Generalized Predictive Control in a Wireless Networked Control System

Abstract

The NCS (networked control system) is different from the conventional control systems which is the integration of the automation and control over communication network. When an NCS operates over the communication network, one of the major challenges is the network-induced delay in data transfer among the controllers, actuators, and sensors. This delay degrades system performance and causes system unstablility. This paper proposes a GPC (generalized predictive control) with the Kalman state estimator to compensate for the network-induced delay and packet loss. The GPC is implemented in WiNCS (Wireless NCS) based on IEEE 802.11 standard. An analytical NCS model and NS2 (network simulator version 2) are developed to simulate and evaluate the performance under the effect of various delays and packet loss rates. The result shows that the proposed GPC is adaptive and robust to the uncertainties in a time-delay system. The WiNCS is evaluated with latency and throughput measurements in various environments. The experiment setup conforming to the IEEE 802.11 standard achieves an average latency of 1.3 ms and a data throughput of 3.000 kB/s up to a distance of 70 m. The results demonstrate the feasibility of real-time closed-loop control with the proposed concept.

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

“…As an example, the inter-download times of video segments are predicted in [102], where the output sequences are the interdownload times of the already downloaded segments and the states are the instants of the next download request. ARIMA: [13], [38], [40], [46], [47], [54], [58], [59], [63], [100], [119] Kalman: [32], [ CF: [16], [134], [149] Cluster: [15], [34], [51], [117], [122], [123], [148], [156] Decision trees: [35], [98], [ Functional: [28], [29], [38], [64], [99], [104], [105] SVM: [51], [114], [139] ANN: [14], [48], [106], [ 2) Bayesian inference: This approach allows to make statements about what is unknown, by conditioning on what is known. Bayesian prediction can be summarized in the following steps: 1) define a model that expresses qualitative aspects of our knowledge but has unknown parameters, 2) specify a prior probability distribution for the unknown parameters, 3) compute the posterior probability distribution for the parameters, given the observed data, and 4) make predictions by averaging ove...…”

“…Traffic prediction is also addressed in [99], where the authors propose to use a database of events (concerts, gatherings, etc.) to improve the quality of the traffic prediction in case of unexpected traffic patterns and in [100], where a general predictive control framework along with Kalman filter is proposed to counteract the impact of network delay and packet loss. The objective of [101] is to build a model for user engagement as a function of performance metrics in the context of video streaming services.…”

Application of Wireless Network Control to Course-Keeping for Ships

“…In [11], a controller with explicit consideration of multipacket transmission was investigated with a special focus on lossy multipacket transmission in the wireless communication context. In [12], a generalized predictive control with the Kalman state estimator was proposed to compensate for the wireless network-induced delay and packet loss.…”

Autonomous Internet of Things predictive control application based on wireless networked multi-agent topology and embedded operating system

“…Despite the plentiful achievements, many representative results have studied the network predictive control (NPC) approach on the NCS structure. Although, there is little research on the problem of wireless network communication 18–21 which can be highlighted as follows. Wang et al 18 present an event-driven predictive control model for a W-NCS with packet losses in the feedback channel.…”