Optimal Scheduling for Discounted Age Penalty Minimization in Multi-Loop Networked Control

Driven by recent telecommunication demand, shared communication channels are more abundant today than ever before. These shared wired/wireless networks for communication are being exploited more and more in control systems to connect sensors, controllers and actuators resulting in so-called networked control systems (NCSs). NCSs replace the more traditional control systems where dedicated point-to-point (wired) connections are being used. The advantages of using NCS technology are inexpensive and easily modifiable communication links which allow control algorithms to be easily implemented in situations where dedicated connections are not possible (either economically or physically). However, the drawback is that the control system is susceptible to undesirable (possibly destabilizing) side-effects such as time-varying transmission intervals, time-varying delays, packet dropouts, quantization and a shared communication medium. These network-induced effects undermine fundamental assumptions on which traditional control theory is built and, therefore, it is essential to develop new techniques and tools that can be used to analyze and/or design control systems which communicate via a shared network. This thesis advances NCS analysis and design methodologies by contributing new theoretical developments, new software tools and new experimental validation results. The first new theoretical development is in the area of analyzing robust stability properties with respect to network-induced effects. A sum of squares (SOS) approach for a class of nonlinear NCSs incorporating bounded time-varying delays, bounded time-varying transmission intervals and a shared communication medium is developed. Mathematical models that describe these nonlinear NCSs are cast into suitable hybrid system formulations. Based on these hybrid system formulations, (families of) Lyapunov functions are constructed using SOS techniques. Amongst other benefits, it is shown that this technique improves the guaranteed robustness margins compared to existing work. The second new theoretical development concerns the design of networkaware decentralized controllers guaranteeing robust stability properties with respect to network-induced effects. We develop one of the first approaches based on semidefinite programming techniques to synthesize stabilizing decentralized observer-based output-feedback controllers for linear plants where the controllers, sensors and actuators are connected via a shared communication network subject to time-varying transmission intervals and delays. To effectively deal with the shared communication medium, a switched observer structure is adopted that switches based on the transmitted measured outputs and a switched controller structure is also adopted that switches based on the transmitted control inputs at each transmission time. By taking a discrete-time switched linear system perspective on modeling these decentralized NCSs, we are able to derive a general model that captures all these networked and decentralized control aspects. We provide linear matrix inequality (LMI)-based synthesis conditions which, if satisfied, provide stabilizing observer-based controllers that are both decentralized and robust to network effects. Regarding new software tools, the first prototype of a toolbox is developed to automate (robust) stability analysis (and controller design) for NCSs. Specifically, it is shown that the toolbox can be employed to efficiently verify if a linear time-invariant (LTI) plant and an LTI controller interconnected with a shared network are robust to certain network imperfections. The main intention of the toolbox is to make the available theory readily accessible to and applicable for the general control community. Additionally, the chosen software structure enables the incorporation of custom models or custom stability/performance analysis conditions in an easy manner, thereby allowing the control community to contribute and to further develop the toolbox. Finally, an experimental case study involving a wirelessly controlled inverted pendulum/cart system is investigated. In particular, the communication network itself is analyzed such that the network-induced effects can be characterized in terms of bounds on the transmission intervals and transmission delays. Based on these bounds, the prototype toolbox is applied to analyze the robustness regions for different performance specifications, which aid in tuning the controller to achieve more closed-loop robustness with respect to the network-induced effects. This leads to a validation of the developed theory in an experimental setting. In addition to the validation of the developed theory, many new insights into the network behavior are obtained and explained, thereby raising new interesting questions for future research on NCSs.

Wireless Model Based Predictive Networked Control System

The performance of a networked control system (NCS) is affected directly and indirectly by time delays generated in network communication. This paper addresses these time delays, their effects on system performance and the measures taken in an NCS design. For a time delay that is random but less than one sampling interval T, we model the system as a time-invariant control system with constant time delay T. For a time delay that is random and greater than one sampling interval T, we consider the system as a control system with packet drop and model it as a jump linear control system. Based on the above models, we propose a systematic method to improve the control system quality of performance by choosing a proper sampling interval to reduce data transmission, optimally scheduling to minimise packet loss and optimising controller design in case of packet loss. The simulation results and lab experiments demonstrate that the design method results in high performance in networked real-time control system.

In this paper, a novel approach for networked control system (NCS) task scheduling is proposed. The proposed neural-genetic method utilizes the information about the quality of service (QoS) over the communication network and enables online reconfigurable scheduling on distributed environment. In this way the NCS's bandwidth can be shared properly among different parallel control tasks. For NCS, two significant factors of QoS that affect validity of scheduling results are the packet loss and delay, which occurred in the communication among tasks. By adopting a Elman neural network based prediction model, the one-step ahead packet loss and time delay are obtained. The knowledge about the predict QoS factors, combined with the task execution features and the resources available in the system, are used as an entry to improve the decisions of the proposed scheduling algorithm. Such algorithm uses genetic algorithm techniques to find out the appropriate task scheduling scheme to adapt changes of application and communication circumstance. The proposed neural-genetic approach is evaluated through simulation by using a model parameterized with the features obtained from a real scenario of Ethernet based control system. The simulation results clearly show the effectiveness of the proposed approach in solving the task scheduling problems in NCS.

In this paper, we introduce a decentralized event-triggered scheduling scheme for multi-loop Networked Control Systems (NCSs) in which the individual control loops are coupled through a shared communication channel. The proposed scheduling design combines deterministic and probabilistic attributes to efficiently allocate the limited communication resource among the control loops in an event-based fashion. Based on local error thresholds, each control loop determines whether to compete for the channel access. As a result, control loops with higher transmission priorities are more likely to utilize the channel, which in turn leads to more efficient usage of the limited resource. Each eligible subsystem then attempts to transmit at times specified by local waiting times, which are randomly distributed and local-error dependent. In this manner, the probability of data collisions in the communication channel is reduced. If the channel capacity is reached at some time instants, data packets are dropped. We demonstrate stochastic stability of such NCSs in terms of Lyapunov Stability in Probability (LSP). The numerical results show that our approach improves resource utilization and reduces the networked-induced error variance in comparison with time-triggered, random access, and centralized scheduling policies.

Design, Analysis and Real-time Implementation of Networked Predictive Control Systems

Although there has been a lot of work about the synthesis and analysis of networked control systems (NCSs) with quantizers as well as NCSs with packet losses, most results are developed for the case of considering the quantizers and packet losses separately (either only concentrating on the data quantization or only focussing on the packet losses). In this paper, we address the synthesis approach of model predictive control (MPC) for NCSs subject to both data quantization and packet losses. This is motivated by the fact that it is common and natural in NCSs that the issues of the quantization and packet losses are coexisting. We firstly propose a novel model to describe the state and input quantization and the packet losses of the NCS in a unified framework. Further, from the new model, a MPC strategy is developed, which can guarantee the recursive feasibility. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

The use of a communication network in the closed-loop control systems has many advantages such as remotely controlling equipment, low cost, easy to maintenance, efficient information transmission, etc. However, the Distributed or Networked Control Systems (NCS) has many drawbacks, such as network-induce end-to-end time delay and packet loss, which lead to a significant degradation in controller performance and may result in instability. Aiming at solving performance degradation in NCS, this paper propose to take the advantages and strength of the conventional Proportional-Integral-Derivative (PID), Fuzzy Logic (FL), and Gain Scheduling (GS) fundamentals to design a Fuzzy-PID like-Gain Scheduling (F-PID-GS) control technique, which has been proved to be effective in obtaining better performance. The TrueTime toolbox is used to establish the simulation model of the NCS. Ethernet as a communication network is simulated for different load conditions and random packet loss. The design approach is tested on a second-order stepper motor. The results obtained show the effectiveness of the proposed approach in improving the overall system performance.

This kind of control systems wherein the control loops are closed through a real time communication network can be called networked control systems. Networked control system is a new hot topic in the control engineering. The reduction of bandwidth necessitated by the communication network in a networked control system is a major concern. For this reason, a computation method of reducing the number of data packet exchanges between the sensor and the controller/actuator was explored. A novel model of controller and intelligent prtotocol were also designed for reducing the number of data packet exchanges which could make the networked control system stable.

Connecting a complex control system with various sensors, actuators, and controllers as a networked control system by a shared data network can effectively reduce complicated wiring connections. This system is also easy to install and maintain. The trend is to use networked control systems for time-sensitive applications, such as remote DC motor actuation control. The performance of a networked control system can be improved if the network can guarantee quality-of-service (QoS). Due to time-varying network traffic demands and disturbances, QoS requirements provided by a network may change. In this case, a network has to reallocate its resources and may not be able to provide QoS requirements to a networked control application as needed. Therefore, the application may have to gracefully degrade its performance and perform the task as best as possible with the provided network QoS. This paper proposes a novel approach for networked DC motor control systems using controller gain adaptation to compensate for the changes in QoS requirements. Numerical and experimental simulations, and prototyping, are presented to demonstrate the feasibility of the proposed adaptation scheme to handle network QoS variation in a control loop. The effective results show the promising future of the use of gain adaptation in networked control applications.

This study is devoted to the problem of designing a robust output‐feedback discrete‐time sliding mode control (ODSMC) for the networked systems involving both measuring and actuating data packet losses. Packet losses in the networked control systems (NCSs) have been modelled by utilising the probability and the characteristics of the sources and the destinations. Here, the well‐known Bernoulli random binary distribution is used to model consecutive packet losses in the NCSs. In this study, first, a robust observer‐based discrete‐time sliding mode control is proposed for the NCSs including random packet losses. The packet losses occur in the channels from the sensors to the controller and the channels from the controller to the actuators. Then, using the notion of exponential mean square stability, the boundedness of the obtained closed‐loop system is analysed with a linear matrix inequality approach. Our proposed robust ODSMC can be applied to unstable NCSs, and there is no need to stabilise the underlying system in advance. Illustrative examples are presented to show the effectiveness of the proposed approach.

Due to major advancements in the area of networking over the past decade, a new paradigm of control systems has emerged, namely networked control systems. Such systems differ from classical control systems in that their control loops are closed around communication networks. Thus, the need for new stability and performance guarantees arises. In this paper, we aim to shed some light on the major advancements and inherent problems in networked control systems.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.

Network control system (NCS) is a kind of distributed control system, which is widely used in industrial control process. The communication network is used to exchange information and control signals among various physical components of subsystems. The transmission of data and information on the communication network to achieve system control will lead to the inevitable delay of information transmission time, which will reduce the performance and even make the system unstable. Reducing network traffic is one of the most effective methods to reduce the impact of network control system delay on system performance. This paper discusses the adjustable deadband as a method to reduce network traffic in networked control systems, and proposes a modeling method of networked control systems with random delay and communication constraints based on quasi T-S fuzzy model. Based on robust fault-tolerant control theory and information scheduling theory, the integrity design of this kind of networked control system is analyzed. After detailed theoretical analysis, simulation results are given to further verify the proposed scheme.