is single input single output (SISO) system. The limitation of IVINM‐AIC is that it is used for simple SISO system.

The theoretic analysis of improved Varela immune network controller is original and it is useful for the analysis and design of new and complex immune controller. The experiment design is useful for comparison of new test in future.

The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system.

In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor.

The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range.

In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.

To develop a new predictive control scheme based on neural networks for linear and non‐linear dynamical systems.

The approach relies on three different multilayer neural networks using input‐output information with delays. One NN is used to identify the process under control, the other is used to predict the future values of the control error and finally the third one is used to compute the magnitude of the control input to be applied to the plant.

This scheme has been tested by controlling discrete‐time SISO and MIMO processes already known in the control literature and the results have been compared with other control approaches with no predictive effects. Transient behavior of the new algorithm, as well as the steady state one, are observed and analyzed in each case studied. Also, online and offline neural network training are compared for the proposed scheme.

The theoretical proof of stability of the proposed scheme still remains to be studied. Conditions under which non‐linear plants together with the proposed controller present a stable behavior have to be derived.

The main advantage of the proposed method is that the predictive effect allows to suitable control complex non‐linear process, eliminating oscillations during the transient response. This will be useful for control engineers to control complex industrial plants.

This general approach is based on predicting the future control errors through a predictive neural network, taking advantage of the NN characteristics to approximate any kind of relationship. The advantage of this predictive scheme is that the knowledge of the future reference values is not needed, since the information used to train the predictive NN is based on present and past values of the control error. Since the plant parameters are unknown, the identification NN is used to back‐propagate the control error from the output of the plant to the output of the controller. The weights of the controller NN are adjusted so that the present and future values of the control error are minimized.

The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot.

The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration.

This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online.

Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

The purpose of the paper is to analyze the active suppression of the aeroelastic vibrations of ailerons with strongly nonlinear characteristics by neural network/reinforcement learning (NN/RL) control method and comparing it with the classic robust methods of suppression.

The flexible wing and aileron with hysteresis nonlinearity is treated as a plant-controller system and NN/RL and robust controller are used to suppress the nonlinear aeroelastic vibrations of aileron. The simulation approach is used for analyzing the efficiency of both types of methods in suppressing of such vibrations.

The analysis shows that the NN/RL controller is able to suppress the nonlinear vibrations of aileron much better than linear robust method, although its efficiency depends essentially on the NN topology as well as on the RL strategy.

Only numerical analysis was carried out; thus, the proposed solution is of theoretical value, and its application to the real suppression of aeroelastic vibrations requires further research.

The work shows the NN/RL method has a great potential in improving suppression of highly nonlinear aeroelastic vibrations, opposed to the classical robust methods that probably reach their limits in this area.

The work raises the questions of controllability of the highly nonlinear aeroelastic systems by means of classical robust and NN/RL methods of control.

This paper aims to investigate the emergence of the enabling characteristics of new budgetary practices and their implications for the role of controller.

The longitudinal perspective of this qualitative case study is based on interviews of controllers and managers involved in budgetary work. This study monitored the four enabling characteristics of management control, namely, repair, internal transparency, global transparency and flexibility (Adler and Borys, 1996), related to the new budgeting practices in one global paper company.

The findings of the study demonstrate that the implementation of rolling forecasting was a major attempt at “repair” to remedy the incompleteness of accounting information, which made controllers experts in producing and delivering more realistic forward-looking information in the organization. The increasing internal and global transparency of new budgetary practices enabled controllers at various levels of organization to develop new competences, which helped controller network to build a holistic view of the totality of control and supply more relevant information in organization. Moreover, the inherent flexibility of the system was a major condition for improving organizational effectiveness in budgetary work. However, the study shows that the controller’s attitude towards enabling formalization is not necessarily positive if the system is not aligned with professional mindset and competence.

This study adds to the understanding of the complementarity between new developments of budgeting and controller role by addressing the enabling uses of management control systems, which have the potential to enhance the controller role change.

The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control.

This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments.

The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture.

The proposed method can effectively solve the occlusion problem in visual servo control.

A neural network controller is proposed for the motion control of robot manipulators with force/torque feedback signals. This controller is trained with reinforcement learning algorithms and a model is extracted from the synaptic weights within the neural network. This model is continuously refined by the feedback signals to ensure its validity even in a stochastic and non‐stationary environment. With this model and the real‐time force/torque feedback data, the robot can acquire a fine skill for a particular assembly task for which it is trained.

In this paper, two omni‐directional mobile vehicles are designed and controlled implementing distributed mechatronics controllers. Omni‐directionality is the ability of mobile vehicle to move instantaneously in any direction. It is achieved by implementing Mecanum wheels in one vehicle and conventional wheels in another vehicle. The control requirements for omni‐directionality using the two above‐mentioned methods are that each wheel must be independently driven, and that all the four wheels must be synchronized in order to achieve the desired motion of each vehicle.

Distributed mechatronics controllers implementing Controller Area Network (CAN) modules are used to satisfy the control requirements of the vehicles. In distributed control architectures, failures in other parts of the control system can be compensated by other parts of the system. Three‐layered control architecture is implemented for; time‐critical tasks, event‐based tasks, and task planning. Global variables and broadcast communication is used on CAN bus. Messages are accepted in individual distributed controller modules by subscription.

Increase in the number of distributed modules increases the number of CAN bus messages required to achieve smooth working of the vehicles. This requires development of higher layer to manage the messages on the CAN bus.

The limitation of the research is that analysis of the distributed controllers that were developed is complex, and that there are no universally accepted tool for conducting the analysis. The other limitation is that teh mathematical models of the mobile robot that have been developed need to be verified.

In the design of omni‐directional vehicles, reliability of the vehicle can be improved by modular design of mechanical system and electronic system of the wheel modules and the sensor modules.

The paper tries to show the advantages of distributed controller for omni‐directional vehicles. To the author's knowledge, that is a new concept.