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

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.

During the spinning forming process of chitosan and collagen absorbable suture, the diameter is uneven. To solve this problem, this article provides a diameter control system of absorbable suture which based on generalized predictive control (GPC) algorithm. The system consists of the diameter detection module based on linear CCD, the control module based on STM32, the actuator of reverse series type double hydraulic cylinder. The control algorithm adopts self-adaptive generalized predictive control (GPC) algorithm which successes in realizing closed loop control of large time delay system in the process of absorbable suture forming. The results of the experiment show that the control system has good rapidity and good stability, the control accuracy reached 1%.

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.

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.

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.

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.

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.

S0ingle-input/single-output generalised predictive control (GPC) has been applied successfully to muscle-relaxant anaesthesia in both simulations and clinical trials. This work was extended later to the multivariable case involving simultaneous control of muscle relaxation through EMG measurements, and unconciousness using blood-pressure monitoring in a series of simulation studies. To achieve even higher degrees of automation, a fault detection isolation and accommodation layer has been superimposed on top of the multivariable GPC strategy. The performance of the overall supervisory control scheme has been evaluated in a series of simulation runs on a work station. The results are encouraging in the sense that the algorithm has proved very effective in detecting, diagnosing and eventually compensating for various simulated faults normally encountered in the operating theatre. The experiments used two versions of GPC: the multivariable GPC (using a P-canonical form for the process model) and GPC with feedforward, and were conducted in noise-free and noise-corrupted situations since operating theatres can be electrically dirty environments. The study forms the basis for an experiment protocol which will be followed in clinical trials.

This paper provides the design of a Model Predictive Controller (MPC) using the Generalized Predictive Control (GPC) algorithm applied to grid-connected inverters with LCL filter. The main objective is to obtain a current controller that ensures reference tracking capacity for sinusoidal references and rejection of specific harmonic disturbances produced by grid voltages. The closed-loop system with the GPC controller is evaluated through time and frequency response analysis.

This particular study concerns the application of the generalised predictive control (GPC) algorithm using the concept of prespecified set-points in clinical anaesthesia. The study reveals that the SISO GPC version behaves very well under these circumstances and displays good robustness properties when mismatch conditions between the actual and assumed set-point profiles are considered. Moreover, when the scheme is extended to enhance a multivariable structure involving simultaneous control of muscle relaxation together with unconsciousness using blood pressure measurements, the levels of interaction from one channel to the other one can be considerably reduced without great compromise on other characteristics such as transient response and speed making therefore this whole concept of set-point prespecification an attractive candidate for future clinical trials.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Generalized predictive control (GPC) algorithm is proposed to apply to a distributed axial dispersion chemical reactor. For a class of nonlinear distributed parameter systems described by parabolic partial differential equations (PDEs), Principal Component Analysis (PCA) method is utilized to extract the basis functions of dominant space, and the time-space decomposition is derived in terms of these basis functions to obtain the time domain outputs for system modeling. The input stimulation and time-domain outputs are used to construct the time domain decoupling ARX model, its parameters are identified by the recursive least squares (RLS) algorithm. Based on the decoupling ARX model, the MIMO system can be simplified as multiple SISO system, GPC strategy is then directed used to design the DPS controller. Simulation results show the efficiency of the proposed system modeling and control scheme.

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.

An optimal predictive control algorithm is introduced for the control of linear and nonlinear discrete-time multivariable systems. The controller is specified in a ‘restricted structure’ form involving a set of given linear transfer-functions and a set of gains that minimise a Generalised Predictive Control (GPC) cost-index. The set of functions can be chosen as proportional, integral and derivative terms, however, a wide range of controller structures is possible. This is referred to as Restricted-Structure GPC control. The multi-step predictive control cost-function is novel, since it includes weightings on the ‘low-order’ controller gains and the rate of change of gains. This considerably improves the numerical computations ensuring critical inverse computations cannot lead to a singular matrix. It also provides the option of adding soft or hard constraints on the controller gains which provides additional flexibility for control design. The ability to include a plant model that can include a general nonlinear operator is also new for restricted structure control solutions. The low-order controller provides a potential improvement in robustness, since it is often less sensitive to plant uncertainties. The simple controller structure also enables relatively unskilled staff to retune the system using familiar tuning terms, and provides a potentially simpler QP problem for the constrained case.

In this paper, an optimal Smith-predictor design based on a generalized predictive control (GPC) approach is developed. The basic idea is to back-calculate the Smith control parameters based on the GPC control law, which has been designed to minimize a cost function. The design has turned out to be very simple to use. With a second-order model, the primary controller is simply a PID control. With a first-order model, it is a PI controller. The relationship between the control parameters obtained via the GPC approach and a pole placement approach is derived so that the latter approach may be used to derive initial GPC weights for further fine-tuning. Simulation and a real-time experiment illustrate the performance of the thus-designed optimal Smith control system.