Stochastic adaptive event‐triggered control and network scheduling protocol co‐design for distributed networked systems

Abstract

In a distributed 'networked control system' (NCS), multiple physical systems or agents are connected to their corresponding controllers through a shared packet-switched communication network. For such distributed NCS, periodic sampled controller design is unsuitable to handle packet-switched closed-loop control systems and a novel stochastic optimal adaptive event-sampled controller scheme is proposed in the application layer for each physical system or agent expressed as an uncertain linear dynamic system. Lyapunov stability analysis will be utilised to derive the event trigger condition. In addition, a network scheduling protocol is also required for such NCS. Traditional network scheduling protocols are unsuitable for such NCS since the behaviour of the physical systems is disregarded during the protocol design. Therefore in this study, a novel distributed network scheduling protocol via cross-layer approach is developed to improve the performance of distributed NCS by minimising an overall system cost function which consists of the information collected from both the event-triggered controller for each physical system in the application layer and the distributed scheduling protocol from the network layer. It will be demonstrated that the proposed co-design approach will not only allocate the network resources efficiently but also it will improve the performance of the overall distributed NCS. Simulation results are included to demonstrate the effectiveness of the proposed cross-layer co-design.

Multi‐input–multi‐output system experimental validation of model‐free control and virtual reference feedback tuning techniques

“…In the MFC a -CFDL p experimental scenario, the azimuth motion is controlled by an MFC algorithm with the parameters K P = −0.021 and α = 1.7, which accomplish (18), and the pitch motion is controlled by a CFDL-MFAC algorithm withφ(1) = 2100, φ(1) ∈ (2085, 2115.5) according to (5) and 7, ρ = 50, η = 0.5, λ = 7, and μ = 0.79.…”

“…That is the reason why data-driven control is often associated with model-free control (MFC). The performance improvement and guarantee is carried out systematically by the proper definition of optimisation problems, and several objective functions (o.f.s) are used [1][2][3][4][5][6][7]. The main data-driven techniques that carry out the modelfree iterative update of controller parameters using experiments conducted on the CS are iterative feedback tuning (IFT) [8], correlation-based tuning [9], frequency-domain tuning [10], iterative regression tuning [11], simultaneous perturbation stochastic approximation [12,13], data-driven predictive control [14,15], model-free adaptive control (MFAC) [16], MFC [17], unfalsified control [18], adaptive online IFT [19], data-driven reinforcement learning control [20], and model-free or data-driven iterative learning control [21][22][23].…”

Dynamic Output Feedback Control of Discrete Markov Jump Systems based on Event-Triggered Mechanism

“…In [14], the event-triggered scheme is applied to the fault detection to guarantee the fault detection accuracy. Furthermore, an event-triggered scheme with an adaptive threshold would subserve the quality of the control; see [15,16].…”

Robust Stability and Stabilization Of TCP‐Networked Control Systems with Multiple Delay System Modeling

“…In the second approach, for a given communication network, the control performance is enhanced by improving the quality of service (QoS) of the network . In the third approach, a co‐design of both the plant controller and some of the communication network parameters is considered . Therefore, the designer can use degrees of freedom of both the control system and the communication network to improve the performance of NCS.…”

“…. Most researchers consider multiple systems connected to their controllers through a network and design a network scheduling protocol , rather than designing both the plant controller and the communication network controller. In , a pair of communication sequences that preserve observability was first identified and then an observer‐based feedback controller based on those sequences was designed.…”