This paper focuses on the problem of robust H control for a class of switched non-linear systems with neutral uncertainties. The multiple Lyapunov function approach and the common Lyapunov function approach are exploited respectively for the cases whether states are measurable or not. Uncertainties are allowed to appear in channels of state, control input and disturbance input. Conditions for the solvability of the robust H control problem and design of both hybrid state feedback and dynamic output feedback controllers are presented. As an application, a hybrid state feedback strategy is proposed to solve the standard robust H control problem for non-linear systems when no single continuous controller is effective.

Given a finite or countable family of continuous vector fields {f_i(x)}_iI, we show that there exists a feedback : Rⁿ -> I such that the switched system x^· = f _(x) (x) is globally asymptotically stable whenever there exists a smooth control Liapunov function V such that for all x != 0, V(x)f_i (x) < 0, for some i I.

In this paper the control design methods for spark ignition engine models are discussed. Both single-input single-output (SISO) and multi-input multi-output (MIMO) systems are investigated for the design of an engine control system. A comprehensive comparative study for two classes of non-linear control methods, i.e. globally linearizing control (GLC) and sliding mode control, is given. The simulations results are presented to illustrate the capability and performance of the two kinds of controllers. The results show that the controller applying GLC can give accurate tracking for the engine model without uncertainty and disturbance; however, it fails to keep good tracking performance when uncertainty is considered. In contrast, the controller using the sliding mode is able to achieve the target, which shows that the engine system with the proposed sliding mode controller has good robustness to uncertainty and disturbance.

Achievable high recording track density in hard disk drives (HDDs) is often limited by the servo performance and the presence of disturbances and instability in the HDDs’ structures. For better servo performance, the position error signal (PES) sampling rate should be increased, as it is closer to the corresponding continuous time controller. However, the fixed number of servo sectors within the embedded servo system and the spinning speed limits the upper bound for the PES sampling rate. In this paper, a new PES detection strategy through direct measurement of the head position from the frequency-encoded user data channel is proposed in order to increase measurement accuracy and position update rate, in particular for embedded servo systems. Having a higher sampling rate will be very crucial in order to achieve more than the 1600 kTPI required for the 10 terabits per square inch areal density targeted by the Information Storage Industry Consortium (INSIC). The ability to extract positioning information within the user data sector will allow accurate read back and assist in writing processes.

In this paper we investigate the tracking control of non-holonomic mobile robots with uncertainties. Non-holonomic kinematic systems with visual feedback are uncertain and more involved in comparison with common kinematic systems. Barbalat's theorem and Lyapunov techniques are exploited to craft a dynamic feedback robust controller that enables tracking of the mobile robot configuration despite the lack of depth information and the lack of precise visual parameters. The most interesting feature of this paper is that the problem is discussed in the image frame and the inertial frame, which makes the problem easy and useful. The convergence of the error system by using the proposed method is proved rigorously. A simulation is given to show the effectiveness of the presented controllers.

Because of the high possibility of uncertain production disturbances, an established optimized schedule may not keep its optimality or even feasibility. Production rescheduling, which gives attention to both optimal scheduling and dynamic scheduling, is proposed in this background. One of the most important issues in rescheduling research is the rescheduling strategy that refers to rescheduling start-up decision making and rescheduling methodology adoption. It is ill structured, and involves some fuzzy and random information. In order to solve it, a fuzzy Petri net model for rescheduling (FPN-R) is proposed. Based on it, a fuzzy reasoning approach for rescheduling decision-making is discussed. Finally, a case study from a wafer fabrication plant is used to show the application and feasibility of the proposed FPN-R model and reasoning approach.

We present a computationally attractive technique to study the reachability of rectangular regions by trajectories of continuous multi-affine systems. The method is iterative. At each step, finer partitions and finite quotients that over-approximate the reachability properties of the initial system are produced. We exploit some convexity properties of multi-affine functions on rectangles to show that the construction of the quotient at each step requires only the evaluation of the vector field at the set of all vertices of all rectangles in the partition and finding the roots of a finite set of scalar affine functions. This methodology can be used for formal analysis of biochemical networks, aircraft and underwater vehicles, where multi-affine models are widely used.

The speed-regulation problem for a permanent-magnet synchronous motor (PMSM) servo system is studied in this paper. In order to improve the disturbance rejection property of the PMSM, a novel composite controller for the speed-loop is presented. First, an extended state observer (ESO) is introduced to estimate the disturbances of the system. The estimated value is used in the feed-forward compensation design. Second, a continuous feedback-based finite-time control technique is employed for the feedback design. The composite speed controller can be considered as a composition of finite-time proportional feedback plus feed-forward compensation based on ESO (FTP + ESO). Two standard proportional–integral (PI) controllers are employed for two current loops. The closed-loop system of the speed error can be regarded as a first-order finite-time control system with bounded disturbances. Rigorous analysis shows that the proposed scheme can enhance the disturbance rejection property of the closed-loop system. Simulation and experimental comparisons with two other control methods, ie the composite control method with proportional feedback plus feed-forward compensation based on ESO (P + ESO), and the PI control method, are given to verify the effectiveness of the proposed method.

In this paper we present the idea of hybrid servo track writing and give a complete description of the developed hybrid servo track writer platform including its main functions and servo mechanism. The major disturbances and noise level in media-level servo track writing are identified based on the position error signal measured on the hybrid servo track writer platform. A dual-stage servo system, which is applicable in servo writing, is proposed and implemented on the experimental platform. With the disturbances and noise models, an advanced loop-shaping scheme based on Kalman–Yakubovic–Popov lemma is used to optimize the controller for the micro-actuator servo loop. The experimental results show that the tracking accuracy can reach 6 nm, which is able to support a track density of more than 400 000 tracks per inch.

An adaptive on-line scheme is designed to identify the uncertain and time-varying resonance modes in hard disk drive servo systems. Based on the identified results, the uncertain resonance mode is extracted and a peak filter is designed accordingly to provide stronger capability of disturbance suppression. The scheme does not require any extra excitation signal or additional signal processing for the parameter identification. Compared with the conventional method, the rates of learning and error convergence are increased dramatically. The scheme can provide good stability margin at high frequency to the variation of the resonance frequency and the peak gain. Simulation results show the validity and effectiveness of the proposed scheme.

In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.

In this article, we address issues related to modelling of a discrete event system in a way that would effectively provide an easy implementation of the scheduling policy on the actual controller and at the same time would provide an answer on how profound the utilization of the system resources is. Herein we propose a method that relies on usage of a matrix model and a max-plus algebra model. The idea is to use the advantages of each of the given models. A system, initially given in matrix form, can be transferred into a max-plus model under the given sequence. In the case of structural changes, the system matrix model can be easily modified and hence supervisory matrix controller redesign is straightforward. On the other hand, the max-plus model is suitable for performance analysis, which in some cases could be difficult if a matrix model is used. Moreover, max-plus algebra represents the system on the higher level of abstraction, which is suitable for investigation of particular properties of discrete event systems. In addition, using both models, we propose a method for determining a scheduling sequence that is not well formed.

Behaviour permissiveness is an important criterion in evaluating the performance of a liveness-enforcing supervisor for discrete event systems. The existence of uncontrollable events is a standard feature in the supervisory control of discrete event systems. A natural problem arising in this area is the existence of an optimal supervisor when there exist uncontrollable events in a plant. For a class of Petri net models of flexible manufacturing systems, whose optimal liveness-enforcing Petri net supervisors can be synthesized by a particular method through the addition of a set of monitors, this paper aims to identify a set of transitions such that the existence of an optimal liveness-enforcing net supervisor depends on their controllability. The controllability of a transition is decided by solving a linear programming problem to verify whether a monitor with arcs to the transition can act to inhibit it when it is otherwise enabled. A number of examples are presented to demonstrate the application of the proposed methods.

The transportation of crude oil from production fields to refineries is a very important operation in the oil industry. In this paper, an inventory routing problem for crude oil transportation is studied, where the crude oil is transported from a central depot to a set of customers with dynamic demand using multiple transportation modes. Oil can be transported through marine routes, pipelines or a combination of the two modes. The marine transportation of crude oil is performed by a heterogeneous fleet of tankers with limited capacity owned by an oil distributor itself and/or the tankers of different types rented from a third party. Each transportation operation has a lead time and the storage capacity of oil at each customer is limited. The problem is to determine over a given planning horizon an optimal oil transportation plan that minimizes the total transportation and inventory costs subject to various constraints. The plan defines the number of tankers of each type to rent and the number of tankers of each type to dispatch on each route in each period. A mixed-integer programming model is established for the problem. Because of the high complexity and large size of the problem, the model is too complicated to be solved exactly. A metaheuristic method, the Greedy Randomized Adaptive Search Procedure (GRASP) enhanced by an intensification strategy based on Path Relinking is developed to find its near-optimal solutions. Numerical test results of the method demonstrate the effectiveness of the method.

This paper develops a recursive algorithm for estimating the least-cost planning sequence in a manufacturing system that is modelled by a labelled Petri net. We consider a setting where we are given a sequence of labels that represents a sequence of tasks that need to be executed during a manufacturing process, and we assume that each label (task) can potentially be accomplished by a number of different transitions, which represent alternative ways of accomplishing a specific task. The processes via which individual tasks can be accomplished and the interactions among these processes in the given manufacturing system are captured by the structure of the labelled Petri net. Moreover, each transition in this net is associated with a non-negative cost that captures its execution cost (eg, in terms of the amount of workload or power required to execute the transition). Given the sequence of labels (ie, the sequence of tasks that has to be accomplished), we need to identify the transition firing sequence(s) (ie, the sequence(s) of activities) that has (have) the least total cost and accomplishes (accomplish) the desired sequence of tasks while, of course, obeying the constraints imposed by the manufacturing system (ie, the dynamics and structure of the Petri net). We develop a recursive algorithm that finds the least-cost transition firing sequence(s) with complexity that is polynomial in the length of the given sequence of labels (tasks). An example of two parallel working machines is also provided to illustrate how the algorithm can be used to estimate least-cost planning sequences.

An examination of the literature results on timed and untimed discrete event system (DES) models reveals clearly that the supervisory control problem is more tractable on untimed models. Thus it is interesting to consider the extent to which untimed DES models can be used to design controllers for dynamical systems. In order to approach a larger class of systems appropriate abstraction methods are necessary as well as some extensions of the untimed supervisory control methods. This paper proposes an abstraction procedure that can be used to extract untimed DES models from hybrid automata models with control inputs and continuous disturbances. The abstractions obtained using this procedure are state machines in which every state corresponds to a region of the state space of the hybrid automaton and transitions between states correspond to transitions between the regions. Results describing properties of the abstraction procedure are obtained, including a semidecidability result. The procedure is useful not only for sequential problems, but also in the context of concurrency.

We establish asymptotic and exponential stability theorems for delay impulsive systems by employing Lyapunov functionals with discontinuities. Our conditions have the property that when specialized to linear delay impulsive systems, the stability tests can be formulated as Linear Matrix Inequalities (LMIs). Then we consider Networked Control Systems (NCSs) consisting of an LTI process and a static feedback controller connected through a communication network. Due to the shared and unreliable channels, sampling intervals become uncertain and variable. Moreover, samples may be dropped or experience uncertain and variable delays before arriving at the destination. We show that the resulting NCSs can be modelled by linear delay impulsive systems and we provide conditions for stability of the closed-loop system in terms of LMIs. By solving these LMIs, one can find a positive constant that determines an upper bound between each sampling time and the subsequent input update time, for which stability of the closed-loop system is guaranteed.

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. These systems often exhibit hybrid behaviours, therefore, model-based diagnosis methods have to be based on hybrid system models. Most previous work in hybrid systems diagnosis has focused either on parametric or discrete faults. In this paper, we develop an integrated approach for hybrid diagnosis of parametric and discrete faults by incorporating the effects of both types of faults into our event-based qualitative fault signature framework. The framework allows for systematic design of event-based diagnosers that facilitate diagnosability analysis. Experimental results from a case study performed on an electrical power distribution system demonstrate the effectiveness of the approach.

Considering dynamical changes in manufacturing systems, such as rework and system reconfiguration, this paper presents a new Petri net (PN) model called an intelligent token PN (ITPN). In this model, tokens representing job instances carry real-time knowledge about system status and changes just like smart cards in practice. By taking this advantage, dynamical changes of a system can be easily modelled. An ITPN can model a system in a modular way, leading to a very compact model. When changes occur, it needs to modify only the changed part from the current model. Thus, an ITPN is highly reusable. Based on the ITPN model, a deadlock-free control (DFC) policy is presented. With this control policy, an ITPN is deadlock-free and reversible. In particular, when an ITPN is changed according to the changes of the system, the policy is still applicable to the modified model. Thus, one need not modify the control policy when an ITPN model is modified.

Fault monitoring is an essential requirement for safety and reliability of industrial systems. The paper presents a novel event-based online monitoring technique for automated manufacturing systems, ensuring timely and accurate detection of system failures. The monitor model is based on first-order hybrid Petri nets, ie, Petri nets that make use of first-order fluid approximation. The proposed fault analysis technique relies on a modular framework, so that elementary monitors can be connected with other monitors to check more complex systems while avoiding the state–space explosion problem. In addition, the presented monitor detects system faults as soon as possible, before the maximum execution time assigned to each task. Several examples and an application to an automated manufacturing system proposed in the related literature enlighten the simplicity and modularity of the technique.

One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. For interactive services, sending and receiving an arbitrary number of messages may occur during a service process. It is important that the service composition is not only based on input/output interface, but also related to process behaviour. This paper deals with the problem of interactive Web service composition, ie, for a complex interactive composition schema, and how to discover and select concrete services to satisfy the requirements of users and service interaction. Firstly, a Web service is modelled as an open Petri net, with messages and inner process description, and the service interaction is achieved via asynchronous composition. Then, according to abstract composite service, concrete services are discovered by some rules. These rules include interface consistency, structural-characteristic preservation, which assures the correct interaction between services. With the help of Petri net language, the similarity degree of abstract and concrete services based on their interface and inner process is evaluated, which provides guides for service selection.

Fairness is among the most well known properties of Petri net systems, but it has not been investigated in depth for the purpose of application. This paper aims at such an investigation, and as a result, alternative definitions based on synchronic distances are proposed with examples for illustration.

The issue of supervisory control of discrete event systems (DES) based on Petri nets (PN) has been investigated by using various methods. However, there exists a problem that the supervisor, introduced to enforce the given specification, causes a deadlock in the closed-loop system, especially when the deadlock occurs in the closed-loop system in which the original plant model is live. To address this problem, we propose a hybrid approach to design a deadlock-free PN controller for DES with given specification. The approach mainly consists of two stages. Firstly, a supervisor is designed to enforce the given specification using the reported place invariants method. Then, we regard the obtained closed-loop system with deadlock introduced by the supervisor as a new plant net and adopt the theory of regions to design a deadlock-free PN controller. The controller designed by our approach is guaranteed to be maximally permissive when the original plant PN is fully controllable and observable. Even for partially controllable and observable plant PN, it is possible to obtain a suboptimal solution.

This paper considers an iterative deadlock prevention policy in the context of the automated manufacturing systems (AMS) that constitute one of the major production technologies in modern industry. Owing to the specialty of an AMS, the reachability graph (RG) of its given Petri net model shows powerful analysis and control capability to crack such a hard nut. However, the fact that the number of nodes involved in an RG grows exponentially with the size of a Petri net model makes all the RG-based policies infeasible. The approach conducted in this paper illustrates that the bad nodes in an RG can be extracted iteratively by a set of mixed integer programming formulations so that the explicit enumeration of all nodes of the RG is avoided. The proposed approach promises a computationally efficient policy that guarantees a nearly optimal liveness-enforcing supervisor.

This paper presents a novel optimization algorithm: a group search optimizer (GSO) for training an artificial neural network (ANN) used for diagnosis of breast cancer. The GSO is inspired by animal social searching behaviour. Its global search performance has been proved competitive to other evolutionary algorithms and the particle swarm optimizer. The parameters of a three-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Wisconsin diagnostic breast cancer data from the UCI Machine Learning repository are employed as a benchmark classification problem to evaluate the proposed method. In comparison with other sophisticated machine learning techniques used for ANN training, including some ANN ensembles, the GSO for ANN, GSOANN, has a better convergence rate and generalization performances for the breast cancer diagnosis problem.

In this paper, we develop fixed-final time nearly optimal control laws for a class of non-holonomic chained form systems by using neural networks to approximately solve a Hamilton–Jacobi–Bellman equation. A certain time-folding method is applied to recover uniform complete controllability for the chained form system. This method requires an innovative design of a certain dynamic control component. Using this time-folding method, the chained form system is mapped into a controllable linear system for which controllers can systematically be designed to ensure exponential or asymptotic stability as well as nearly optimal performance. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated in an example.

A new electrical tomograph that is designed specifically for low-cost and accessibility is described. It offers a unique environment for three-dimensional (3D) electrical impedance imaging. Using a cross-bar switch, the hardware can support ad hoc measurement strategies and is linked to control software, which integrates the complete process from 3D finite element modelling through to image reconstruction and generation of movie files. Digital signal processing algorithms are used to derive measurements of complex impedance from the measurement of amplitude and phase difference between the driven and received signals. This is achieved by rapid sampling of sinusoidal signals having frequencies between 1.0 Hz and 1.0 MHz. The instrument can deliver about 100 measurements per second, corresponding to a typical 16-electrode tomography frame, and is targeted at processes with modest dynamics or for laboratory development of tomographic applications and algorithms. The latest version (LCT2) is described here and uses a USB2 link to provide data transfer rates up to 20 MBytes per second. Sinusoidal excitation signals are derived using a direct digital synthesis chip. Voltage measurements are digitized to 16-bit accuracy and the standard system can accommodate 64 electrodes. The software embraces the EIDORS-3D soft-field reconstruction algorithms and the resulting impedance imaging capability of the device and characterization of the signals are described.

In this paper, diversity-preserving non-destructive operators for tree-based genetic programming (GP) are proposed to control code bloat, which is one of the main issues of GP. Firstly, the proposed method is tested using two GP benchmark problems – namely symbolic regression and 11-multiplexer problems. The proposed approach is compared with the traditional standard approach and a crossover hill-climbing approach, which combines non-destructive operators and traditional operators. The newly proposed approach appears superior to the other two compared approaches, in confining intron growth, which is supposed to be the main reason for code bloat, and achieves equal or better performance. When parsimony pressure is applied, the effect of the proposed GP on code bloat is even clearer. The offspring distribution is analysed to illustrate that introns are effectively confined by the new approach. Afterwards these ideas are applied to a real-world problem of breast cancer detection with the Wisconsin Diagnosis Breast Cancer dataset and their ability to solve a real world problem is demonstrated.

This paper presents a review of the development of biologically inspired algorithms (BIAs), including the evolutionary algorithms and swarm intelligence, as well as the newly emerged bacterial foraging algorithms. Our retrospective review is classified to these three areas of BIAs, and their common features and functionalities are discussed respectively. In order to identify the roots and clarify the variants of various BIAs, our discussion is also extended to identify the difference between each algorithm, particularly by indicating their biological background. The review focuses on not only the background of the original studies of BIAs but also their principles and applications, which are supported with a number of references included in this paper.