Showing papers in "International Journal of Systems Science in 1996"
TL;DR: Two network flow methods are presented in this paper to optimize a city emergency evacuation plan, and by solving a shortest path problem on this graph, it is obtained the shortest evacuation plan.
Abstract: Two network flow methods are presented in this paper to optimize a city emergency evacuation plan. The problem is to assign each resident of the city to one of the places of refuge (PR) in preparation for major disasters. We model the city as an undirected graph, and by solving a shortest path problem on this graph, we obtain the shortest evacuation plan. The second model takes the capacity limit on each PR explicitly into account. The problem can then be transformed into a minimal cost flow problem on a slightly modified graph. We can evaluate the efficiency of the current city evacuation plan by comparing this against the optimal solutions of the above stated problems. Also, various pieces of information obtainable from these solutions can be utilized in evaluating the current evacuation policy. In addition, sensitivity analysis can be performed to answer various what-if questions
194 citations
TL;DR: A method that incorporates the concept of Pareto's domination would be more interesting because it would permit more general use and be more satisfactory to obtain an optimal surface in which the user will be able to choose his own working conditions.
Abstract: Most optimization problems consist in reconciling multiple objectives with each other, particularly in food processes. For example, it is necessary to optimize different parameters such as texture, flavour, and so on, in order to formulate a new product; before using bacteria or yeasts, it is very important to find an optimal culture medium for cell growth or end product synthesis, for instance. Traditionally, objectives were either combined lo form a scalar objective, through a linear combination of multiple attributes;, or else only one was optimized and the others were turned into constraints. As these techniques depended on the user's choice, they were not adapted to solve multiple-objective problems found in the food industry, where it is more satisfactory to obtain an optimal surface in which the user will be able to choose his own working conditions. Consequently, a method that incorporates the concept of Pareto's domination would be more interesting because it would permit more general use. This w...
190 citations
TL;DR: The problem of precisely controlling (within sensor resolution) the height of a steel ball above the ground by levitating it against the force of gravity using an electromagnet using a standard linear state feedback controller.
Abstract: The problem of precisely controlling (within sensor resolution) the height of a steel ball above the ground by levitating it against the force of gravity using an electromagnet is considered. The state variables used to model the system are the ball's position below the magnet, the ball's speed and the current in the electromagnet. Two state-space controllers are compared in terms of their performance in controlling the ball's position. The first controller is based on feedback linearization where a nonlinear state-space transformation along with nonlinear state feedback is used to linearize the system exactly. A linear controller is then used on the resulting system to control the ball's position. As a direct measurement of ball speed is not available, a nonlinear observer with linear error dynamics is used to estimate the speed. The second controller is a standard linear state feedback controller whose design is based on a linear model found by perturbing the nonlinear system model about an operating po...
176 citations
TL;DR: The Haar wavelets operational matrix of integration P is derived, which is similar to those previously derived for other types of orthogonal functions such as Walsh, block-pulse, Laguerre, Legendre and Chebyshev, and can be used to solve problems such as identification, analysis and optimal control.
Abstract: The Haar wavelets operational matrix of integration P is derived, which is similar to those previously derived for other types of orthogonal functions such as Walsh, block-pulse, Laguerre, Legendre and Chebyshev. A general procedure of forming this matrix P is summarized. This matrix P can be used to solve problems such as identification, analysis and optimal control, like that of the other orthogonal functions. However, in the process of solving practical problems, the different resolution bases can be selected on the local time domain but on the global time domain, in order to understand more clearly system behaviour on the local time domain. This advantage results from the property of Haar wavelet localization, and the other orthogonal functions do not have this property. A numerical example will be used to demonstrate this point.
158 citations
TL;DR: According to the theory of the quasi-renewal process developed in this paper, the expected maintenance cost rate and availability are obtained and optimum maintenance policies are discussed for these three models.
Abstract: This paper proposes a quasi renewal process, and its application in maintenance theory is discussed. The properties of this quasi renewal process are studied and its renewal function is derived. Three imperfect maintenance models are proposed and they model imperfect maintenance in a way that, after maintenance, the lifetime of a unit will decrease to a fraction of its immediate previous one. According to the theory of the quasi-renewal process developed in this paper, the expected maintenance cost rate and availability are obtained and optimum maintenance policies are discussed for these three models. Finally, a class of related optimization problems is discussed and a numerical example is presented
131 citations
TL;DR: A kind of global controller design method can be developed, and thus the disadvantage of using fixed P in the Lyapunov function can be overcome and a constructive algorithm is developed to obtain the stabilizing feedback control law.
Abstract: This paper presents a design method for fuzzy control systems. The method is based on a fuzzy state-space model A suitable piecewise smooth quadratic (PSQ) Lyapunov function is used to establish asymptotic stability of the closed-loop system. With the PSQ Lyapunov function a kind of global controller design method can be developed, and thus the disadvantage of using fixed P in the Lyapunov function can be overcome. Furthermore, a constructive algorithm is developed to obtain the stabilizing feedback control law. The controller design algorithm involves solving a set of certain algebraic Riccati equations. An example is given to illustrate the application of the method
131 citations
TL;DR: The paper develops a cost model with an imperfect debugging and random life cycle as well as a penalty cost that is used to determine the optimal release policies for a software system.
Abstract: The paper develops a cost model with an imperfect debugging and random life cycle as well as a penalty cost that is used to determine the optimal release policies for a software system. The software reliability model, based on the nonhomogeneous Poisson process, allows for three different error types: critical, major and minor errors. The model also allows for the introduction of any of these errors during the removal of an error. Using the software reliability model presented, the cost model with multiple error types and imperfect debugging is developed. This cost also considers the penalty cost due to delay for a scheduled delivery time and the length of the software life cycle is random with a known distribution. The optimal software release policies that minimize the expected software system costs (subject to the various constraints) or maximize the software reliability subject to a cost constraint, are then determined. Numerical examples are provided to illustrate the results.
125 citations
TL;DR: The present paper deals with an inventory model for deteriorating items with instantaneous supply, linearly increasing demand and shortages in inventory, and a theory is developed to obtain the optimal solution.
Abstract: The present paper deals with an inventory model for deteriorating items with instantaneous supply, linearly increasing demand and shortages in inventory. A two-parameter Weibull distribution is taken to represent the time to deterioration. A theory is developed to obtain the optimal solution of the problem; it is then illustrated with the help of a numerical example. The sensitivity of the optimal solution towards changes in the values of different system parameters is also studied
94 citations
TL;DR: It is the self-feedback links of the context units of the modified Elman network which provide a dynamic trace of the gradients in the parameter space and enable the network to model dynamic systems of orders higher than one.
Abstract: A dynamic backpropagation (DBP) algorithm is presented to train the Elman network to model dynamic systems. The relationship between the Elman network trained by the DBP algorithm and the modified Elman network previously proposed by the authors is clarified. The paper shows that the modified Elman network is an approximate realization of the Elman network trained by the DBP algorithm. It is the self-feedback links of the context units of the modified Elman network which provide a dynamic trace of the gradients in the parameter space and enable the network to model dynamic systems of orders higher than one. The paper first gives the results of modelling a second-order linear plant and a third-order linear plant. Neither plant could be modelled using Elman networks trained by the standard backpropagation algorithm, but both were successfully modelled by DBP-trained Elman networks as they had been in previous studies by modified Elman networks. Finally, the paper reports on the application of the DBP-traine...
92 citations
TL;DR: A new approach to the design of optimal residuals in order to diagnose incipient faults based on multi-objective optimization and genetic algorithms is developed, and simulation results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.
Abstract: This paper develops a new approach to the design of optimal residuals in order to diagnose incipient faults based on multi-objective optimization and genetic algorithms. In this approach the residual is generated via an observer. To reduce false and missed alarm rates in fault diagnosis, a number of performance indices are introduced into the observer design. Some performance indices are expressed in the frequency domain to take account of the frequency distributions of faults, noise and modelling uncertainties. All objectives then are reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search for an optimal solution to satisfy these inequality constraints on performance indices. The approach developed is applied to a flight control system example, and simulation results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.
90 citations
TL;DR: It is shown that the switching between the Lorenz systems with slightly different parameters generates the sliding region such that when the trajectory enters the region, it will stay in it and the system trajectory converges to the equilibrium asymptotically, and the stabilization of chaos is realized.
Abstract: A variable structure control strategy is proposed to stabilize the well-known Lorenz chaos. It is shown that the switching between the Lorenz systems with slightly different parameters generates the sliding region such that when the trajectory enters the region, it will stay in it. Hence the system trajectory converges to the equilibrium asymptotically, and the stabilization of chaos is realized.
TL;DR: A suitable piecewise differentiate quadratic (PDQ) Lyapunov function is used to establish asymptotic stability of the closed-loop system and a constructive algorithm is developed to obtain the stabilizing feedback control law.
Abstract: This paper presents a solution of the H∞ control problem for a class of continuous-time nonlinear systems. The method is based on a fuzzy dynamical model of the nonlinear system. A suitable piecewise differentiate quadratic (PDQ) Lyapunov function is used to establish asymptotic stability of the closed-loop system. Furthermore, a constructive algorithm is developed to obtain the stabilizing feedback control law. The controller design algorithm involves solving a set of suitable algebraic Riccati equations. An example is given to illustrate the application of the method
TL;DR: Both global exponential stability and periodic solutions of delay Hopfield neural networks are analysed via the method of constructing suitable Lyapunov functionals using the parameters of the connection matrix.
Abstract: Both global exponential stability and periodic solutions of delay Hopfield neural networks are analysed via the method of constructing suitable Lyapunov functionals. Simple sufficient conditions are given for global exponential stability and the existence of periodic solutions. The conditions are presented in terms of the parameters of the connection matrix, and they are easy to check and apply in practice
TL;DR: A robust adaptive neural network feedback linearization control law is presented for a class of nonlinear dynamic systems and it is shown that uniformly stable adaptation is assured and asymptotic tracking is achieved if Bounded Basis Functions are used.
Abstract: In this paper, a robust adaptive neural network feedback linearization control law is presented for a class of nonlinear dynamic systems. First, the ‘GL’ matrices and the corresponding operator are introduced, which brings a new methodology into the analysis of neural networks. Secondly, the basic ideas of Feedback Linearization Control (FLC) of nonlinear systems are discussed. Finally, a robust adaptive neural network FLC of nonlinear systems is presented. It is shown that uniformly stable adaptation is assured and asymptotic tracking is achieved if Bounded Basis Functions (BBF) are used, and output tracking errors also converge to zero
TL;DR: Simulation results for an inverted pendulum system show that the proposed control architecture provides fast convergence and global asymptotic stability of the algorithm is established in the Lyapunov sense and is shown to be robust with respect to the modelling error.
Abstract: The paper presents a model reference adaptive control architecture for a class of nonlinear dynamic systems, which are either ill-defined or rather complex. The architecture employs fuzzy systems to model the unknown plant nonlinearity. Then an adaptive law is constructed based on these fuzzy systems. Global asymptotic stability of the algorithm is established in the Lyapunov sense and is shown to be robust with respect to the modelling error, which resulted from the fuzzy systems' approximate representation of the nonlinear plant. Simulation results for an inverted pendulum system show that the proposed control architecture provides fast convergence.
TL;DR: It is shown that if gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotic tracking is achieved, and a new adaptive neural network controller for robots is presented.
Abstract: Neural network modelling of robots is introduced using the GL matrices and operator (Ge et al. 1994), and a new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques, and there is no need for matrix inversion. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required and no time-consuming training process is necessary, except for initializing the neural networks based on approximate parameters of the initial posture at time t = 0. It is shown that if gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotic tracking is achieved.
TL;DR: An integrated pattern recognition method for the computer-aided interpretation of the ECG for diagnostic purposes and the output results are available in a concise tabular form for easy inspection by the cardiologists.
Abstract: The paper presents an integrated pattern recognition method for the computer-aided interpretation of the ECG for diagnostic purposes. The overall process of computer-aided interpretation has been subdivided into the stages of preprocessing, feature extraction, parameter measurement, frontal plane axis (FPA) angle calculation and diagnostics. A novel method has been developed for the delineation of peaks with boundary detection up to an accuracy of 99·98%. Accuracies have been obtained up to 99·83% in QRS identification and 96% in P and T wave identification, using fuzzy criteria, respectively. A fast and efficient algorithm has been implemented for FPA calculation. A point scoring scheme for left and right ventricular hypertrophy has been used for the diagnostics. Through this integrated approach the output results are available in a concise tabular form for easy inspection by the cardiologists. The method is suitable for both off-line and on-line applications in cardiac care units. The method has been su...
TL;DR: Two adaptive fuzzy robot control algorithms, which employ tracking errors of the joint motion to drive the parameter adaptation, are derived and are shown to be robust and stable.
Abstract: In this paper, two adaptive fuzzy robot control algorithms, which employ tracking errors of the joint motion to drive the parameter adaptation, are derived. The predominant concern of the adaptation laws is to reduce the tracking errors. In particular, they require no feedback of joint accelerations. These adaptive controllers do not require the exact robot dynamics but only the boundary of the dynamics. Theoretical results and simulation studies on a two-link robot manipulator show that these adaptive fuzzy robot controllers are robust and stable.
TL;DR: Periodic solutions and global stability of Hopfield neural networks with variable delays are investigated andicent conditions are given for the existence of periodic solutions and the global stability.
Abstract: Periodic solutions and global stability of Hopfield neural networks with variable delays are investigated. Sufficent conditions are given for the existence of periodic solutions and for the global stability
TL;DR: Three new maintenance models, two imperfect preventive maintenance models and a cost limit (imperfect) repair model are proposed, in these models the imperfect maintenance is treated in such a way that after maintenance the lifetime of a unit will be decreased to a fraction of the one immediately preceding it.
Abstract: Three new maintenance models, two imperfect preventive maintenance models and a cost limit (imperfect) repair model are proposed. In these models the imperfect maintenance is treated in such a way that after maintenance the lifetime of a unit will be decreased to a fraction of the one immediately preceding it, and the imperfect repair time as well as cost increases as the number of imperfect repairs increases. The long-run expected maintenance cost rates and corresponding asymptotic average availabilities for these three models are derived. The optimum maintenance policies subject to maintenance time are discussed. Some constraint optimization problems related to maintenance cost rate and availability are presented. Finally, numerical examples are given.
TL;DR: This paper deals with a positioning system for an autonomous vehicle ARSKA that is based on fusion of internal dead reckoning navigation and periodic absolute position measurements and is performed by using a Kalman-type filtering technique.
Abstract: This paper deals with a positioning system for an autonomous vehicle ARSKA. Localization of the vehicle is based on fusion of internal dead reckoning navigation and periodic absolute position measurements. Fusion is performed by using a Kalman-type filtering technique. A similar kind of approach is used in correcting the heading measurement. This is important because the position error is mostly the result of the accumulated heading error. The resulting accuracy depends on the accuracy of the dead reckoning estimation and on the accuracy and frequency of the absolute position measurement. As an absolute position measurement system, several alternatives can be used. Two different systems have been used with the vehicle: an external optical measurement device, tachymeter, and different combinations of DGPS. A real guarding application will be discussed in the paper.
TL;DR: The process to determine the robot position and orientation by using information originated from the external sensors is defined as the mobile robot relocalization, and the greater the frequency of this process, the better the knowledge of its position the robot will have, and therefore its movements will be better directed to the point it must reach.
Abstract: A mobile robot needs to know its position and orientation with accuracy in order to decide the control actions that permit it to finish the entrusted tasks successfully. To obtain this information, dead-reckoning-based systems have been used, and more recently inertial navigation systems. However, these systems have some errors that grow bigger as time goes by, therefore a moment comes when the information provided is useless. Because of this, there should be a periodic process that updates the robot position and orientation of the vehicle. The process to determine the robot position and orientation by using information originated from the external sensors is defined as the mobile robot relocalization. It is obvious that the greater the frequency of this process, the better the knowledge of its position the robot will have, and therefore its movements will be better directed to the point it must reach. The algorithm to achieve this can be classified in two large groups: relocalization through an a priori ...
TL;DR: This study presents a model for predicting the reliability of k-out-of-n: G systems assuming that components are subjected to several stages of degradation as well as catastrophic failures.
Abstract: In some environments the components may not fail fully but can degrade, and there may be multiple stages of degradation. In such cases, the efficiency of the system may decrease. In this study we present a model for predicting the reliability of k-out-of-n: G systems assuming that components are subjected to several stages of degradation as well as catastrophic failures. In the analysis we consider the state-dependent transition rates for the catastrophic failures and degradation processes. We also present the expressions to determine the reliability and mean time to failure (MTTF) of the k-out-of-n systems. Reliability and M TTF expressions for a special case of the model without catastrophic failures are also presented. Several numerical examples are given to illustrate the results.
TL;DR: A valuable technique for motion planning, based on the use of a distance field, is extended to consider non-holonomic constraints, both unicycle-like robots and steering wheel robots are considered.
Abstract: In the paper a valuable technique for motion planning, based on the use of a distance field, is extended to consider non-holonomic constraints. Both unicycle-like robots and steering wheel robots are considered. Results show the effectiveness of the proposed approach.
TL;DR: The proposed method is useful to realize the robustness of fuzzy control because the fuzzy rules are equivalently improved by the robust input for the sliding mode control on each learning step of the tuning process.
Abstract: This paper proposes a design of fuzzy control of a manipulator using the concept of a sliding mode. The fuzzy rules are tuned by learning the input for the sliding mode control. Usually it is difficult to construct the ideal sliding mode control because the input for the manipulator cannot be switched during the sampling interval. Since the magnitude of the chattering on the sliding surface is approximately proportional to the constrained force in switching surface, the tuning method of the fuzzy rules is easily obtained. The proposed method is useful to realize the robustness of fuzzy control because the fuzzy rules are equivalently improved by the robust input for the sliding mode control on each learning step of the tuning process. A numerical example is given to illustrate the usefulness of the proposed method
TL;DR: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained and feasibility of such systems and different ways of integration are described.
Abstract: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained. The feasibility of such systems and different ways of integration, so far made, in the context of image processing and pattern recognition are described. Scope for further research and development is outlined. An extensive bibliography is also provided.
TL;DR: An efficient hybrid genetic algorithm named the adaptive simulated annealing genetic algorithm (ASAGA) which is used in control applications and illustrated by simulation examples for system identification and control that include neural networks which are particularly suitable for applications of ASAGA.
Abstract: We propose an efficient hybrid genetic algorithm named the adaptive simulated annealing genetic algorithm (ASAGA) which is used in control applications. Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, combining them produces an adaptive algorithm that has the merits of both genetic algorithms and simulated annealing by introducing an adaptive cooling schedule and mutation operator such as simulated annealing. The validity and efficiency of the proposed algorithm are illustrated by simulation examples for system identification and control that include neural networks which are particularly suitable for applications of ASAGA
TL;DR: A new approach to multiple objective optimization design for robust multivariable control systems, based on eigenstructure assignment and genetic algorithms, which considers various performance indices in the objectives, which are individual eigenvalue sensitivity functions, and the sensitivity and the complementary sensitivity functions in the frequency domain.
Abstract: This paper develops a new approach to multiple objective optimization design for robust multivariable control systems, based on eigenstructure assignment and genetic algorithms. It considers various performance indices (or cost functions) in the objectives, which are individual eigenvalue sensitivity functions, and the sensitivity and the complementary sensitivity functions in the frequency domain, instead of a single performance index for a control system. Based on these performance indices, the robustness criteria are expressed by a set of inequalities. The paper will make full use of the freedom provided by eigenstructure assignment to find a controller to satisfy the robustness criteria. A numerical algorithm for multi-objective optimization using genetic algorithm approaches is developed and applied to the simulation of a distillation column control system design
TL;DR: This paper deals with automatic steering of autonomous wheeled mobile robots by using fuzzy logic, and a new method for designing and tuning fuzzy path trackers for autonomous vehicles is proposed.
Abstract: This paper deals with automatic steering of autonomous wheeled mobile robots by using fuzzy logic. The fuzzy controller has as inputs the vehicle's relative position and orientation with respect to the path to follow, and the controller's output is the steering angle. The dynamic behaviour of path tracking with fuzzy controllers is investigated. The problem is not easy, owing to the nonlinear nature of the feedback tracking system. A new method for designing and tuning fuzzy path trackers for autonomous vehicles is proposed. The results of the application of the designed fuzzy path tracking controllers to RAMA, a mobile robot designed and built for navigation in indoor and outdoor industrial environments, are shown. The experiments demonstrate a very stable and precise path tracking.
TL;DR: The generalized likelihood ratio technique is generalized for linear dynamic stochastic systems with unknown inputs to detect sensor or actuator failures in Kalman filter state estimator systems.
Abstract: The generalized likelihood ratio (GLR) technique performs statistical tests on the innovations sequence of a Kalman filter state estimator. Using the results of these tests, sensor or actuator failures are detected and identified. In this paper, we generalize this strategy for linear dynamic stochastic systems with unknown inputs