A diameter control system of absorbable suture based on generalized predictive control algorithm

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

In order to improve the dependability and security of the lever control system, we introduced the generalized predictive control (GPC) algorithm. We used MATLAB/Simulink program development tools to control the double capacity water tank. At first, we used the water tank level control device as the object and set up a mathematical model of the control system. Then we put forward the generalized predictive control algorithm based on the model. The simulation results show that the presented algorithm has a good dynamic response performance, fast-track settings, and a preferable control effect. Experimental results show that the improved methods are reasonable and practicable.

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. The adaptive control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Controller performance was evaluated with a measure of output disturbance rejection and an input sensitivity transfer function. The results suggest that disturbances entering the cavity flow are colocated with the control input at the cavity leading edge. In that case, only tonal components of the cavity wall-pressure fluctuations can be suppressed and arbitrary broadband pressure reduction is not possible. In the control-algorithm development, the cavity dynamics are treated as linear and time invariant (LTI) for a fixed Mach number. The experimental results lend support this treatment.

Reliable control of the oxygen regulator is essential to aiding high security and comfort for breathing mask users in the aircraft. However, test methods for regulator design have the disadvantages of high cost and long cycle. Herein, the mathematical model of an Electronic Oxygen Regulator (EOR) was established, where Generalized Predictive Control (GPC) algorithm was applied to the oxygen supply process. Simulation was made on MATLAB (Matrix Laboratory) platform and the results were thoroughly investigated by considering three kinds of interference: changing breath state, time delay and gas leakage. Results suggest that, the GPC-based oxygen regulator was more effective in improving the response speed and lowering breathing resistance than Proportion Integration Differentiation (PID) algorithm, which may be a plausible reference for controller design of the EOR.

Modeling and control of PEMFC based on least squares support vector machines

An embedded implementation of the Generalized Predictive Control algorithm applied to automotive active suspension systems

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional (2D) general predictive iterative learning control (2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control (ILC) algorithm for a 2D system and designed in the generalized predictive control (GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

In this work, a practical event-based framework for multivariable processes is introduced. The developed control algorithm is based on a Generalized Predictive Control (GPC) algorithm suitable for MIMO systems. The most important advantage of the proposed control scheme is the adaptability of control actions to process disturbances and process interactions. Moreover, event-based approach allows prioritizing selected control variables, paying more attention to important variables (often related to security or costs) while neglecting non-critical ones. This is possible through adjusting the sensor deadband value, making feasible a tradeoff between control effort and control performance. Using this approach, it is possible to keep less relevant variables within the established limits, focusing the control system attention on those critical for process operation. The analyzed event-based control technique for multivariable systems is tested through a simulation study and considering an industrial stirred tank reactor process. The obtained results show several benefits regarding event-based control application, keeping all properties of event-based approach as well as model predictive control ones.

Generalized predictive control (GPC) algorithm has been applied to all kinds of industry control systems. But systemic and effective method for nonlinear system has not been found.To this problem,this paper integrates the characteristics of PID technology and GPC,present a PID generalized predicitive control algorithm for a class of nonlinear system,and improves the control quality of the system.

In this paper, a practical event-based framework for multivariable processes is introduced. The developed control algorithm is based on a Generalized Predictive Control (GPC) algorithm suitable for multiple-input multiple-output systems. The most important advantage of the proposed control scheme is the adaptability of control actions to process disturbances and process interactions. The application of the developed control system allows us to reduce the amount of messages exchanged between control system agents, what is beneficial for communication burden. Moreover, event-based approach allows us prioritizing the selected control variables, paying more attention to important variables (often related to security or costs), while neglecting the noncritical ones. This is possible through adjusting the sensor deadband value, making feasible a tradeoff between control effort and control performance. Using this approach, it is possible to keep less relevant variables within the established limits, focusing the control system attention on those critical to process operation. The analyzed event-based control technique for multivariable systems is tested through a simulation study and real experiments. The obtained results show several benefits regarding event-based control application, keeping all properties of event-based approach as well as model predictive control ones.

A kind of soft-sensing method of gauge of strip based on fast generalized predictive control(GPC) algorithm is presented. Based on in-depth studying simplified calculating principle of fast GPC algorithm, and using fast algorithm’s characteristics of small calculation amount and fast calculating speed the method establishes the real-time online soft-sensing model of the strip gauge. Combining GPC’s advantages of multi-steps prediction, rolling horizon optimization and feedback compensation etc, it can better solves problems of measurement delay caused by gage installation location and measurement errors caused by tension time-varying and equipment tremor. The test results show that the method can real-time, on-line measures the exit thickness of rolling strip accurately, and provides reliable guarantee for optimized control of shape and gauge. It can also be used in the key parameters measurement of other complex industrial process.

The predictive inelastic state control (PISC) algorithm is proposed by incorporating an elastic-based predictive state control algorithm with inelastic structural analysis. Force analogy method based on initial stiffness is used in the analysis to simplify the numerical computation. This proposed inelastic control algorithm compensates for the time delay that occurs in practical application of structural control by predicting the inelastic structural response over a period equal to the time delay, and substituting the predicted structural response in the expression of an ideal control force without considering time delays. A one-storey moment-resisting frame with different inelastic characteristics is analyzed using this algorithm. Numerically simulated results show that the proposed control algorithm is feasible for various types of inelastic structures. While the control efficiency deteriorates with increase in time delay magnitude, PISC shows good performance within a certain range of time delays. More importantly, it is found that a control system with smaller time delay magnitude does not guarantee better performance than that with larger time delay magnitude. Finally, numerical simulation on a six-storey moment-resisting frame is performed to demonstrate that PISC gives good control results in a practical inelastic structure. Copyright © 2004 John Wiley & Sons, Ltd.

Servo applications often require fast response inner current loop in order to ensure high dynamic performance of the whole system. Predictive control algorithm can achieve fast dynamic response while avoiding overshoot. However, there is an inherent defect that it relies on the accurate mathematical model of the controlled object. An incremental predictive current control algorithm adopting an improved PWM updating strategy was presented. The relationship between system stability and parameter mismatch was analyzed using root locus method. Simulation and experimental results demonstrate that the proposed incremental predictive current control algorithm shows well dynamic performance and the improved updating strategy achieves significantly increased robustness to parameter mismatch, verifying the feasibility of the methods presented.

An Adaptive, State-Space Based GPC with a Two-Degrees-of-Freedom Controller Structure

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.