Predictive controller for networked multi-agent systems with communication delay and packet loss

We describe distributed tracking of a linear dynamical system via networked sensors. The networked sensors communicate with each other by means of a multi-hop protocol over a communication network. MMSE-optimal solution is Kalman filtering when measurements are available centrally, but new methods are required to account for communication constraints. We derive optimal algorithms to deal with arbitrary network topology and then extend these results to account for communication delays and packet losses. The proposed techniques differ from related techniques proposed in two important aspects: a) there is no designated leader/fusion node and each sensor attempts to optimally track the system trajectory based on its local observations and time-dependent information available from other sensors in the network; b) the message computation at each sensor is structurally identical, where the computed message from each sensor is the innovation in the state conditioned on all the information available upto that time at each sensor. Consequently, the sensor network can be queried at any time and at any node to obtain optimal estimates for the state of the dynamical system

In this study, a particle swarm optimization (PSO) based Multi-Agent Reinforcement Learning (MARL) approach for load frequency control (LFC) of multi-area power system over communication network considering communication delay and packet loss is presented. A networked control system (NCS) is framed to model a constant delay and packet loss using Markovian approach. The controller using PSO based MARL technique having two levels of controlled action in each area of power system act as an estimator agent and controller agent respectively. The proposed method for frequency regulation of multi-area power system is studied in detail and verified for different types of load disturbances. In addition, to check the system dynamic performance of electric power system a mean square errors (MSEs) of states with different value of communication delay are computed. The simulation results of this study is validated and compared with some pre-design methods available from literatures, along with capacity of multi agent system (MAS) for application of LFC in power system over communication network.

Real-time telerobotic systems connected through packet networks belong to the broader family of Networked Control Systems, and can be easily destabilized by communication delay and packet losses, when they are not properly compensated. The solutions available in the literature are mainly based on Control Theory. This classical approach could be improved by the joint design of the network, e.g., the introduction of quality-of-service guarantees as currently done in teleconference applications. Control/network co-design needs a simulation framework where both aspects are properly and jointly addressed. The paper faces this topic starting from the discussion of its critical issues, and proposes a co-simulation tool based on SystemC for the network simulation and Matlab/Simulink.

The performance of networked control systems (NCS) in non-deterministic networks, such as the Internet depends largely on the network delays and packet losses. The reproduction of these delays and packet losses in a NCS implies the use of network simulators and emulators. In this paper, a system that performs as a sensor and as an actuator of Internet delays and packet losses is presented. It collects these parameters from the Internet and reproduces them into a local network data flow. The paper presents some comparative results and makes a performance analysis of the whole system.

Control of time-delay systems represents one important class of control problems. A lot of work has been done in recent years. Much of the research work has been focused on the stability analysis and stabilization of time-delay systems based on the Lyapunov functional and linear matrix inequality (LMI) approaches. While the LMI approach does provide an e±cient tool, the results are mostly only su±cient and only numerical solutions are available. Our research aims to present analytical solutions by some algebraic approaches, which can provide an insightful understanding of control problem of time-delay systems. On the other hand, there has also been significant recent interest in networked multi-agent system (MAS) where unavoidably there exist delays in information acquisition as well as information exchange between agents in addition to possible input/state delays in each agent. This motivates the study of MASs with input and communication delays. The thesis is divided into two parts: Part I is focused on optimal control of single agent systems with multiple input delays; Part II is on distributed consensus control of MAS with communication and input delays. More specifically, in Part I, we shall study the linear quadratic regulation (LQR) and tracking for a single agent with linear dynamics. We first focus on LQR for linear discrete-time systems with multiple delays in a single input channel. By introducing a backward stochastic system, we establish a relationship between the LQR problem for the original system and optimal estimation for a backward stochastic system which can be solved by using projection approach. The explicit optimal controllers are developed for both finite time and infinite time horizon cases. A parallel result for linear continuous-time systems is also established, where the optimal feedback gain is given in terms of the solution of Riccati-type partial differential equations (PDEs).

This paper studies the relationship between critical sampling interval and stability of the sampled-data control system with output feedback. The communication between system output measurements and controllers can be divided into two cases: no packet loss and packet loss. Packet loss process can also be divided into two cases: independent and identically distributed process and Markov chain. In the absence of packet loss, the upper bound of sampling interval is given to stabilize the whole system in the sense of mean square. In the case of independent and identically distributed packet loss, we give the range of sampling interval to ensure the mean square stable of the whole system, and determine the upper critical value of the sampling interval when the whole system is stable in the sense of mean square. Finally, in the case of Markov packet loss, the condition on sampling interval is given to ensure the mean square stability of the whole system.

An overview is presented of compression and packet loss effects in speech biometrics. These new problems appear particularly in recent applications of biometrics over mobile or Internet networks. The influence of speech compression on speaker recognition performance in mobile networks is investigated. In a first experiment, it is found that the use of GSM coding degrades the performance. In a second experiment, the features for the speaker recognition system are calculated directly from the information available in the encoded bit stream. It is found that a low LPC order in GSM coding is responsible for most performance degradations. A speaker recognition system was obtained which is equivalent in performance to the original one which decodes and reanalyses speech before performing recognition. The joint packet loss and compression effects over IP networks are also studied. It is experimentally demonstrated that the adverse effects of packet loss alone are negligible, while the encoding of speech, particularly at a low bit rate, coupled with packet loss, can reduce the verification accuracy considerably.

For constructing a decentralized optimal control strategy for networked multi-agent systems that can accomplish consensus in a leaderless structure, we employ the inverse optimal control approach which circumvents the task of solving a HJB equation by introducing a cooperative control Lyapunov function. The decentralized controller we obtain is more flexible than the controller u = Lx in fixed network, because it not only has the advantages to handle systems of high relative degree, but it can also improve the performance of networked multi-agent systems by adjusting a positive definite and symmetrical matrix. On the other hand, the decentralized controller is optimal with respect to a meaningful cost functional which is useful to quantify and improve the performance of networked multi-agent systems. Finally, examples are presented to illustrate both the design process and performance of cooperative controls.

The hierarchical networked control has attracted many attentions in the field of the islanded microgrid (MG). However, how to reduce the effects caused by random packet loss of communication is still need to be studied. In this study, the authors proposed a fully distributed cooperative secondary networked optimal control for both frequency and voltage restoration. A distributed consensus Kalman filter based estimator (DCKF‐estimator) is introduced to obtain the estimation of the output state with the communication packet loss. Then, by incorporating the DCKF‐estimator into the output feedback‐like controller, the optimal output feedback‐like gain is obtained based on the linear quadratic Gaussian‐like control scheme. In addition, they also deduced the stability condition of the islanded MG closed‐loop system. The simulations in different scenarios were carried out to evaluate the control performance. The results show that the proposed approach achieves accurate regulations (on frequency, voltage) and fast transient response, and has better stability and robust performance over communication packet loss.

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

This brief studies the predictive controller design of networked systems with communication delay and data loss. A networked predictive control scheme is employed to compensate for communication delay and data loss actively rather than passively. Based on analysis of the closed-loop networked predictive control systems, a design strategy of the predictive controller is proposed. The designed predictive controller can achieve the desired control performance and also guarantee the system stability. A numerical example demonstrates the compensation for communication delay and data loss in networked systems using the proposed predictive controller design strategy.

The wide availability of microprocessors with much higher computational power capabilities has encouraged many researchers during the last decade to introduce the predictive control techniques to the field of power electronics and motor drives. One technique of predictive control, which is called model predictive control, is introduced in this paper and its application to control a five-phase induction motor is also presented. The mathematical formulation of both model predictive torque control and model predictive speed control algorithms are explained, which shows that the algorithms' equations are intuitive so they can be easily understood and implemented. Since a load torque estimate is needed in the predictive speed controller, an observer based on the minimal-order Gopinath's method is designed to get the load torque value. A comparison between the two techniques is carried out based on a simulation study. Also, the simulation results show the effectiveness of the predictive control in achieving a high dynamic performance control of multiphase induction motor drives.

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

Technical reports indicate that wireless and mobile devices will account for 71% of all IP traffic by 2022, an increase of 19% over four years. This increase is related to advances in wireless data communication technologies. Wireless networks have become one of the most important ways to connect devices to the Internet, therein improving productivity and encouraging information sharing. IEEE 802.11, known as Wi-Fi, has become the main standard for wireless local area networks. The most important metrics for measuring the quality of Wi-Fi are delay, jitter, and packet loss. Packet loss occurs when one or more packets fail to reach their destination and can occur for a variety of reasons. Packet loss influences the user's perceived quality of applications over Wi-Fi networks, mainly multimedia and real-time applications. The availability of accurate models for packet loss in Wi-Fi networks enables the development of more efficient methods for performance analysis and network design, as well as better computational simulations. Modeling packet loss in such networks presents a major challenge because packets may be lost for many different reasons, including signal attenuation, noise, multipathing, signal refraction, thermal noise, competition for media access and buffer issues. In this paper, we provide an overview of the causes of packet loss and a comprehensive survey of the available models for packet loss in Wi-Fi networks. The potential benefits of the survey are: (i) the systematic presentation of available packet loss models for Wi-Fi networks, their parameters, and respective packet loss rate evaluation, (ii) comparison of models considering validation scenarios and input parameters, and (iii) description of open issues and future research directions. We hope that our analysis will help researchers understand the most important characteristics of the packet loss process in Wi-Fi networks and the strengths and weaknesses of the main packet loss models.