Showing papers in "International Journal of Systems Science in 2013"
TL;DR: A subspace-aided data-driven approach for batch process monitoring that serves as a non-parametric way of estimating the probability density function and an industrial benchmark of fed-batch penicillin production is used to verify the effectiveness of the proposed approach.
Abstract: Batch processes are characterised by a prescribed processing of raw materials into final products for a finite duration and play an important role in many industrial sectors due to the low-volume and high-value products. Process dynamics and stochastic disturbances are inherent characteristics of batch processes, which cause monitoring of batch processes a challenging problem in practice. To solve this problem, a subspace-aided data-driven approach is presented in this article for batch process monitoring. The advantages of the proposed approach lie in its simple form and its abilities to deal with stochastic disturbances and process dynamics existing in the process. The kernel density estimation, which serves as a non-parametric way of estimating the probability density function, is utilised for threshold calculation. An industrial benchmark of fed-batch penicillin production is finally utilised to verify the effectiveness of the proposed approach.
262 citations
TL;DR: This article focuses on the tracking control problem for switched nonlinear systems in strict-feedback form subject to an output constraint, and a barrier Lyapunov function is employed, which grows to infinity when its arguments approach domain boundaries.
Abstract: This article focuses on the tracking control problem for switched nonlinear systems in strict-feedback form subject to an output constraint. In order to prevent transgression of the constraint, a barrier Lyapunov function is employed, which grows to infinity when its arguments approach domain boundaries. Under the simultaneous domination assumption, a continuous controller for the switched system is constructed. Furthermore, asymptotic tracking is achieved without violation of the constraint, and all closed-loop signals keep bounded, when a mild requirement on the initial condition is satisfied. Finally, a simulation example is provided to illustrate the effectiveness of the proposed results.
153 citations
TL;DR: A brief survey of major application areas, structure properties, challenges and solution approaches to CCOPT is presented and the research results achieved in the past few years are presented.
Abstract: A chance-constrained optimisation CCOPT model has a dual goal: guaranteeing performance as well as system reliability under uncertainty. The beginning of CCOPT methods dates back in the 1950s. Recently, CCOPT approaches are gaining momentum as modern engineering and finance applications are forced to consider reliability and risk metrics at the design and planning stages. Although theoretical development and practical applications have been made, many open problems remain to be addressed in this area. This article attempts to provide a brief survey of major application areas, structure properties, challenges and solution approaches to CCOPT. In particular, we present our research results achieved in the past few years.
97 citations
TL;DR: It is proved that any given level of gain attenuation from external disturbance/parametric estimation error to system output is achieved with the developed control law.
Abstract: Adaptive-based integral sliding mode control scheme is developed to solve the actuator fault-tolerant compensation problem for linear time-invariant system in the presence of unknown actuator faults and external disturbances. A nonlinear integral-type sliding manifold is first presented that incorporates a virtual nominal control to achieve prescribed specifications of the perturbed system, and an adaptive sliding mode controller is constructed to automatically compensate for external disturbances and unknown time-invariant faults. It is shown that the proposed controller has the capability to guarantee that the resulting closed-loop system is asymptotically stable. Control design methodology is then extended to tackle with the unknown time-varying actuator faults. It is proved that any given level of gain attenuation from external disturbance/parametric estimation error to system output is achieved with the developed control law. The closed-loop performance of the new control solution derived here is evaluated extensively through numerical simulations in which the flexible spacecraft attitude control under both the external disturbances and actuator faults are considered.
69 citations
TL;DR: The fuzzy Lyapunov function approach is considered for stabilising continuous-time Takagi-Sugeno fuzzy systems and is extended to systems with large number of rules under membership function order relations and used to design parallel-distributed compensation fuzzy controllers which are also solved in terms of LMIs.
Abstract: In this article, the fuzzy Lyapunov function approach is considered for stabilising continuous-time Takagi-Sugeno fuzzy systems. Previous linear matrix inequality LMI stability conditions are relaxed by exploring further the properties of the time derivatives of premise membership functions and by introducing slack LMI variables into the problem formulation. The relaxation conditions given can also be used with a class of fuzzy Lyapunov functions which also depends on the membership function first-order time-derivative. The stability results are thus extended to systems with large number of rules under membership function order relations and used to design parallel-distributed compensation PDC fuzzy controllers which are also solved in terms of LMIs. Numerical examples illustrate the efficiency of the new stabilising conditions presented.
67 citations
TL;DR: This work proposes two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algorithm (DASA).
Abstract: Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems DOPs with continuous variables: the self-adaptive differential evolution algorithm jDE and the differential ant-stigmergy algorithm DASA. The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.
67 citations
TL;DR: An economic production quantity (EPQ) model in an imperfect production system where the production system may undergo in ‘out-of-control’ state from ‘in-control' state, after a certain time that follows a probability density function.
Abstract: This article deals with an economic production quantity EPQ model in an imperfect production system. The production system may undergo in ‘out-of-control’ state from ‘in-control’ state, after a certain time that follows a probability density function. The density function varies with reliability of the machinery system that may be controlled by new technologies, investing more costs. The defective items produced in ‘out-of-control’ state are reworked at a cost just after the regular production time. Occurrence of the ‘out-of-control’ state during or after regular production-run time is analysed and also graphically illustrated separately. Finally, an expected profit function regarding the inventory cost, unit production cost and selling price is maximised analytically. Sensitivity analysis of the model with respect to key parameters of the system is carried out. Two numerical examples are considered to test the model and one of them is illustrated graphically.
64 citations
TL;DR: By using the MDADT approach, a sufficient condition is obtained to guarantee the exponential stability with a weighted H ∞ performance for the underlying systems by using a mode-dependent average dwell time (MDADT) approach.
Abstract: This article is concerned with the disturbance attenuation properties of a class of switched linear systems by using a mode-dependent average dwell time MDADT approach The proposed switching law is less strict than the average dwell time ADT switching in that each mode in the underlying system has its own ADT By using the MDADT approach, a sufficient condition is obtained to guarantee the exponential stability with a weighted H ∞ performance for the underlying systems A numerical example is given to show the validity and potential of the developed results on improving the disturbance attenuation performance
60 citations
TL;DR: Based on the finite-time stability analysis results, static state and dynamic output feedback controllers are designed to finite- Time boundness and stability of switched continuous linear system.
Abstract: In this article, the finite-time stability analysis and stabilisation for switched continuous linear system are addressed. Several sufficient conditions for finite-time boundness and stability of switched system are proposed in this article. By comparing the proposed results, it is shown that more the information about the switching signal is known, less conservative results can be derived. Then, based on the finite-time stability analysis results, static state and dynamic output feedback controllers are designed to finite-time stabilise switched linear systems. Several numerical examples are given to illustrate the proposed results within this article.
60 citations
TL;DR: Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting, and the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article.
Abstract: This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine SVM method. Since SVMs have greater generalisation performance and guarantee global minima for given training data, it is believed that support vector regression will perform well for forecasting demand for perishable farm products. In order to improve forecasting precision FP, this article quantifies the factors affecting the sales forecast of perishable farm products based on the fuzzy theory, which is suitable for real situations. Numerical experiments show that forecasting systems with SVMs and fuzzy theory outperform the radial basis function neural network, based on the criteria of day absolute error, relative mean error and FP. Since there is no structured way to choose the free parameters of SVMs, the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article. Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting.
58 citations
TL;DR: A new indirect adaptive switching fuzzy control method for fuzzy dynamical systems, based on Takagi–Sugeno (T–S) multiple models is proposed, and results are presented, indicating the effectiveness and the advantages of the proposed method.
Abstract: A new indirect adaptive switching fuzzy control method for fuzzy dynamical systems, based on Takagi–Sugeno T–S multiple models is proposed in this article. Motivated by the fact that indirect adaptive control techniques suffer from poor transient response, especially when the initialisation of the estimation model is highly inaccurate and the region of uncertainty for the plant parameters is very large, we present a fuzzy control method that utilises the advantages of multiple models strategy. The dynamical system is expressed using the T–S method in order to cope with the nonlinearities. T–S adaptive multiple models of the system to be controlled are constructed using different initial estimations for the parameters while one feedback linearisation controller corresponds to each model according to a specified reference model. The controller to be applied is determined at every time instant by the model which best approximates the plant using a switching rule with a suitable performance index. Lyapunov stability theory is used in order to obtain the adaptive law for the multiple models parameters, ensuring the asymptotic stability of the system while a modification in this law keeps the control input away from singularities. Also, by introducing the next best controller logic, we avoid possible infeasibilities in the control signal. Simulation results are presented, indicating the effectiveness and the advantages of the proposed method.
TL;DR: This article addresses the problem of designing an iterative learning control for trajectory tracking of rigid robot manipulators subject to external disturbances, and performing repetitive tasks, without using the velocity measurement, with the use of a Lyapunov-like positive definite sequence.
Abstract: This article addresses the problem of designing an iterative learning control for trajectory tracking of rigid robot manipulators subject to external disturbances, and performing repetitive tasks, without using the velocity measurement. For solving this problem, a velocity observer having an iterative form is proposed to reconstruct the velocity signal in the control laws. Under assumptions that the disturbances are repetitive and the velocities are bounded, it has been shown that the whole control system robot plus controller plus observer is asymptotically stable and the observation error is globally asymptotically stable, over the whole finite time-interval when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed observer–controller schemes.
TL;DR: A single-manufacturer and single-retailer supply chain model under two-level permissible delay in payments when the manufacturer follows a lot-for-lot policy in response to the retailer's demand is developed.
Abstract: This article develops a single-manufacturer and single-retailer supply chain model under two-level permissible delay in payments when the manufacturer follows a lot-for-lot policy in response to the retailer's demand. The manufacturer offers a trade credit period to the retailer with the contract that the retailer must share a fraction of the profit earned during the trade credit period. On the other hand, the retailer provides his customer a partial trade credit which is less than that of the manufacturer. The demand at the retailer is assumed to be dependent on the selling price and the trade credit period offered to the customers. The average net profit of the supply chain is derived and an algorithm for finding the optimal solution is developed. Numerical examples are given to demonstrate the coordination policy of the supply chain and examine the sensitivity of key model-parameters.
TL;DR: The design of impulsive observers with variable update intervals for Lipschitz nonlinear systems with delays in state and Discontinuous Lyapunov function/funtional approaches are developed to analyse the stability of error dynamics.
Abstract: This article is concerned with the design of impulsive observers with variable update intervals for Lipschitz nonlinear systems with delays in state. Discontinuous Lyapunov function/funtional approaches are developed to analyse the stability of error dynamics. Delay-independent sufficient conditions for uniform exponential stability of the error dynamics over variable update intervals are derived in terms of linear matrix inequalities LMIs. When these LMIs are feasible, the observer gain matrix can be solved numerically with an LMI-based optimisation algorithm. Numerical examples are provided to show the efficiency of the proposed approach.
TL;DR: Based on the Lyapunov–Krasovskii functional approach and the linear matrix inequality technique, sufficient conditions for swarm systems to achieve practical consensus are proposed where the time-varying external disturbance can be in L 2 or L ∞.
Abstract: Practical consensus problems for general high-order linear time-invariant swarm systems with interaction uncertainties and time-varying external disturbances on directed graphs are investigated in this article. A dynamic consensus protocol with non-uniform time-varying delays is adopted to deal with the practical consensus problem. Using state space decomposition, practical consensus problems of a swarm system are converted into stability problems of a disagreement subsystem. Based on the Lyapunov–Krasovskii functional approach and the linear matrix inequality technique, sufficient conditions for swarm systems to achieve practical consensus are proposed where the time-varying external disturbance can be in L 2 or L ∞. Numerical simulations are presented to demonstrate theoretical results.
TL;DR: The finite-time consensus problems of second-order multi-agent system under fixed and switching network topologies, based on the graph theory, LaSalle's invariance principle and the homogeneity with dilation is studied.
Abstract: The finite-time consensus problems of second-order multi-agent system under fixed and switching network topologies are studied in this article. Based on the graph theory, LaSalle's invariance principle and the homogeneity with dilation, the finite-time consensus protocol of each agent using local information is designed. The leader-following finite-time consensus is analysed in detail. Moreover, some examples and simulation results are given to illustrate the effectiveness of the obtained theoretical results.
TL;DR: The integrated approach of AHP and MCGP is a better scientific and efficient method than traditional methods in finding a suitable location for buying or renting a house for business, especially under multiple qualitative and quantitative criteria within a shorter evaluation time.
Abstract: Location selection is a crucial decision in cost/benefit analysis of restaurants, coffee shops and others. However, it is difficult to be solved because there are many conflicting multiple goals in the problem of location selection. In order to solve the problem, this study integrates analytic hierarchy process AHP and multi-choice goal programming MCGP as a decision aid to obtain an appropriate house from many alternative locations that better suit the preferences of renters under their needs. This study obtains weights from AHP and implements it upon each goal using MCGP for the location selection problem. According to the function of multi-aspiration provided by MCGP, decision makers can set multi-aspiration for each location goal to rank the candidate locations. Compared to the unaided selection processes, the integrated approach of AHP and MCGP is a better scientific and efficient method than traditional methods in finding a suitable location for buying or renting a house for business, especially under multiple qualitative and quantitative criteria within a shorter evaluation time. In addition, a real case is provided to demonstrate the usefulness of the proposed method. The results show that the proposed method is able to provide better quality decision than normal manual methods.
TL;DR: A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed and two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms.
Abstract: A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed in this article. The model is formulated into the frameworks of the expected value model EVM and the chance-constrained programming CCP based on two different distance measures. In order to solve the model, two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms. However, in the first algorithm, named SSGA, a special type of genetic algorithm is combined and in the second, SSVDO, a vibration-damping optimisation VDO algorithm is united. The Taguchi method is employed to tune the parameters of the two proposed algorithms. Finally, some numerical examples are given to illustrate the applications of the proposed methodologies and to compare their performances.
TL;DR: On the basis of the finite-time stability theory and the differential inequality principle, it is proved that the resulting closed-loop system is stable and the trajectory tracking error converges to zero in finite time.
Abstract: An adaptive nonsingular fast terminal sliding mode control scheme consisting of an adaptive control term and a robust control term for electromechanical actuator is proposed in this article. The adaptive control term with an improved composite adaptive law can estimate the uncertain parameters and compensate for the modelled dynamical uncertainties. While the robust control term, which is based on a modified nonsingular fast terminal sliding mode control method with fast terminal sliding mode TSM reaching law, provides fast convergence of errors, and robustifies the design against unmodelled dynamics. Furthermore, the control method eliminates the singular problems in conventional TSM control. On the basis of the finite-time stability theory and the differential inequality principle, it is proved that the resulting closed-loop system is stable and the trajectory tracking error converges to zero in finite time. Finally the effectiveness of the proposed method is illustrated by simulation and experimental study.
TL;DR: It is shown that the consensus problem for MASs with switching topology can be regarded as a special case of the problem considered in this article, and the related theoretical results are presented.
Abstract: This article investigates the consensus problem of multi-agent systems MASs with imperfect communication both in channels and in actuators. The data transmission among agents may fail due to limited communication capacity, and the actuators may fail to receive information owing to noisy environment. We use a Markov chain approach to characterise the occurrence of the two types missing data in a unified framework. A sufficient consensus condition is first obtained in terms of linear matrix inequalities. Then, based on this condition, a novel controller design method is further developed such that the MAS with imperfect communication reaches mean-square consensus. It is shown that the consensus problem for MASs with switching topology can be regarded as a special case of the problem considered in this article, and the related theoretical results are presented as well. Numerical examples are provided to illustrate the effectiveness of the proposed approach.
TL;DR: An approach to robust stability of linear systems from the consideration of the saturating control is proposed, which would work only if the slope of the continuous nonlinear function within the boundary layer is low enough.
Abstract: It has been a widely accepted notion that approximation of discontinuous control by certain continuous function in a boundary layer results in chattering elimination in sliding mode control systems. It is shown through three different types of analysis that in the presence of parasitic dynamics, this approach to chattering elimination would work only if the slope of the continuous nonlinear function within the boundary layer is low enough, which may result in the deterioration of performance of the system. A few examples are provided. An approach to robust stability of linear systems from the consideration of the saturating control is proposed.
TL;DR: It is proved that the proposed adaptive tracking control is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system.
Abstract: In this article, the constrained control allocation is proposed for overactuated ocean surface vessels with parametric uncertainties and unknown external disturbances. The constrained control allocation is transformed into a convex quadratic programming problem and a recurrent neural network is employed to solve it. To complete the control allocation, the control command is derived via the backstepping method. Adaptive tracking control is proposed for the full-state feedback case using the backstepping technique and the Lyapunov synthesis. It is proved that the proposed adaptive tracking control is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system. Then, the obtained control command is distributed to each actuator of overactuated ocean vessels. Finally, simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control and the constrained control allocation scheme.
TL;DR: Under the given control protocols in Markov switching topologies, the second-order multi-agent systems can reach mean square consensus if and only if each union of the graphs corresponding to all the nodes in closed sets has a spanning tree.
Abstract: This article deals with the mean square consensus problem for second-order discrete-time multi-agent systems. Both cases of systems with and without time delays in Markov switching topologies are considered. With the introduced control protocols, necessary and sufficient conditions for mean square consensus of second-order multi-agent systems are derived. Under the given control protocols in Markov switching topologies, the second-order multi-agent systems can reach mean square consensus if and only if each union of the graphs corresponding to all the nodes in closed sets has a spanning tree. Finally, a simulation example is provided to illustrate the effectiveness of our theoretical results.
TL;DR: The design method proposed is extended to the uncertain switched nonlinear systems in nested lower triangular form to solve the global robust stabilisation problem under arbitrary switchings.
Abstract: This article deals with the global robust stabilisation for a class of switched nonlinear systems under arbitrary switchings. The system under consideration is in lower triangular form and contains uncertainty. Both common Lyapunov function and state feedback controller are simultaneously constructed by backstepping such that the closed-loop system is globally robustly asymptotically stable under arbitrary switchings. Lastly, the design method proposed is extended to the uncertain switched nonlinear systems in nested lower triangular form to solve the global robust stabilisation problem under arbitrary switchings. Two examples are given to show the effectiveness of the proposed methods.
TL;DR: A novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory is presented and the robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances.
Abstract: This article presents a novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory. We propose a novel model, which presents the highly non-linear dynamics of robot manipulator in the form of linear discrete-time time-varying system. Based on the proposed model, we develop a two-term control law. The first term is an ordinary time-optimal and minimum-norm TOMN control by employing parametric controllers to guarantee stability. The second term is a novel robust control to improve the control performance in the face of uncertainties. The robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances. Performance of the proposed method is compared with two discrete methods, namely the TOMN control and an adaptive iterative learning AIL control. Simulation results confirm superiority of the proposed method in terms of the convergence speed and precision.
TL;DR: A dynamic pre- and post-deterioration cumulative discount policy to enhance inventory depletion rate resulting low volume of deterioration cost, holding cost and hence higher profit is proposed.
Abstract: Product perishability is an important aspect of inventory control. To minimise the effect of deterioration, retailers in supermarkets, departmental store managers, etc. always want higher inventory depletion rate. In this article, we propose a dynamic pre-and post-deterioration cumulative discount policy to enhance inventory depletion rate resulting low volume of deterioration cost, holding cost and hence higher profit. It is assumed that demand is a price and time dependent ramp-type function and the product starts to deteriorate after certain amount of time. Unlike the conventional inventory models with pricing strategies, which are restricted to a fixed number of price changes and to a fixed cycle length, we allow the number of price changes before as well as after the start of deterioration and the replenishment cycle length to be the decision variables. Before start of deterioration, discounts on unit selling price are provided cumulatively in successive pricing cycles. After the start of deterioration, discounts on reduced unit selling price are also provided in a cumulative way. A mathematical model is developed and the existence of the optimal solution is verified. A numerical example is presented, which indicates that under the cumulative effect of price discounting, dynamic pricing policy outperforms static pricing strategy. Sensitivity analysis of the model is carried out.
TL;DR: The main purpose of this article is to investigate an optimal resource allocation plan to minimise the cost of software during the testing and operational phase under dynamic condition and suggests policy for the optimal release time of the software.
Abstract: Allocation of efforts to a software development project during the testing phase is a multifaceted task for software managers. The challenges become stiffer when the nature of the development process is considered in the dynamic environment. Many software reliability growth models have been proposed in last decade to minimise the total testing-effort expenditures, but mostly under static assumption. The main purpose of this article is to investigate an optimal resource allocation plan to minimise the cost of software during the testing and operational phase under dynamic condition. An elaborate optimisation policy based on the optimal control theory is proposed and numerical examples are illustrated. This article also studies the optimal resource allocation problems for various conditions by examining the behaviour of the model parameters and also suggests policy for the optimal release time of the software. The experimental results greatly help us to identify the contribution of each selected parameter and its weight.
TL;DR: It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighbourhood of zero by appropriate choice of the design parameters.
Abstract: This article develops an adaptive fuzzy control method for accommodating actuator faults in a class of unknown nonlinear systems with unmeasured states. The considered faults are modelled as both loss of effectiveness and lock-in-place stuck at unknown place. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is developed for estimating the unmeasured states. Combining the backstepping technique with the nonlinear tolerant-fault control theory, a novel adaptive fuzzy faults-tolerant control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighbourhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach.
TL;DR: This article presents the joint state filtering and parameter identification problem for uncertain stochastic nonlinear polynomial systems with unknown parameters in the state equation over nonlinearPolynomial observations, where the unknown parameters are considered Wiener processes.
Abstract: This article presents the joint state filtering and parameter identification problem for uncertain stochastic nonlinear polynomial systems with unknown parameters in the state equation over nonlinear polynomial observations, where the unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained mean-square filter for the extended state vector also serves as the mean-square identifier for the unknown parameters. Performance of the designed mean-square state filter and parameter identifier is verified for both, positive and negative, parameter values.
TL;DR: The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes and is investigated in order to demonstrate the effectiveness of the technique.
Abstract: A wireless sensor network WSN singular and plural of acronyms are spelled the same is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN so that it works properly, observing specific quantities and metrics to evaluate the WSN operational conditions. Among them, one of the most important is the reliability. Considering a WSN as a system composed of sensor nodes the system reliability approach can be applied, thus expressing the WSN reliability in terms of its nodes' reliability. More specifically, since often standby power management policies are applied at node level and interferences among nodes may arise, a WSN can be considered as a dynamic system. In this article we therefore consider the WSN reliability evaluation problem from the dynamic system reliability perspective. Static–structural interactions are specified by the WSN topology. Sleep/wake-up standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability, we use dynamic reliability block diagrams and Petri nets. The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes. In order to demonstrate the effectiveness of the technique, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.