Alleviation of Delay in Tele-Surgical Operations using Markov Approach based Smith Predictor

The acceptance of tele-robotics and teleoperations through networked control system (NCS) is increasing day-by-day. NCS involves the feedback control loop system wherein the control components such as actuators and sensors are controlled and allowed to share their feedback over real time network with distributed users spread geographically. The performance and surgical complications majorly depend upon time delay, packet dropout and jitter induced in the system. The delay of data packet to the receiving side not only causes instability but also affect the performance of the system. In this article, author designed and simulate the functionality of a model-based Smith predictive controller. The model and randomized error estimations are employed through Markov approach and Kalman techniques. The simulation results show a delay of 49.926ms from master controller to slave controller and 79.497ms of delay from sensor to controller results to a total delay of 129.423ms. This reduced delay improve the surgical accuracy and eliminate the risk factors to criticality of patients’ health.

SUPERVISED TUNING OF A CHILLER WITH A COLD PHASE-CHANGE MATERIAL USING AN RSM-SMITH PREDICTOR

The proposed work is focused on the tuning of the latent-heat storage of a PCM with MWCNT encapsulation for a chiller system in the milk-pasteurization process using an RSM-based predictive Smith controller. The PCM was synthesized using sodium polyacrylate and MWCNT particles, encapsulated in spherical balls stored in the IBT (ice bank tank of the chiller unit). Experimental work is conducted on the heat-transfer characteristics of the chiller unit with servo-operated flow-control valves based on the central composite design of experiments. The system is cascaded using a PID controller with a Smith predictor to stabilize the latent temperature of milk cooling in the chiller system. The system is considered to be a first-order transformation with the rise and settling time. The plant model is obtained, using the response-surface method based on the transfer function to minimize the error derivatives of the chiller system. Results of the proposed method show a robust performance in keeping the PCM temperatures and bacterial-growth value stabilized, allowing a less drastic control compared to the cascade-model predictive control.

Seeing the future: predictive control in neural models of ocular accommodation

Ocular accommodation is the process of adjusting the eye's crystalline lens so as to bring the retinal image into sharp focus. The major stimulus to accommodation is therefore retinal defocus, and in essence, the job of accommodative control is to send a signal to the ciliary muscle which will minimise the magnitude of defocus. In this paper, we first provide a tutorial introduction to control theory to aid vision scientists without this background. We then present a unified model of accommodative control that explains properties of the accommodative response for a wide range of accommodative stimuli. Following previous work, we conclude that most aspects of accommodation are well explained by dual integral control, with a "fast" or "phasic" integrator enabling response to rapid changes in demand, which hands over control to a "slow" or "tonic" integrator which maintains the response to steady demand. Control is complicated by the sensorimotor latencies within the system, which delay both information about defocus and the accommodation changes made in response, and by the sluggish response of the motor plant. These can be overcome by incorporating a Smith predictor, whereby the system predicts the delayed sensory consequences of its own motor actions. For the first time, we show that critically-damped dual integral control with a Smith predictor accounts for adaptation effects as well as for the gain and phase for sinusoidal oscillations in demand. In addition, we propose a novel proportional-control signal to account for the power spectrum of accommodative microfluctuations during steady fixation, which may be important in hunting for optimal focus, and for the nonlinear resonance observed for low-amplitude, high-frequency input. Complete Matlab/Simulink code implementing the model is provided at https://doi.org/10.25405/data.ncl.14945550

Research on Optimization of Fuzzy Network Control System Based on New Smith Predictive Time Delay Compensation

Aiming at the problem of network delay when the network control system transmits data, this paper adopts a new type of fuzzy control method of Smith predictor to compensate for the delay. In actual application scenarios, it is difficult to accurately match the Smith prediction model with the actual model. At the same time, the quantization factor and scale factor in the fuzzy PID controller are too dependent on experience, which makes the system’s adaptability to actual working conditions very poor. In this paper, genetic algorithm is used to optimizes fuzzy PID. According to the simulation results, when the Smith predictive model does not match the actual model, the steady-state performance and dynamic performance of the system under the fuzzy PID control optimized by the genetic algorithm are improved.

Smith predictor compensation and fuzzy incremental control for delay of space docking hardware-in-the-loop simulation system

Hardware-in-the-loop (HIL) simulation for space manipulator docking is an important means to simulate real space docking on the ground. The HIL simulation system in this paper utilizes the contact force measured by force sensor to calculate the dynamics of the mechanisms, and the docking process is simulated by the parallel robot. The measurement delay of force sensor and dynamic response delay of the parallel robot are inevitable, which not only affect the accuracy of simulation but also lead to the instability of the HIL simulation system. The traditional first-order phase compensation is the most commonly used force sensor compensator; but when the force changes with a high frequency, its compensation effect becomes bad, which will lead to the divergence of the HIL simulation system. Most control methods of the parallel robot are based on the model of the parallel robot, but the forces of the parallel robot are complex during the docking process, and the system parameters, motion frequency, and dynamic response characteristics are time-varying; thus, it is difficult to design the controller based on the model. In this paper, the Smith predictor compensation (SPC) method and fuzzy incremental control (FIC) method are utilized to decrease the delays of the force sensor and parallel robot, respectively. The effectiveness of the Smith predictor compensation and fuzzy incremental control method in reducing the delay of the HIL system and in improving the stability of the system is verified by simulation and experiment; compared with the traditional first-order phase compensation and proportional-integral-differential control methods, the advantages of the proposed methods are illustrated. The research in this paper provides an important technical means for accurately simulating the real docking process.

Disturbance-Improved Model-Free Adaptive Prediction Control for Discrete-Time Nonlinear Systems with Time Delay

This study proposes a Disturbance-improved Model-free Adaptive Prediction Control (DMFAPC) algorithm for a discrete-time nonlinear system with time delay and disturbance. The algorithm is shown to have good robustness. On the one hand, the Smith predictor is used to predict the output at a future time to eliminate the time delay in the system; on the other hand, an attenuation factor is introduced at the input to effectively eliminate the measurement disturbance. The proposed algorithm is a data-driven control algorithm that does not require the model information of the controlled system; it only requires the input and output data. The convergence of the DMFAPC is analyzed. Simulation results confirm the effectiveness of this algorithm.