Networked Control System Architecture Research Articles

Small-world architecture of networked control systems

ABSTRACTThe paper proposes a method to generate communication structures for networked controllers that have the small-world property. This property, which is known from complex networks, means that in a large ring of agents, a small number of shortcut links substantially improve the performance of the overall system. To investigate the consequences of this property for control systems, the paper considers multi-agent systems with leader–follower structure where additional communication links among the local control stations are selected by the agents in a random way. The performance of the overall system is evaluated in terms of the delay with which the agents react on leader commands. The main result provides a relation between the probability p of the existence of additional edges in the communication graph and the performance of the overall system. It is proved that for any probability p > 0 there exists an upper bound of the performance index and that this bound is independent of the number of agents. Furthermore, a quantitative analysis shows that it is sufficient to connect the agents with a very low probability p ≈ 10% in order to get a satisfactory performance. These results are illustrated by a robot formation example.

Multi-Node Fault-Tolerant Two-Cell Real-Time S2A Network

This paper presents a novel fault-tolerant networked control system architecture consisting of two cells working in-line. This architecture is fault-tolerant at the level of the controllers as well as the sensors. Each cell is based on the sensor-to-actuator approach and has an additional supervisor node. It is proven, via analysis as well as OMNeT++ simulations that the production line succeeds in meeting all control system requirements with no dropped or over-delayed packets. A reliability analysis is then undertaken to quantitatively estimate the increase in reliability due to the introduction of fault-tolerance.

A secure control framework for resource-limited adversaries

Cyber-secure networked control is modeled, analyzed, and experimentally illustrated in this paper. An attack space defined by the adversary’s model knowledge, disclosure, and disruption resources is introduced. Adversaries constrained by these resources are modeled for a networked control system architecture. It is shown that attack scenarios corresponding to denial-of-service, replay, zero-dynamics, and bias injection attacks on linear time-invariant systems can be analyzed using this framework. Furthermore, the attack policy for each scenario is described and the attack’s impact is characterized using the concept of safe sets. An experimental setup based on a quadruple-tank process controlled over a wireless network is used to illustrate the attack scenarios, their consequences, and potential counter-measures.

Packetized MPC with dynamic scheduling constraints and bounded packet dropouts

We study a Networked Control System architecture which uses a communication network in the controller–actuator links. The network is affected by packet dropouts and allows access to only one plant input node at each time instant. This limits control performance significantly. To mitigate these limitations we propose a control and network protocol co-design method. Succinctly, the underlying features of the proposed method are as follows: a sequence of predicted optimal control values over a finite horizon, for an optimally chosen input node, is obtained using Model Predictive Control ideas; the entire resulting sequence is sent to the chosen input node; a smart actuator is used to store the predictions received and apply them accordingly. We show that if the number of consecutive packet dropouts is uniformly bounded, then partial nonlinear gain ℓ2 stability and also a more traditional linear gain ℓ2 stability can be ensured via appropriate choice of design parameters and the right assumptions. Whilst our results apply to general nonlinear discrete-time multiple input plants affected by exogenous disturbances, for a disturbance-free case we prove that Global Asymptotic Stability follows from our main result. Moreover, we show that by imposing stronger assumptions, Input-to-State Stability is achievable as well. Finally we demonstrate the potential of the proposed method via simulations.

Design and Implementation of Secure Networked Predictive Control Systems Under Deception Attacks

This brief addresses the security issues of data transmitted in networked control systems (NCSs), especially confidentiality, integrity and authenticity. A secure networked predictive control system (SNPCS) architecture is presented, which integrates the Data Encryption Standard (DES) algorithm, Message Digest (MD5) algorithm, timestamp strategy, and recursive networked predictive control (RNPC) method. The former three parts are used to form a secure transmission mechanism between the controller side and the plant side, which is responsible for enforcing the data confidentiality and checking the data integrity and authenticity. To guarantee the control system performance when suffering from deception attacks, the RNPC method based on round-trip time delays is proposed to compensate for the adverse effects introduced by the deception attacks as well as the network communication constraints, such as time-varying network delay, packet disorder and packet dropout. A theoretical result using the switched system theory is obtained for the closed-loop stability of the RNPC system. Practical experiments are performed to demonstrate the effectiveness of the proposed SNPCS.

A Multi-objective Optimization based on Hybrid Quantum Evolutionary Algorithm in Networked Control System

Abstract This paper proposed a new hybrid quantum clone evolutionary algorithm (HQCEA) in a two-layer networked learning control system (NLCS) architecture. This special architecture achieves better control performance, better interference rejection and_ increasing the adaptability to varying environment. The proposed scheduling algorithm HQCEA optimizes the network transmission period which increases the diversity of the solution space of functions and avoid trapping into local peak effectively. As thus, Network resources are allocated reasonably to reduce delays and dropped packets, improving network utilization with communication constraints. According to the simulation results, the HQCEA overcomes the shortcoming of the traditional QEA, and can deal with the continuous functions with multi-peak and complex plant successfully in a shorter time.

A two-layer networked learning control system using actor–critic neural network

Aiming at the fact that the controlled objects are becoming more and more complex, while the existing control strategies cannot fulfill the needs for higher quality control performance due to the limitations of the widely used single-layer networked control system architecture, a two-layer networked learning control system architecture is proposed. Under this architecture, independent local controllers are interconnected to form the lower layer, while a learning agent which communicates with the independent local controllers in the lower layer forms the upper layer. To implement such a system, a discard-packet strategy is firstly developed to deal with network-induced delay, data packet out-of-order and data packet loss. The cubic spline interpolator is then employed to compensate the lost data. Finally, the output of the learning agent using actor–critic neural network is used to dynamically tune the control signal of local controller. Control simulations of different ways for a nonlinear heating, ventilation and air-conditioning (HVAC) system are compared. Simulation results show that this new architecture can effectively improve the control performance.

Model Based Predictive Networked Control Systems

Networked control systems where the sensors, controller and actuators of a digital control system reside on different computer nodes linked by a network, aim to overcome the disadvantages of conventional digital control systems at the application level, such as difficulty of modification, vulnerability to electrical noise, difficulty in maintenance and upgrades. However random communication delay and loss on the network may jeopardize stability since the communication delay decreases the phase margin of the control system and data loss can be considered as noise. In this project, we propose a novel networked control method where satisfactory control is possible even under random delay and data loss. We keep a model of the plant inside the controller node and use it to predict the plant states into the future to generate corresponding control outputs. At every sampling period the states of the model are reset to the actual or predicted states of the plant. The ambiguity of plant state during periods of total communication loss are also addressed.The proposed model based predictive networked control system architecture is first verified by simulation on the model of a DC motor under conditions of data loss and noise. Then experiments are repeated on a dedicated test platform using a physical DC motor. Results show that significant amounts of data loss and delay can be tolerated in the system before performance starts to degrade.

PERFORMANCE LIMITS IN MULTI-CHANNEL NETWORKED CONTROL SYSTEM ARCHITECTURES

In networked control systems the communication links between controller and plant affect closed loop properties. To optimize performance, the communication medium should be used carefully. For that purpose, several configurations have been investigated. In the present work, we study a bit-rate limited multi-channel networked control system architecture, where communication is through parallel links. By adopting an additive quantization noise model, we characterize optimal coder-decoder pairs and show how to allocate individual bit-rates. The results obtained establish performance bounds for the multi-channel networked control system architecture.

Alice: An information‐rich autonomous vehicle for high‐speed desert navigation

AbstractThis paper describes the implementation and testing of Alice, the California Institute of Technology's entry in the 2005 DARPA Grand Challenge. Alice utilizes a highly networked control system architecture to provide high performance autonomous driving in unknown environments. Innovations include a vehicle architecture designed for efficient testing in harsh environments, a highly sensory‐driven approach to fuse sensor data into speed maps used by real‐time trajectory optimization algorithms, health and contingency management algorithms to manage failures at the component and system level, and a software logging and display environment that enables rapid assessment of performance during testing. The system successfully completed several runs in the National Qualifying Event, but encountered a combination of sensing and control issues in the Grand Challenge Event that led to a critical failure after traversing approximately 8 miles. © 2006 Wiley Periodicals, Inc.