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Recently, the modeling and design of distributed controllers in Cyber-Physical Systems (CPSs), which suffers from messages lost, delay variation and jitter, has gained lots of research attentions. A special CPS, Arbitrated Networked Control System (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, w...
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... (BD) of an ECU. Wakeup sym- bol is used to bring the multinode clusters out of the low-power state by sending a pattern that causes the BDs to wake up the local ECU. The wakeup signals distributed on the network are supported by communication controllers and BD devices [14]. We consider a typical distributed embedded architecture shown in- Fig. 1 (see [1, Fig. 1(a)]). FlexRay networking supports single-channel configuration, as well as the dual-channel one with some nodes connected to both channels and others connected to just one channel [13]. We extend the above-mentioned ANCS to a hybrid bus-star topol- ogy with two channels (see [14, Figs. 1-10]). Here, channel A is ...
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... ECU. Wakeup sym- bol is used to bring the multinode clusters out of the low-power state by sending a pattern that causes the BDs to wake up the local ECU. The wakeup signals distributed on the network are supported by communication controllers and BD devices [14]. We consider a typical distributed embedded architecture shown in- Fig. 1 (see [1, Fig. 1(a)]). FlexRay networking supports single-channel configuration, as well as the dual-channel one with some nodes connected to both channels and others connected to just one channel [13]. We extend the above-mentioned ANCS to a hybrid bus-star topol- ogy with two channels (see [14, Figs. 1-10]). Here, channel A is implemented as a bus ...
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... order to illustrate an FSDMC model for the ANCS in this paper, we still consider the typical distributed embedded architecture proposed in [1] (see- Fig. 1), where an ECU collects sensor data (denoted as a task T s ). A communication bus (e.g., FlexRay) then transmits the data as message m 1 to a second ECU (marked as ECU 3 ), where the resident control algorithm is implemented (denoted as task T c ). The output of the controller is then sent as a message m 2 to the actuator in ECU 2 , ...
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... the number of the semi-dormant controllers is obtained, that is, d = 4. The total arrival rate is also generated by summing the second row, that is, λ = c i=1 ω i (t) = 5000B = 5kB. When Algorithm 1 is called ten thousand times, that is, M = 10 000, the statistical frequencies of the number of semi- dormant controllers is derived, and is shown in Fig. 11. We find that the number of semi-dormant controllers generally follows a normal distribution. Hence, we choose the corresponding semi- dormant controller number of maximal frequency as the optimal value˜dvalue˜ value˜d * , that is, ˜ d * = 4. It is noteworthy that the sampling data in Fig. 11 is not unique, but generally follows a ...
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... semi- dormant controllers is derived, and is shown in Fig. 11. We find that the number of semi-dormant controllers generally follows a normal distribution. Hence, we choose the corresponding semi- dormant controller number of maximal frequency as the optimal value˜dvalue˜ value˜d * , that is, ˜ d * = 4. It is noteworthy that the sampling data in Fig. 11 is not unique, but generally follows a normal distri- ...
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... we derive the optimal values˜Φvalues˜ values˜Φ, ˜ F , and˜τand˜ and˜τ nom by Al- gorithm 2. While calling MOPSO, we use a population of 100 particles, a repository size of 40 particles, a mutation rate of 0.5, an iteration times of 3000, and 30 divisions for the adaptive grid. Furthermore, let λ = 5.0. The decision variables are cho- Fig. 12 respectively shows four Pareto fronts that are produced by calling the MOPSO four times. In Fig. 12(a)-(d), the selected Pareto optimal solution is determined while˜dwhile˜ while˜d * = 4, 4, 3, 5, ...
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... 2. While calling MOPSO, we use a population of 100 particles, a repository size of 40 particles, a mutation rate of 0.5, an iteration times of 3000, and 30 divisions for the adaptive grid. Furthermore, let λ = 5.0. The decision variables are cho- Fig. 12 respectively shows four Pareto fronts that are produced by calling the MOPSO four times. In Fig. 12(a)-(d), the selected Pareto optimal solution is determined while˜dwhile˜ while˜d * = 4, 4, 3, 5, ...
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... Pareto optimal solutions and the corresponding nondom- inated vectors in Fig. 12(a) are shown in Table ...
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Citations
... In the sensor, a probability ratio-dependent scheduling is built to smartly pick informative transmission sensors measurement along a moderate rate of transmission limit for networks that are secure [16]. For cyber-physical system (CPS) along a flexible hierarchical time-division multiple access, an arbitrated networked control system (ANCS) along with schedule actions or arbitration networks in control system suggested by experts [17]. ...
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... An arbitrated networked control system (ANCS) with dual communication channels, dual-channel ANCS for short, was proposed in [6] to maintain load balance and guarantee global consistency (see Fig. 2 in [6]). Gong et al. [7] considered the problem of dynamic resource provisioning of the flat semi-dormant multicontroller (FSDMC) under optimal costperformance ratio (CPR). ...
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... An arbitrated networked control system (ANCS) with scheduling or arbitrating networks in a control system is proposed for CPS with a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy with event trigger protocol (Gong et al. 2017). A multiobjective optimization problem is formulated to minimize the nonlinear energy consumption function and the simple delay function. ...
Task scheduling is one of the challenging research problems in distributed computing, especially in a complex scenario like a cyber–physical system. The cyber–physical system consists of a physical system and cyber system which are operated on the different domain of response time. The performance of the scheduling algorithm under the cyber–physical system depends on both cyber and physical factors. But both the factors are unpredictable one in reality which makes the scheduling a challenging one. This paper proposes a fuzzy logic controller based on an efficient scheduler which tackles the above problem. The proposed dynamic scheduler involves three scheduling algorithms which will be selected by the fuzzy controller based on the dynamic behavior of a cyber–physical system. A neural network is incorporated into the fuzzy controller to provide the learning capability, and an adaptive neural fuzzy inference system (ANFIS) is designed. The simulation results demonstrate the superiority of the proposed mechanism in comparison with the existing one.
... Cyber-physical systems (CPS) deal with the properties of modern sophisticated systems that materialize from a natural confluence of computing, control and communications. Many systems appertaining to avionics, transport, industrial automation and medicine employ distributed processing techniques that require communication networks to exchange data between different sensors and actuators which result in complexity and heterogenity [1]. CPS poses an array of intellectual challenges that stem not from union, rather, intersection of the physical and the cyber worlds. ...
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... A special type of CPS named arbitrated networked control system (ANCS) was first presented in [16]. Gong et al. [17] extended the ANCS and proposed an ANCS with dual communication channels (see [17,Fig. 2]). ...
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The analysis problem of message response time is a key and challenging problem for in-vehicle networks that has gained much research attention in recent years. The arbitrated networked control system (ANCS), which is a special cyber–physicalsystem (CPS), was recently designed for scheduling or arbitrating networks in a control system. A dominant feature of this CPS is the arbitration of messages on a shared communication medium. The control applications in an ANCS are sensitive to the end-to-end delay of message responses from sensors to actuators. In this study, a multi-hierarchical flexible TDMA/fixed priority scheduling policy is first adopted to configure the bus in an ANCS, which is based on the event trigger protocol. Then, an M/PH/1 queue model with random-sized batch arrivals is used to model the control application in the ANCS, and to obtain the stationary probability for each control application. On the basis of the model, a constrained optimization problem is formulated to minimize the nonlinear cost function of the ANCS, and the probability density function of the uncertain response delay for each message is generated. The use of an optimal minislot algorithm that is based on the DFP variable-scale method and a parameter estimation algorithm of the pdfs of the uncertain response delays is proposed to obtain optimal values of the bus minislot and matrix parameters of PH distribution, respectively. Furthermore, the probability transformed GM (1,1) (PTGM (1,1)) with two parameters, which is a novel grey model (1,1) (GM (1,1)), is proposed for predicting the response delay for each message, in which a two-parameters optimization algorithm of PTGM (1,1) that is based on the DFP-method is provided to minimize the mean absolute percentage error and obtain the optimal values of the two parameters for each message. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and show the accuracy of the predicted results.
... YBER physical systems, which are distributed, networked systems that fuse computational processes with the physical world exhibiting a multidisciplinary nature, have recently become a research focus [1][2][3][4]. As a typical application of CPS in green transportation, electric vehicles have been widely studied with different topics by researchers and engineers from academia, industry and governmental organizations [5][6][7][8][9][10][11]. ...
As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
Many Cyber-Physical Systems (CPSs) in industrial applications are embedded control systems. With the increasing scale and complexity of such system, the resource efficiency in the system design has become an important issue. The resources of CPSs mainly include: computation resource, communication resource and memory resource. The CPSs combine computation, communication and instruction/data storage closely, and realize the combination and coordination of computing resources and physical resources. This paper classifies the existing mainstream research results from three aspect: computation resource, communication resource and memory resource, reviews the research hotspots of each research field, and discusses the urgent problems related to computation resource, communication resource and memory resource in CPSs, as well as the possible research directions in the future.
Cyber-physical system (CPS) is the product of technological development to a certain stage, and also is the future trends in information technology. High-performance computing ability is the guarantee of CPS’s real-time and accuracy applications, and the emergence of distributed technology provides the implementation possibility of high-performance CPS. Task scheduling is a typical combination optimization problem and the task allocation problem on multi-processor distributed systems refers to how to use system resources most efficiently in a distributed computing environment to complete a limited set of tasks. Based on the behavior of ants searching for food in nature, ant colony algorithm is a kind of positive feedback algorithm with good robustness and easy parallel implementation and has certain advantages for dealing with constraint satisfaction. In order to introduce an adaptive mechanism and mutation strategy, shorten the calculation time of ant colony algorithm, speed up CPS algorithm convergences, and improve distributed CPS prediction accuracy, this paper analyzed the research status and significance of ant colony algorithm, expounded the development background, current situation, and future challenges of task optimization and scheduling of distributed CPS, elaborated the principles and methods of ant colony optimization algorithm model and mathematical description of CPS task scheduling, proposed a task management model of distributed CPS based on improved ant colony algorithm, explored the task optimization scheduling of distributed CPS based on improved ant colony algorithm, and finally conducted an numerical simulation to test the effect the proposed algorithm and model. The simulation results show that the proposed algorithm model enhances the local search ability and improves the quality of the task scheduling problem, and has good effectiveness, stability and adaptability. The study results of this paper provide a reference for the further research on the optimization and scheduling of distributed CPS tasks.
