Controller synthesis for networked control systems

Cloosterman, Mbg Marieke; Hetel, Laurentiu; Wouw, Nathan van de; Heemels, W.P.M.H.; Daafouz, Jamal; Nijmeijer, Henk

doi:10.1016/j.automatica.2010.06.017

Cited by 298 publications

(241 citation statements)

References 33 publications

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“…By using Remark 7, we can achieve the upper bound of sampling intervals up to T M = 1.891 s. While the result in [17] is 0.014 s. It has shown that the approach in this paper can provide less conservative results than [17] for this example. 3 shows the difference between the previous valuẽ x(s k ) and the most recent valuex(s k+1 ) of the output signals from transmission deadband 1 for δ 1 = 0.1 and δ 2 = 0.1.…”

Section: Examplementioning

confidence: 84%

Signal difference‐based deadband control for networked control systems with variable sampling intervals

Liu

Che

Chu

2014

IET Control Theory & Applications

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This study investigates a signal difference-based deadband control approach as a solution to reduce data transmission in networked control systems (NCSs). A new modelling method for the NCSs with time-varying delays, time-varying sampling intervals and signal transmission deadbands is presented. The Lyapunov functional with discontinuity is exploited, which takes full advantages of the NCSs' characteristic information including the bounds of network delay (BND), the bounds of sampling interval (BSI) and the bounds of transmission deadband (BTD). In addition, it has been shown that Lyapunov functional decreases at the jump instants. Furthermore, the new stability analysis and stabilisation conditions for the NCSs are proposed, which describes the relationship of BND, BSI, BTD and the system stability. Three examples are used to demonstrate the effectiveness of the proposed methods. The simulation results have shown that the proposed approach could guarantee the system asymptotically stable and effectively reduce the data transmission in network channel.

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Section: Examplementioning

confidence: 84%

Signal difference‐based deadband control for networked control systems with variable sampling intervals

Liu

Che

Chu

2014

IET Control Theory & Applications

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“…In that case a feedback scheduler is assumed to compute on-line new control intervals according to the Central Processing Unit (CPU) load and system's state. Different control approaches have been considered to design varying sampling controllers, e.g., see (Cloosterman et al (2010)) for a survey. Among others, in (Hu and Michel (2000)) a stability analysis of the effect of varying sampling is provided for nonlinear systems, and in (Olm, Ramos, and Costa-Castelló (2011)) an H ∞ approach is used to conclude on the allowed sampling intervals that keep stability of a digital repetitive control scheme.…”

Section: Varying Sampling Approachesmentioning

confidence: 99%

Real-time control systems: feedback, scheduling and robustness

Simon

Seuret

Sename

2017

International Journal of Systems Science

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The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

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“…In this case, the number of generators of the set AB increases exponentially with h, that is L = 0 i=−h L i L i , and hence existing control schemes, such as [1,[5][6][7][8][9][10][11][12][13][14] are not computationally tractable for large delays. In [15], to make the on-line computation of the control update independent of h, the set-valued map (2) was replaced bŷ…”

Section: Therefore the Ddi (3) Is Es(x) Withmentioning

confidence: 99%

“…In Table 1 the dimension of the LMI that needs to be solved to stabilise the linear DDI (1) is provided for a selection of control solutions; the off-line synthesis methods presented in [5,7], the on-line optimisation-based methods presented in [11,15] and for the method presented in this paper. To obtain a fair comparison, the constraints were not taken into account for the numbers shown in Table 1.…”

Section: Illustrative Examplementioning

confidence: 99%

“…This yields a standard but higher-dimensional system without delay, and the classical stabilisation techniques apply to this system. For example, using this technique statefeedback controllers were obtained for uncertain systems with delay in [5][6][7], for uncertain systems with time-varying delay in [8,9] and for uncertain singular systems with time-varying delay in [10]. Furthermore, control strategies that can handle constraints were developed for uncertain systems with delay in [11,12] and for uncertain systems with time-varying delay in [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stabilisation of linear delay difference inclusions via time-varying control Lyapunov functions

Gielen

Lazăr

2012

IET Control Theory Appl.

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Abstract:The stabilisation of linear delay difference inclusions is often complicated by computational issues and the presence of constraints. In this study, to solve this problem, a receding horizon control scheme is proposed based on the Razumikhin approach and time-varying control Lyapunov functions. By allowing the control Lyapunov function to be time varying, the computational advantages of the Razumikhin approach can be exploited and at the same time the conservatism associated with this approach is avoided. Thus, a control scheme is obtained which takes constraints into account and requires solving on-line a low-dimensional semi-definite programming problem. The effectiveness of the proposed results is illustrated via an example that also shows the computational limitations of existing control strategies.

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Controller synthesis for networked control systems

Cited by 298 publications

References 33 publications

Signal difference‐based deadband control for networked control systems with variable sampling intervals

Signal difference‐based deadband control for networked control systems with variable sampling intervals

Real-time control systems: feedback, scheduling and robustness

Stabilisation of linear delay difference inclusions via time-varying control Lyapunov functions

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