State feedback control for discrete delay systems with controller failures based on average dwell-time method

A switched system approach to scheduling of networked control systems with communication constraints

“…Recently, switched Lyapunov function method was extensively used to assess asymptotic stability of discrete-time switched delay systems [16], [17], [18], where arbitrary switching signal was considered and thus every subsystem in switched systems was required to be stable. So far, few result has appeared to test asymptotic stability for discrete-time switched delay systems with unstable subsystems [19]. In [19], average dwell time technique [20] was utilized to design a stabilizing switching signal for delay switched systems with unstable subsystem, where the delay was assumed to be constant.…”

“…So far, few result has appeared to test asymptotic stability for discrete-time switched delay systems with unstable subsystems [19]. In [19], average dwell time technique [20] was utilized to design a stabilizing switching signal for delay switched systems with unstable subsystem, where the delay was assumed to be constant. But, the delays involved in system (12) are time-varying, thus, these existing results cannot be used to analyse the stability of system (12).…”

“…where Γ j (s) is defined in (19), which implies that the functional V j (k) in (16) along any state trajectory of system (15) with ω(t) = 0 has an exponential decay rate λ j (0 < λ j < 1) or increase rate λ j (λ j > 1). When λ j = 1, inequality (17) gives delay-dependent sufficient conditions for asymptotical stability with L 2 gain γ j for system (15).…”

RTRL Algorithm Based Adaptive Controller for Non-linear Multivariable Systems

“…They suggested two extended models of SISO neurocontroller to realize multivariable systems for identification and control, in which the controls are generated by training the unknown models with available inputoutput data. Different control strategies [6], [8], [11], [16] and [19] .using neural network with different training methods like feed forward architecture with back propagation learning algorithm [3] are available in literature A methodology to develop a proper model for the design of a robust controller for multivariable system is explained in [17], where the controller is In most of the training techniques, retaining the information about the infinite past are not possible, which is a drawback for the real-time applications. In feed forward neural networks, back propagation with adaptive learning rate is the most widely used gradient based algorithm.…”

ANN based adaptive controller tuned by RTRL algorithm for non-linear systems

“…Shan [1] proposed a strategy to achieve a desired altitude and position trajectories for a space craft, in the presence of periodic disturbances and uncertainties in constant parameters. Different aspects of controller design for nonlinear systems are explained in [2,3,4]. Many efficient control techniques are explained in literature, in which one of the simple and efficient technique is the pole placement technique.…”