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.

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Robust predictive Control for Networked Control Systems with Access Constraints

Based on the rolling horizon optimization strategy, the networked robust predictive control with medium access constraints and packet loss is studied. Firstly, considering the influence of network factors such as medium access constraints and packet loss, Markov jump rule and Bernoulli independent and identically distributed process are used to transform the network problem into the robust problem of networked control system. According to the established Markov jump system model and stability analysis, a robust predictive controller for networked control systems is designed by using linear matrix inequality (LMI) method, which makes the system asymptotically stable. Finally, a numerical example is given to verify the effectiveness of the proposed control method.Networked control system; medium access constraints; packet loss; robust predictive controller.

Implementation of PI and MPC-Based Speed Controllers for a Drive with Elastic Coupling on a PLC Controller

This paper presents some of the issues related to the implementation of advanced control structures (PI controller with additional feedback, Model Predictive Controller) for drives with elastic coupling on a programmable logic controller (PLC). The predominant solutions to electric drive control include the use of rapid prototyping cards, signal processors or programmable matrices. Originally, PLC controllers were used to automate sequential processes, but for several years now, a trend related to their implementation for advanced control objects can be observed. This is mainly due to their compact design, immunity to disturbances and standard programming languages. The following chapters of the paper present the mathematical model of the drive and describe the implementation of the proposed control structures. A PI controller with additional feedback loops and a predictive controller are taken into consideration. Their impact on the CPU load was analysed, and the work was summarised by a comprehensive experimental study. The presented results confirm that it is possible to implement advanced control structures on a PLC controller for drives with elastic coupling while maintaining a sufficiently low load on its CPU.

SLIDING MODE BASED PREDICTIVE CONTROLLER OF A SPHEROIDAL UNDERWATER VEHICLE

The following key points are made after reading this research paper: 1) The approach taken in the paper looks interesting and innovative; 2) The problem and solution are well motivated; 3) The design of proposed hybrid controller (Sliding Mode & Model Predictive Controller) is a new and robust technique. Both qualitative and quantitative analysis and stability analysis are discussed in this paper; and 4) The proposed flowchart for MPC looks interesting. MPC tuning is given in a standard and systematic way.

Predictive Course Control and Guidance of Autonomous Unmanned Sailboat Based on Efficient Sampled Gaussian Process

In view of the vulnerability of ocean unmanned sailboats to the large lateral velocities due to wind and waves during navigation, this paper proposes a Gaussian Process Model Predictive Control (GPMPC) method based on data-driven learning technique to improve the navigation tracking accuracy of unmanned sailboats. The feature model of the sailing course change subject to the wind and waves is learned from the efficient sampling data. It is then combined with the model predictive control to form the course controller. To reduce the influence of wind and waves disturbances, an adaptive weight term is designed in the object function to improve the tracking accuracy of the model predictive control. The guidance commands received by the model predictive controller take into account the path deviation caused by the current and lateral motion of the ship. The results show that GPMPC has the advantages of fast response time and less overshoot; the unmanned sailboat can better achieve waypoint tracking by learning navigation data.

Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.