Robust fault-tolerant leader-follower control of four-wheel-steering mobile robots using terminal sliding mode

This paper addresses the actuator failure compensation problem of non-linear fourwheel-steering mobile robots based on vehicle kinematics, undergoing both known and unknown failures causing degenerated steering performance or wheels stuck at some observable angles. Terminal sliding mode control technique is used to guarantee the tracking stability infinite time with the presence of actuator fault. Simulation results are given to illustrate the effectiveness of the proposed control scheme. © Institution of Engineers Australia 2012.

Adaptive fuzzy multi-surface sliding control of multiple-input and multiple-output autonomous flight systems

In this study, we proposed an adaptive fuzzy multi-surface sliding control (AFMSSC) for trajectory tracking of 6 degrees of freedom inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). It is shown that an adaptive fuzzy logic-based function approximator can be used to estimate the system uncertainties and an iterative multi-surface sliding control design can be carried out to control flight. Using AFMSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. It is proved that the AFMSSC system guarantees asymptotic output tracking and ultimate uniform boundedness of the tracking error. Simulation results are presented to validate the analysis.

Control of polystyrene batch reactor using fuzzy logic controller

Control of polymerization reactors is a challenging issue for researchers due to the complex reaction mechanisms. A lot of reactions occur simultaneously during polymerization. This leads to a polymerization system that is highly nonlinear in nature. In this work, a nonlinear advanced controller, named fuzzy logic controller (FLC), is developed for monitoring the batch free radical polymerization of polystyrene (PS) reactor. Temperature is used as an intermediate control variable to control polymer quality, because the products quality and quantity of polymer are directly depends on temperature. Different FLCs are developed through changing the number of fuzzy membership functions (MFs) for inputs and output. The final tuned FLC results are compared with the results of another advanced controller, named neural network based model predictive controller (NN-MPC). The simulation results reveal that the FLC performance is better than NN-MPC in terms of quantitative and qualitative performance criterion.

Steric control over molecular structure and supramolecular association exerted by tin- and ligand-bound groups in diorganotin carboxylates

Structural data (X-ray and solution and solid-state ^₁₁₉Sn NMR) show that skew-trapezoidal-bipyramidal diorganotin compounds of 2-quinaldate are invariably monomeric, owing to the steric bulk of the carboxylate ligand. In contrast, most of the analogous compounds of 2-picolinate (2-pic) can increase their coordination number by polymerization or the incorporation of solvent in their coordination sphere in the solid state. The exceptional compound is tBu₂Sn(2-pic)₂ (3), for which no increase in coordination number is apparent, a result that is correlated with the bulky tert-butyl groups. Thus, judicious choice of tin or ligand substituents can be exploited to dictate coordination number and/or the degree of supramolecular aggregation in the investigated systems.

Observer-based control of multi-agent systems under decentralized information structure

This paper addresses the problem of decentralized implementation of a global state feedback controller for multi-agent systems. The system is assumed to be under the constraint of a complete decentralized information structure. The decentralization of the control task is achieved through the construction of low-order decentralized functional observers with the purpose of generating the required corresponding control signal for each local control station. A design procedure is developed for obtaining an approximate solution to the design of the observers. Stability analysis is provided for the global system using the proposed observer-based approach. A numerical example is given to illustrate the design procedure and cases when the observers' order increases from the lowest value.

Trimethylene-bridged tetraorganodistannoxanes{[Me3SiCH2(RCOO)Sn(CH2)3Sn(OOCR)CH2SiMe3]O}n(R=Ph,2,4-Me2C6H3): control of structure by variation of R*

The synthesis of trimethylene-bridged carboxylate-substituted tetraorganodistannoxanes {[Me_3SiCH_2(RCOO)Sn(CH_2)_3Sn(OOCR)CH_2SiMe_3]O}_n (1, R = Ph; 2, R = 2,4-Me_2C_6H_3) is reported. Depending on the structure of R, in the solid state these compounds are either dimers (1, n = 2, cis-isomer) with a ladder-type structure or tetramers (2, n = 4) with a double ladder-type structure.

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.

Corporate governance structure and firm performance : empirical evidence from Brusa Malaysia, Kuala Lumper

The issue of corporate governance has been emerging as important phenomena that has been searched extensively both in developed countries due to its strategic impact on the monitoring of management activities and firms’ performance. Yet little attempt has been made in developing countries like Malaysia to ascertain what constitute corporate governance and its impact on firm's performance. Therefore, this study aims at examining the structure of the corporate governance and its impact on firm’s performance. This study is based on 100 firms, which are the component of the Composite Index (CI) serve as market barometer. This study employs cross-sectional annual multiple regression model to examine, what constitutes the corporate governance structure and its impact on performance of the firm. The analysis was based on annual regression over 5 years period from 1997 through 2001. Three different blend of surrogate for corporate governance were developed for good corporate governance structure. These are the independent non-executive (outside) directors, audit committee and remuneration committee. To isolate the size effect from the impact of corporate governance structure on firm’s performance, firm’s size was also included are variable in the model. The ratio of net income before tax to total asset is used as a surrogate for firm’s performance. Evidence from the study indicates that there is partial relation between corporate governance structure and corporate performance. The presence of both audit and remuneration committee serves as an important monitoring device to control management activities that lead to increase firm's performance. While on average, the presence of independent nonexecutive directors does not provide any significant explanation for the firm's performance. However, the firm size appears to have significant impact on corporate performance.

Development of asymmetric rolling for the better control over structure and mechanical properties in IF steel

In the present study, the effects of kinematic and geometric asymmetries in rolling during multi-pass processing of IF steel are examined. The theoretical investigation by final element simulations and experimental investigations by means of electron-backscatter diffraction analysis and tensile tests suggest that asymmetric rolling increases the total imposed strain compared to symmetric rolling, and largely re-distributes the strain components due to additional shear. This enhances the intensity of grain refinement, strengthens and tilts crystallographic orientations, and increases mechanical strength. The effect is highest in the asymmetric rolling with differential roll diameters.

Sliding mode-like learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode-like learning control scheme is developed for a class of single input single output (SISO) complex systems. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the uncertain complex dynamical systems. Second, a sliding mode-like learning control is designed to drive the sliding variable to converge to the sliding surface, and the system states can then asymptotically converge to zero on the sliding surface. The advantages of this scheme are that: 1) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; 2) the closed-loop system behaves with a strong robustness with respect to uncertainties; 3) the control input is chattering-free. The sufficient conditions for the sliding mode-like learning control to stabilise the global fuzzy model are discussed in detail. A simulation example for the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.

The separation of ownership and control under a happiness-based theory of the corporation

Explores the happiness-based theory of the corporation, suggesting that there is no conflict between the pursuance of economic and social objectives on the basis that their interplay is required to facilitate shareholder happiness. Considers: (1) the Berle-Means hypothesis and the separation of ownership and control, the dominant governance structure for large companies; (2) a happiness-based perspective on the separation; and (3) law reform applicable to a happiness-based theory. Argues that the separation of ownership and control is not in shareholders' best interests because the structure is not conducive to the happiness of individual shareholders and should be reformed.