Dynamic control of skilled and unskilled labour task assignments

Efficient allocation of skilled and non-skilled workers allow a company to improve productivity and usually requires an understanding of personnel capability, operating conditions and resource availability. This paper examines a labour control strategy that optimises labour skill level, utilisation, task execution time and processing error. The proposed controller manages different labour groups in a multiple work cell environment, providing real-time job assignment, as well as guiding and navigation features. These features can be used to enhance the performance of existing MRP-based or Just-In-Time production systems. A discrete event simulation-based manufacturing model has been developed to assess the performance of the labour controller. Experiments conducted for the selected production scenarios have demonstrated a productivity improvement when using the proposed control. A second experiment has shown that when a skilled labour uses the labour controller to guide them through the job, their utilisation also increases. The proposed controller also has potential application in other domains, such as minimising the shopping time at a supermarket

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.

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.

Enhancement of transient stability limit and voltage regulation with dynamic loads using robust excitation control

In stressed power systems with large induction machine component, there exist undamped electromechanical modes and unstable montonic voltage modes. This article proposes a sequential design of an excitation controller and a power system stabiliser (PSS) to stabilise the system. The operating region, with induction machines in stressed power systems, is often not captured using a linearisation around an operating point, and to alleviate this situation a robust controller is designed which guaruntees stable operation in a large region of operation. A minimax linear quadratic Gaussian design is used for the design of the supplementary control to automatic voltage regulators, and a classical PSS structure is used to damp electromechanical oscillations. The novelty of this work is in proposing a method to capture the unmodelled nonlinear dynamics as uncertainty in the design of the robust controller. Tight bounds on the uncertainty are obtained using this method which enables high-performance controllers. An IEEE benchmark test system has been used to demonstrate the performance of the designed controller

H∞ dynamic output feedback control of power systems with electric vehicles

This paper presents a H∞ dynamic output feedback control scheme for load frequency control (LFC) of interconnected power systems with multiple input timedelays. In this study, electric vehicles (EVs) are participated in the LFC to support reheated thermal power units to rapidly suppress load and frequency fluctuations. A mathematical model of an interconnected power system is first introduced. This model takes into consideration of the different time delays in control inputs; specifically the communication/information delays between the control center and the fleet of EVs. We then derive stabilization conditions in terms of feasible linear matrix inequalities (LMIs) for the proposed system and develop an effective algorithm to parameterize H∞ controllers ensuring stability of the closed-loop system with H∞ performance. Extensive simulations are given to show the effectiveness of the proposed control method.

Cold rolling mill thickness control using the cascade-correlation neural network

The improvements in thickness accuracy of a steel strip produced by a tandem cold-roIling mill are of substantial interest to the steel industry. In this paper, we designed a direct model-reference adaptive control (MRAC)  scheme that exploits the natural level of excitation existing in the closed-loop with a dynamically constructed cascade-correlation neural network (CCNN) as a controller for cold roIling mill thickness control. Simulation results show that the combination of a such a direct MRAC scheme and the dynamically constructed CCNN significantly improves the thickness accuracy in the presence of disturbances and noise in comparison with to the conventional PID controllers.

A reinforcement learning approach for robot control in an unknown environment

In this paper, a control approach based on reinforcement learning is present for a robot to complete a dynamic task in an unknown environment. First, a temporal difference-based reinforcement learning algorithm and its evaluation function are used to make the robot learn with its trials and errors as well as experiences. Second, the simulation are carried out to adjust the parameters of the learning algorithm and determine an optimal policy by using the models of a robot. Last, the effectiveness of the present approach is demonstrated by balancing an inverse pendulum in the unknown environment.

Mobility prediction based routing for minimizing control overhead in mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided routing (P-LAR) protocol. Simulation study and analytical results of the of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Robust control applied to metal spraying for rapid tooling

The spray forming process is a novel method of rapidly manufacturing tools and dies for stamping and injection operations. The process sprays molten tool steel from a set of arc spray guns onto a ceramic former to build up a thick steel shell. The volumetric contraction that occurs as the steel cools is offset by a volumetric expansion taking place within the sprayed steel, which allows the dimensional accurate tools to be produced. To ensure that the required phase transformation takes place, the temperature of the steel is regulated during spraying. The sprayed metal acts both as a source of mass and a source of heat and by adjusting the rate at which metal is sprayed; the surface temperature profile over the surface of the steel can be controlled. The temperature profile is measured using a thermal imaging camera and regulated by adjusting the rate at which the guns spray the steel. Because the temperature is regulated by adjusting the feed rate to an actuator that is moving over the surface, this is an example of mobile control, which is a class of distributed parameter control. The dynamic system has been controlled using a PI controller before. The paper describes the application of H∞ tracking type controller as the desire was for the average temperature to follow a desired profile. A study on the controllability of the underlying system was aimed at.

Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients

The theory of H/sup /spl infin// optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.

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%.

The mother-daughter guilt dynamic : effects of type 1 diabetes during life transitions

Aim. The aim of the study was to explore and describe the strategies young women with type 1 diabetes used to manage life transitions. The paper describes one aspect of how guilt dynamic often operates between mothers and daughters and how the women managed the guilt dynamic to create stability in their lives.
Background. When a child is diagnosed with diabetes, major transitional changes occur in the relationships between the mother and her child. The changes affect the psychological and social aspects of their lives and have a major impact on how young women manage their diabetes. A guilt dynamic between mothers and young women with diabetes emerged as a major theme in a larger study that investigated how young women with diabetes managed life transitions. Although the literature indicates that mothers of chronically ill children experience guilt feelings towards their children, little research was identified that addressed the emotional dynamics between mothers and daughters with diabetes.
Design. Using grounded theory method, interviews were conducted with 20 women with type 1 diabetes and five mothers during 2002 and 2003. Constant comparative analysis was used to analyse the data and develop an in-depth understanding of the experience of living with diabetes during life transitions.
Findings. The findings revealed that guilt feelings created a two-way dependency between mothers and their daughters with diabetes. The two-way dependency involved feelings of being a burden to each other, difficulty balancing responsibilities for diabetes management, difficulty relinquishing emotional and social dependency especially during life transitions. In addition, these issues were rarely discussed openly with each other or with health professionals. The findings provide additional information about the human experience of the mother–daughter relationship and the effect on coping with diabetes in the context of life transitions.
Conclusions. Understanding the impact diabetes has on the emotional and social well being of both women with type 1 diabetes and their mothers is critical in planning appropriate support for both groups. Most importantly, it is critical to understand the guilt dynamic that operates during young women with diabetes' life transitions when the daughters' dependency on their mother's control and responsibility for diabetes management undergo changes resulting in emotional responses, especially guilt feelings.
Relevance to clinical practice. Health professionals need to understand the emotional and social impact of the guilt dynamics between young women with type 1 diabetes and their mothers. Adequate and appropriate support can minimize the guilt feelings and enhance stability and quality of life for both mothers and their daughters, especially during major life transitions, such as motherhood.