Abstract
The problem of state feedback stabilization is studied for networked control systems (NCSs) subject to actuator saturation and network-induced delays. To facilitate the controller design, the NCSs are modeled as a class of discrete-time systems with bounded delays and input saturation. Based on Lyapunov-Krasovskii theory and free weighting matrix approach, the sufficient condition is derived in terms of linear matrix inequality for the asymptotic stability. Finally, the effectiveness of the developed control approach is proved through numerical examples.
Highlights
Control systems whose feedback paths are implemented by communication networks are called networked control systems (NCSs) [1, 2]
Time delays are often encountered in various practical engineering systems, such as chemical systems, power systems, and networked control systems [6,7,8]
In [17], the problem of integrated design of controller and communication sequences was addressed for networked control systems with simultaneous consideration of medium access limitations and network-induced delays, packet dropouts, and measurement quantization
Summary
Control systems whose feedback paths are implemented by communication networks are called networked control systems (NCSs) [1, 2]. Many researchers have studied stability analysis and controller design for stabilization of NCSs in the presence of network-induced delays. The stabilization problem was studied for a class of networked control systems in the discrete-time domain with random delays in [14]. The robust control problem was studied for a class of stochastic uncertain discrete-time delay systems with missing measurements in [15]. A method was proposed for the analysis and control design of linear systems in the presence of actuator saturation and disturbances in [23]. The problem of adaptive control of linear discrete-time systems with actuator saturation and unknown parameters was investigated in [31]. In [32], a novel adaptive neural network control approach was presented for a class of uncertain discrete-time nonlinear strict-feedback systems with input saturation.
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