Quantized-Feedback-Based Adaptive Event-Triggered Control of a Class of Uncertain Nonlinear Systems

Abstract

A quantized-feedback-based adaptive event-triggered tracking problem is investigated for strict-feedback nonlinear systems with unknown nonlinearities and external disturbances. All state variables are quantized through a uniform quantizer and the quantized states are only measurable for the control design. An approximation-based adaptive event-triggered control strategy using quantized states is presented. Compared with the existing recursive quantized feedback control results, the primary contributions of the proposed strategy are (1) to derive a quantized-states-based function approximation mechanism for compensating for unknown and unmatched nonlinearities and (2) to design a quantized-states-based event triggering law for the intermittent update of the control signal. A Lyapunov-based stability analysis is provided to conclude that closed-loop signals are uniformly ultimately bounded and there exists a minimum inter-event time for excluding Zeno behavior. In simulation results, it is shown that the proposed quantized-feedback-based event-triggered control law can be implemented with less than 10% of the total sample data of the existing quantized-feedback continuous control law.

Switched Fractional Order Multiagent Systems Containment Control with Event-Triggered Mechanism and Input Quantization

“…For example, reference [40] solved the problem of event-triggered fuzzy adaptive tracking control for MASs with input quantization and reduced the communication burden by combining an asymmetric hysteresis quantizer and event triggering mechanism. Based on quantized feedback control, Reference [41] studied the problem of adaptive event-triggered tracking for nonlinear systems with ex-ternal disturbances. In reference [42], the authors designed an adaptive neural control scheme for integer order uncertain nonlinear systems by combining an event-triggered scheme with input quantization technology.…”

“…(2) Compared with references [38,40], the state observer is used to estimate system states, and the RBFNN is developed to estimate uncertain parts. In comparison with references [41,43], fractional order DSC technology is used to avoid the "explosion of complexity" that can occur during traditional backstepping design processes and to obtain fractional derivatives for virtual control continuously.…”

Iterative Algorithm for Parameterization of Two-Region Piecewise Uniform Quantizer for the Laplacian Source

“…It is well known that a nonuniform quantizer model, well accommodated to the signal's amplitude dynamic and a nonuniform pdf, has lower quantization error compared to the uniform quantizer (UQ) model with an equal number of quantization levels or equal bit-rates [2,11,13,18,[20][21][22][23][24][25][26][27]. However, due to the fact that UQ is the simplest quantizer model, it has been intensively studied, for instance in [23,24,[28][29][30][31][32]. Moreover, the high complexity of nonuniform quantizers can outweigh the potential performance advantages over uniform quantizers [21].…”

Adaptive State-Quantized Control of Uncertain Lower-Triangular Nonlinear Systems with Input Delay

“…Motivated by the above-mentioned issues, state-quantized control methods have been proposed for lower-triangular nonlinear systems with Lipchitz conditions [22,23]. To approximate unknown and unmatched nonlinearities in nonlinear systems, a neural-network-based quantized feedback control result is presented in [24]. This approach has been extended to nonlinear strict-feedback systems with state delays [25] where the Lyapunov-Krasovskii functional technique is employed to remove the effects of state delays.…”

“…The measured full states are quantized by state quantizers and the quantized states are only used to design the controller. Different from the existing quantized feedback control results [22][23][24][25], our primary contribution is to establish an input delay compensation strategy using quantized states in the state-quantized control framework while ensuring the robustness against unknown nonlinearities and external disturbances. To this end, an error coordinated transformation using the auxiliary variable is derived to design the delay compensator and the neural-network-based adaptive controller.…”