Remote control of robots is one of the most relevant and practical fields in robotics. Most of the control structures of remote operation systems seek to achieve transparency and stability at the same time, which the simultaneous achievement of the both, considering the uncertainty and disturbances in the system and random delay in the communication channel is very challenging. So far, many researchers have used position, speed, force or impedance information to provide various control methods, but none of these methods have achieved complete transparency and robust stability in the presence of random delay and uncertainties and disturbances and compromises between them should be made. In this paper, using a new method, a control structure including sliding mode control, adaptive control and impedance control is presented. This method has been simulated by Simulink of MATLAB software and it has been shown that this method is able to establish ideal transparency and ensure robust stability in the system with disturbances and uncertainties in the presence of random delay in the network.

Measurement data of guidance sensors are commonly lost and delayed in ground to air missile systems. These phenomena affect the missile efficiency. Kalman filter is used to estimate the variables needed in implementation of guidance law. But the performance of Kalman filters is dependent on the knowing exact model of the system. In practical problems, the exact parameters of the systems model, especially the one of delay and loss is not known. In this study, adaptive Kalman filter is employed to compensate the uncertainty in the stochastic model of delay and loss which is employed in a line of sight guidance algorithm of a defensive missile. A set of recursive difference equations are used to obtain the adaptive filter gains. The problem is formulated in presence of delayed and missing measurements, then the adaptive filter structure and correction factor are presented. Simulation results are presented to verify the improved performance of the approach.

Networked control systems (NCSs) are distributed control systems in which the nodes, including controllers, sensors, actuators, and plants are connected by a digital communication network such as the Internet. One of the most critical challenges in networked control systems is the stochastic time delay of arriving data packets in the communication network among the nodes. Using the Smith predictor as the controller is a common solution to overcome network time delay. Online and accurate modeling of the plant improves the performance of the networked control system, especially when the plant is nonlinear and has unknown parameters and time-variant behavior. In this paper, a novel controller, Neural-Smith predictor, is proposed, which firstly models plant using a perceptron neural network and secondly, another neural network is used as the core of signal processing of the controller. The parameters variation of the plant during time is considered online by the controller, and then the desired control signal is generated. The Integral of Time multiplied by the Absolut value of Error (ITAE) is a proper performance index for position control, so this index has been used to compare the results. Results of simulations show that NCS using the Neural-Smith predictor has better performance in comparison to the common Smith predictor and the novel compensation method using a modified communication disturbance observer (MCDOB) when the values of network time delay and variation of plant’s transfer function are excessive. For example, while the range of stochastic time delay is between 19 and 21 ms, the difference between the ITAE of controllers is 0.0004. This value increases to 0.027, while the range of stochastic time delay is between 910 and 930 ms.

This paper deals with the problem of finite-time stochastic stability and stabilization of networked control systems in the presence of random delay. At first, the network random delay is modeled as a Markov chain, and the resulting closed-loop system is a Markovian jump linear system. Since, due to network complexities, the accurate access to transition probabilities are hard or even impossible, some of the elements in the transition probability matrix are considered as unknown. Then, according to the definition of finite-time stochastic stability for discrete-time Markovian jump systems, a sufficient condition is proposed to guaranty the boundedness of the system states in the sense of mean-square, in a determined time interval. In the next step, the results are extended for the finite-time stochastic stabilization problem of this specific class of systems and the output feedback controller is designed such that the closed-loop system is stochastically finite-time stable. All the results are presented in the form of new linear matrix inequalities (LMIs). The main contribution of this paper is utilizing the static output feedback controller for finite-time stochastic stabilization of the closed-loop system; besides, new LMIs are employed to calculate the output feedback control law. Also, in order to verify the theoretical results and show the applicability of the proposed controller, simulations are performed for two systems.

This paper aims to present a novel and comprehensive model for actuator faults to address the problem of robust fault-tolerant controller design for networked control systems (NCSs) in the presence of phenomena such as random delays, model uncertainties, and actuator faults. For this purpose, firstly, the NCS has been appropriately modeled as discrete-time Markovian jump systems (MJSs) with partly-unknown transition probabilities. Then, the problem of mode-dependent static output feedback controller design has been studied not only as a convex optimization problem but also in the form of linear matrix inequalities (LMIs). Notably, the designed controller guaranteesthe stochastic stability of the closed-loop system in the presence of actuator faults and uncertainties. Finally, through numerical simulations, the theoretical results of this study are proved, and it has been shown that this method is more efficient and superior than other methods.

I‌n t‌h‌i‌s p‌a‌p‌e‌r, w‌e c‌o‌n‌s‌i‌d‌e‌r a d‌y‌a‌d‌i‌c s‌u‌p‌p‌l‌y c‌h‌a‌i‌n w‌i‌t‌h a‌n u‌n‌c‌e‌r‌t‌a‌i‌n s‌u‌p‌p‌l‌i‌e‌r. M‌o‌s‌t e‌x‌i‌s‌t‌i‌n‌g s‌t‌u‌d‌i‌e‌s i‌n s‌u‌p‌p‌l‌y c‌h‌a‌i‌n a‌n‌d i‌n‌v‌e‌n‌t‌o‌r‌y l‌i‌t‌e‌r‌a‌t‌u‌r‌e a‌s‌s‌u‌m‌e t‌h‌a‌t t‌h‌e s‌u‌p‌p‌l‌i‌e‌r i‌s c‌o‌n‌t‌i‌n‌u‌o‌u‌s‌l‌y a‌v‌a‌i‌l‌a‌b‌l‌e t‌o g‌e‌t t‌h‌e o‌r‌d‌e‌r f‌r‌o‌m t‌h‌e r‌e‌t‌a‌i‌l‌e‌r. I‌n t‌h‌i‌s s‌t‌u‌d‌y, t‌h‌i‌s a‌s‌s‌u‌m‌p‌t‌i‌o‌n i‌s r‌e‌l‌a‌x‌e‌d a‌n‌d w‌e a‌s‌s‌u‌m‌e t‌h‌a‌t t‌h‌e s‌u‌p‌p‌l‌y p‌r‌o‌c‌e‌s‌s i‌s u‌n‌r‌e‌l‌i‌a‌b‌l‌e. A‌n u‌n‌r‌e‌l‌i‌a‌b‌l‌e s‌u‌p‌p‌l‌i‌e‌r a‌l‌t‌e‌r‌n‌a‌t‌e‌s b‌e‌t‌w‌e‌e‌n a‌v‌a‌i‌l‌a‌b‌l‌e (O‌N) a‌n‌d u‌n‌a‌v‌a‌i‌l‌a‌b‌l‌e (O‌F‌F) s‌t‌a‌t‌e‌s. T‌h‌e O‌N a‌n‌d O‌F‌F d‌u‌r‌a‌t‌i‌o‌n‌s a‌r‌e c‌o‌n‌s‌i‌d‌e‌r‌e‌d t‌o b‌e i‌n‌d‌e‌p‌e‌n‌d‌e‌n‌t e‌x‌p‌o‌n‌e‌n‌t‌i‌a‌l v‌a‌r‌i‌a‌b‌l‌e‌s. B‌o‌t‌h t‌h‌e r‌e‌t‌a‌i‌l‌e‌r a‌n‌d t‌h‌e s‌u‌p‌p‌l‌i‌e‌r u‌s‌e a (R, Q)-t‌y‌p‌e c‌o‌n‌t‌i‌n‌u‌o‌u‌s r‌e‌v‌i‌e‌w p‌o‌l‌i‌c‌y. T‌h‌e r‌e‌t‌a‌i‌l‌e‌r f‌a‌c‌e‌s P‌o‌i‌s‌s‌o‌n d‌e‌m‌a‌n‌d‌s. T‌h‌e t‌r‌a‌n‌s‌p‌o‌r‌t‌a‌t‌i‌o‌n t‌i‌m‌e b‌e‌t‌w‌e‌e‌n t‌h‌e o‌u‌t‌s‌i‌d‌e s‌u‌p‌p‌l‌i‌e‌r a‌n‌d t‌h‌e s‌u‌p‌p‌l‌i‌e‌r, a‌s w‌e‌l‌l a‌s t‌h‌e t‌r‌a‌n‌s‌p‌o‌r‌t‌a‌t‌i‌o‌n t‌i‌m‌e b‌e‌t‌w‌e‌e‌n s‌u‌p‌p‌l‌i‌e‌r‌s a‌n‌d t‌h‌e r‌e‌t‌a‌i‌l‌e‌r, a‌r‌e a‌s‌s‌u‌m‌e‌d t‌o b‌e c‌o‌n‌s‌t‌a‌n‌t. T‌h‌e l‌e‌a‌d t‌i‌m‌e t‌h‌a‌t t‌h‌e r‌e‌t‌a‌i‌l‌e‌r e‌x‌p‌e‌r‌i‌e‌n‌c‌e‌s i‌s a n‌o‌n-z‌e‌r‌o r‌a‌n‌d‌o‌m v‌a‌r‌i‌a‌b‌l‌e, w‌h‌i‌c‌h i‌s c‌o‌m‌p‌o‌s‌e‌d o‌f c‌o‌n‌s‌t‌a‌n‌t t‌r‌a‌n‌s‌p‌o‌r‌t‌a‌t‌i‌o‌n t‌i‌m‌e a‌n‌d a r‌a‌n‌d‌o‌m d‌e‌l‌a‌y t‌h‌a‌t o‌c‌c‌u‌r‌s d‌u‌e t‌o l‌a‌c‌k o‌f i‌n‌v‌e‌n‌t‌o‌r‌y a‌t t‌h‌e s‌u‌p‌p‌l‌i‌e‌r. T‌h‌e s‌h‌o‌r‌t‌a‌g‌e a‌t t‌h‌e r‌e‌t‌a‌i‌l‌e‌r i‌s b‌a‌c‌k‌o‌r‌d‌e‌r‌e‌d, a‌n‌d d‌e‌l‌a‌y‌e‌d r‌e‌t‌a‌i‌l o‌r‌d‌e‌r‌s a‌r‌e s‌a‌t‌i‌s‌f‌i‌e‌d o‌n a f‌i‌r‌s‌t-c‌o‌m‌e, f‌i‌r‌s‌t-s‌e‌r‌v‌e‌d b‌a‌s‌e. T‌h‌e m‌a‌i‌n c‌o‌n‌t‌r‌i‌b‌u‌t‌i‌o‌n o‌f t‌h‌i‌s p‌a‌p‌e‌r i‌s c‌o‌n‌s‌i‌d‌e‌r‌i‌n‌g b‌o‌t‌h t‌h‌e i‌n‌t‌e‌g‌r‌i‌t‌y a‌n‌d u‌n‌c‌e‌r‌t‌a‌i‌n‌t‌y i‌n t‌h‌e a‌b‌o‌v‌e s‌u‌p‌p‌l‌y c‌h‌a‌i‌n. U‌s‌i‌n‌g t‌h‌e i‌d‌e‌a o‌f t‌h‌e o‌n‌e-f‌o‌r-o‌n‌e o‌r‌d‌e‌r‌i‌n‌g p‌o‌l‌i‌c‌y c‌o‌s‌t, w‌e d‌e‌r‌i‌v‌e a r‌e‌l‌i‌a‌b‌l‌e a‌p‌p‌r‌o‌x‌i‌m‌a‌t‌i‌o‌n f‌o‌r t‌h‌e t‌o‌t‌a‌l c‌o‌s‌t f‌u‌n‌c‌t‌i‌o‌n o‌f t‌h‌e d‌e‌s‌c‌r‌i‌b‌e‌d s‌y‌s‌t‌e‌m, a‌s a w‌e‌i‌g‌h‌t‌e‌d m‌e‌a‌n o‌f c‌o‌s‌t‌s o‌f a o‌n‌e-f‌o‌r-o‌n‌e o‌r‌d‌e‌r‌i‌n‌g p‌o‌l‌i‌c‌y. F‌i‌n‌a‌l‌l‌y, u‌s‌i‌n‌g s‌i‌m‌u‌l‌a‌t‌i‌o‌n s‌t‌u‌d‌i‌e‌s, w‌e s‌h‌o‌w t‌h‌a‌t a‌b‌s‌o‌l‌u‌t‌e e‌r‌r‌o‌r‌s a‌r‌e i‌g‌n‌o‌r‌a‌b‌l‌e.

This paper addresses robust state estimation problem for Genetic Regulatory Networks (GRNs). A delay-dependent robust filter is designed for a realistic nonlinear stochastic model of GRN. The model provided is the most complete model used in the literature so far, in the sense that delays are time-varying, parameter uncertainties (time-varying and norm-bounded) are considered, stochastic noises appear at the state equations as well as the measurement equations. Besides, stochastic noise and disturbance are considered simultaneously in this model. Using a proper Lyapunov-Krasovskii functional based on delay decomposition approach, sufficient conditions for the existence of the filter are derived in terms of linear matrix inequality (LMI). These conditions ensure robust asymptotic mean square stability of the filtering error dynamics with a prescribed disturbance attenuation level. By use of delay decomposition approach and using a lemma containing a stochastic integral inequality, the obtained conditions are delay-dependent and have less conservativeness. The filter parameters are determined then, as the solution of another LMI. A simulation study is also given to show the effectiveness of the proposed filter design procedure.

Highway construction projects are conducted in an outdoor environment, and are therefore sensitive to various weather conditions, especially rainfall. Rainfall is regarded as a major risk factor, which often leads to partial or complete suspension of construction project operations. In this paper, a decision support system is developed for quantifying the impact of rainfall and temperature on highway construction schedules. The system incorporates a database that uses historical daily rainfall and temperature data alongside a knowledgebase that includes a fuzzy inference system, employing fuzzy rules acquired from experts in highway construction. As the monthly delays are random variables, probability theory is also applied for frequency analysis. This system can be useful in preparing realistic schedules for the preconstruction stage and in analyzing weather-related claims for the post construction stage. It can also be used in predicting the impact of short term weather forecasts in the real- time construction process. To demonstrate the usefulness of the system, it has been applied to the cities of Tehran and Gorgan in Iran, which have very different climates, and the results are compared and discussed. The results show how the differences in climate of these cities and the time and stage of construction are reflected in predicted construction delays.

In this research the chaocity of precipitation time series in daily, weekly and monthly scales in the Pole-Kohneh and Ghourbaghestan Stations located in Qarah-Soo watershed is investigated. In order to reassure about the absence of stochastic behavior of the time series, method of surrogate data is applied. Thereafter, the aperiodicity of the time series is investigated using power spectrum analysis. The results of chaocity investigation indicates that according to correlation dimension method and Lyapunov exponent, the daily scale is not chaotic but weekly and monthly scales are chaotic (existence of the largest Lyapunov exponent and correlation dimension between 4 to 7). Also, the phase space reconstruction is done using time delay and embedding dimension method (embedding dimensions of 7 to 10 and time delay of 2 to 4 for Pole-Kohneh Station; embedding dimensions of 9 to 19 and time delay of 2 to 3 for Ghourbaghestan Station). Findings of the research indicate the stochasticity of short term precipitation time series (daily) and chaosity of medium term precipitation time series (weekly and monthly).

To determinine the timeliness cost of machine operation, farm-level data were collected from random sample of 227 sugar beet farms located in the three major districts of Fars province of Iran: Eghlid, Marvdasht and Fasa in 2006. The results showed that timeliness cost of tillage practices (plough and land leveling), applying fertilizers before planting and timeliness in seed planting per everyday lag were 12237, 3147, 881 and 2622 Rials/ha respectively. Timeliness cost of machine operation in stages of applying chemical fertilizers after planting, applying chemical pesticides and cultivation per everyday lag were 881, 1101 and 3671 Rials/ha respectively. The results of production elasticity indicated a significant negative relationship between timeliness of tillage practices, machine operations (such as plough, land leveling, planting and …) and yield of sugar beet.