Dynamic logarithmic state and control quantization for continuous-time linear systems

This paper addresses logarithmic quantizers with dynamic sensitivity design for continuous-time linear systems with a quantized feedback control law. The dynamics of state quantization and control quantization sensitivities during “zoom-in”/“zoom-out” stages are proposed. Dwell times of the dynamic sensitivities are co-designed. It is shown that with the proposed algorithm, a single-input continuous-time linear system can be stabilized by quantized feedback control via adopting sensitivity varying algorithm under certain assumptions. Also, the advantage of logarithmic quantization is sustained while achieving stability. Simulation results are provided to verify the theoretical analysis.

Quantized Feedback Control of Active Suspension Systems Based on Event Trigger

As the deviation error will accumulate during the data acquisition and transcoding process of an active suspension system, this paper presents a sliding-mode-based quantized feedback control method. The aim of the controller is to improve the vertical performance of vehicles in the presence of external interferences. A 7-DOF suspension model with nonlinear springs and actuator dynamics is built for the control purpose. Firstly, a static quantizer on the uplink channel and a dynamic quantizer on the downlink channel are considered in the sliding mode controller to reduce the cumulative error and suppress the sprung mass motions. Secondly, an event trigger mechanism is introduced in the controller design process to reduce energy consumption and operation frequency of the actuator. The overall stability of the designed controller is proved by the Lyapunov functions. Finally, numerical simulations are carried out to evaluate the efficacy of the proposed controller. Different quantitative and trigger conditions are discussed, and the random road excitation is considered as the external disturbance input. The results of the control method indicate that the designed controller can improve the riding comfort with little loss of handling stability compared with the passive system. In addition, the trigger mechanism can improve the working efficiency of actuators effectively.

High-Precision Tracking of Periodic Signals: a Macro-Micro Approach with Quantized Feedback

<div>This paper proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with optical encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below ?1 ?m when subject to a sensor quantization level of 5 ?m.</div>

High-Precision Tracking of Periodic Signals: a Macro-Micro Approach with Quantized Feedback

<div>This paper proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with optical encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below ?1 ?m when subject to a sensor quantization level of 5 ?m.</div>