Vector control of a position sensorless SPMSM drive with RNN based stator flux estimator

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on single layer Recurrent Neural Network (RNN) is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The RNN estimator is used to estimate flux components along the stator fixed stationary axes. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The proposed estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implememnt in a practical environment

FEACS: a flexible and efficient access control scheme for cloud computing

In the past few years, cloud computing has emerged as one of the most influential paradigms in the IT industry. As promising as it is, this paradigm brings forth many new challenges for data security because users have to outsource sensitive data on untrusted cloud servers for sharing. In this paper, to guarantee the confidentiality and security of data sharing in cloud environment, we propose a Flexible and Efficient Access Control Scheme (FEACS) based on Attribute-Based Encryption, which is suitable for fine-grained access control. Compared with existing state-of-the-art schemes, FEACS is more practical by following functions. First of all, considering the factor that the user membership may change frequently in cloud environment, FEACS has the capability of coping with dynamic membership efficiently. Secondly, full logic expression is supported to make the access policy described accurately and efficiently. Besides, we prove in the standard model that FEACS is secure based on the Decisional Bilinear Diffie-Hellman assumption. To evaluate the practicality of FEACS, we provide a detailed theoretical performance analysis and a simulation comparison with existing schemes. Both the theoretical analysis and the experimental results prove that our scheme is efficient and effective for cloud environment.

A new sliding mode-based learning control scheme

 A new sliding mode-based learning control scheme for a class of SISO dynamic systems is developed in this paper. It is seen that, based on the most recent information on the closed-loop stability, a recursive learning chattering-free sliding mode controller can be designed to drive the closed-loop dynamics to reach the sliding mode surface in a finite time, on which the desired closed-loop dynamics with the zero-error convergence can be achieved.

Establishment of temperature control scheme for microbioreactor operation using integrated microheater

This paper presents design and fabrication of a microbioreactor platform, and implementation of two temperature control methods (i.e. on-off and PID) and their performance evaluation on the microbioreactor platform (working volume ~300 μL). The temperature of the microbioreactor content is controlled by using a subminiature heater placed underneath the microbioreactor and is measured with a miniature Pt 100 sensor. The microbioreactor is also integrated with a magnetic stirring capacity and a water evaporation control scheme. Programs for the two temperature control methods are written in LabVIEW software and implemented by interfacing them with a data acquisition card. It is shown that by implementing on–off and PID temperature control methods, the temperature of the microbioreactor content can be tightly controlled with an accuracy of approximately ±0.5 °C of the set point values. Both control methods also provide a good response and settling time values (i.e. around 2 min). Contrary to the on/off control method, the PID control method requires no adjustments whenever the set-point values are modified. The PID temperature control method works well for the entire tested range.

Distributed agent-based control scheme for single-phase parallel inverters in microgrids with photovoltaic systems

In this paper, an agent-based distributed control scheme is presented to control single-phase parallel inverters in solar photovoltaic (PV) systems connected to microgrids. A communication assisted multi-agent framework is developed within microgrids where agents perform their tasks in a distributed manner with an aim of stabilizing load voltage and current under normal and faulted conditions through the asymptotic tracking of the reference current signal. The distributed agent-based control scheme requires information from the neighboring agents through communication network to decide control actions. The proposed control scheme utilizes Ziegler-Nichols (Z-N) tuning approach to design proportional integral (PI) controllers for controlling inverters within the multi-agent system (MAS). A microgrid with parallel inverter-connected solar PV systems is considered for simulations under normal and faulted conditions where results show the excellency of the proposed agent-based scheme in comparison to the conventional scheme without MAS.

Anonymous credential-based access control scheme for clouds

In the cloud, data is usually stored in ciphertext for security. Attribute-based encryption (ABE) is a popular solution for allowing legal data users to access encrypted data, but it has high overhead and is vulnerable to data leakage. The authors propose an anonymous authorization credential and Lagrange interpolation polynomial-based access control scheme in which an access privilege and one secret share are applied for reconstructing the user's decryption key. Because the credential is anonymously bounded with its owner, only the legal authorized user can access and decrypt the encrypted data without leaking any private information.

A robust adaptive hybrid force/position control scheme of two planar manipulators handling an unknown object interacting with an environment

This paper presents a robust hybrid force/position control scheme of two cooperative manipulators handling an unknown object interacting with an unknown environment. The uncertainty of the object is considered in the weight, length, and the position of centre of mass (COM). The environment is assumed to have an unknown but high stiffness. A hybrid force/position control algorithm is designed for the known system and environment case. The exponential convergence of the position and the interaction force with the environment is proved using the Lyapunov direct method. Similarly, in the unknown object and environment case, and in the presence of bounded disturbances on the robots and the object, an adaptive sliding mode hybrid force/position control scheme is designed. The asymptotic convergence of the object's position and the constraint force is guaranteed using the proposed control methodology. The internal forces and moments between the object and robots are controlled independently of the object's motion and environmental interaction forces. Simulation results confirm the performance and effectiveness of the suggested control methodologies.

A congestion control scheme based on fuzzy logic in wireless body area networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Space vector modulation based fast speed response field-orientation control of induction motor drive with adaptive neural integrator

This paper presents a novel fast speed response control strategy for the poly-phase induction motor drive system based on flux angle. The control scheme is derived in rotor field coordinates and employs the estimation of the rotor flux and its position. An adaptive notch filter is proposed to eliminate the dc component of the integration of signals used for the rotor flux estimation. To improve the performance of the rotor flux estimator, derivative term of the back emf is incorporated in the system. The voltage components in the synchronous reference frame are generated in the controllers which are transformed to stationary reference frame for driving the motor. Space vector modulation technique is used here. Simulation of the drive system was carried out and the results were compared with those obtained for a system that produces the above mentioned voltage components using the conventional PI controller. It is observed that the proposed control methodology provides faster response than the conventional PI controller incorporated system.

A loop-shaping approach to intelligent control

In this paper, we propose a loop-shaping approach to in telligent control with dynamically constructed neurocon troller. In the proposed control scheme, the process uncer tainly is reduced in the controller rather than in the process, without explicit identification of the process under control. The inherent noise/distrurbances in the process are utilized to satisfy persistency of excitation condition. The use of a reference model in form of a filter allow the frequency response of the closed-loop to be adapted in line with the changes in frequency response of the filter. The approach is evaluated on the example of control of polymerization reactor with promising results.

Dynamic output feedback sliding-mode control using pole placement and linear functional observers

This paper presents a methodological approach to design dynamic output feedback sliding-mode control for a class of uncertain dynamical systems. The control action consists of the equivalent control and robust control components. The design of the equivalent control and the sliding function are based on the pole-placement technique. Linear functional observers are developed to implement the sliding function and the equivalent control. Stability of the resulting system under the proposed control scheme is guaranteed. A numerical example is given to demonstrate its efficacy.

Dynamic output feedback control of single-link flexible manipulators

This paper presents an efficient technique to design dynamic feedback control scheme for single-link flexible manipulators.  A linear model can be derived for the robotic system using the assumed-mode method.  Conventional techniques such as pole-placement or LQR require physical measurements of all systme states,  posing a stringent requirement for its implementation.  To overcome this problem, a low-order state functional observer is proposed here for reconstruction of the state feedback control action.  The observer design involves solving an optimisation problem with the objective to generate a feedback gain that is as close as possible to that of the required feedback controller.  A condition for robust stability of the closed-loop system under the observer-based control scheme is given.  The attractive features of the propsed technique are the resulted functional state observer is of a very low order and it requires only sensor measurements of only the output- the tip position of the arm.

Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Uplink power control via adaptive hmm estimation

Transmit power control is an important consideration in the cellular system design since it increases system capacity, improves QoS and reduces multi-user interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss of the fading channel is presented. Making power control decisions based on the measured received power allows to model the fading channel pathloss dynamics with a Hidden Markov Model. Applying the online HMM identification algorithm enables accurate estimation of the real pathloss which ensures efficient performance of the suggested power control scheme.