Robust generalised predictive control with terminal output weightings

This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping.

Networked level control is a very important part in many industries. In this paper, a new method called generalized predictive control (GPC) combined with a queuing-selecting strategy is applied for level control in networked control system (NCS). A distributed model which contains an event-driven controller, and a time-driven sensor and actuator is adopted in the system. The proposed method relieve the degradation of the network control system caused by the network-induced delay . The experiment is carried out on three-coupled-tank over real network to test the proposed method, which verifies the stability and robustness of the proposed method.

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 paper, a new GPC (Generalized Predictive Control) algorithm which always guarantees the stability of a closed loop system is proposed. The feedback part of the presented predictive control is shown to be equivalent to that of RHTC (Receding Horizon Tracking Control) with a terminal state constraint. The stability of the presented control law is proved and the simplest form of the stabilizing GPC is presented, which can be widely applied to various areas. The proposed control is different from Clarke's GPC in the sense that the latter guarantees stability for an indefinitely large horizon, while the former guarantees the stability for any horizon greater than the system order.

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.

GPC FOR NON-UNIFORMLY SAMPLED SYSTEMS BASED ON THE LIFTED MODELS

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%.

Generalized predictive controller (GPC) has robust performance when faced to control non-minimum phase and open-loop unstable processes. GPC has been originally developed with linear predictor models which leads to a formulation that can be solved analytically. If a nonlinear model is used then nonlinear optimization and nonlinear modeling techniques are necessary. This paper presents a new method of multivariable GPC design for nonlinear systems. The proposed GPC design incorporates the neural network model based on radial basis function neural network and optimization methodology of cost function by Lamarckian hybrid method using evolutionary programming and Nelder-Mead’s simplex algorithm. Steps of the control law implementation and intelligent procedure are discussed. Simulation results on a nonlinear MIMO (multi-input multi-output) system are presented to show the effectiveness of the neuro-evolutionary GPC method. The new GPC design is applicable for improve the approximation of nonlinear systems with unknown interactor matrix using neural network model and an efficient control law optimization task by simplex-evolutionary hybrid methodology.

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.

Generalized predictive control (GPC) is nowadays, widely ,spread in control theory as well as in the industrial world. Generalized predictive control (GPC) belong to this family has been demonstrated as a powerful controlling process plants, In this work a polynomial approach of generalized predictive control is proposed whose aim to find optimal values for the tuning control and application of predictive control with polynomial form RST and application of induction motor and developed prediction optimal equation with cost function and resolved of the Diophantine equations this application is reserved in our work for the induction motor with example of a dynamic system and non linear multivariable with load torque.

In its present form, for a fixed plant model GPC (generalized predictive control) guarantees stability only for two specific choices of horizons in the cost function. In one instance, GPC has been shown to result in a deadbeat control law while in the other, the control law has been shown to converge to that given by the solution of the corresponding algebraic Riccati equation. However a lower bound on the costing horizon that results in a stabilizing controller is not known a priori. This paper presents sufficient conditions for stability of closed loop systems that result from implementing solutions of the finite horizon LQ problem for arbitrary fixed costing horizons. On this basis, a class of predictive control laws that ensures frozen-time stability of the closed loop system is proposed. When the plant is required to track a known reference signal that is bounded, the sufficient conditions for frozen-time stability of the closed loop are used to derive a controller structure that guarantees the tracking error to be bounded.

The development and evaluation of generalised predictive control (GPC) for online administration of muscle relaxant drugs in operating theatre is described. The necessary software was transferred into the operating theatre at two sites where successful trials were performed. Excellent regulation has been achieved, and the GPC algorithm displayed good robustness properties against disturbances such as surgical diathermy. The work was extended later to the multivariable case involving simultaneous control of muscle relaxation, and unconsciousness using blood pressure monitoring. While preliminary results are very encouraging, it is hoped to conduct more clinical trials using this system to establish its robustness especially over a wide spectrum of patients whose dynamics vary on a 1:4 ratio.

Usually speed and position of a permanent magnet brushless direct current motor (PMBLDCM) rotor is controlled in a conventional cascade structure. The inner current control loop runs at a larger bandwidth than the outer speed control loop to achieve an effective cascade control. This paper proposes a multirate based general predictive control (GPC) law performed for the conventional cascaded PI-PI scheme. Both speed and torque tracking objectives are achieved in matched and mismatched parameter case. The measured and unmeasured disturbances can be effectively rejected and the motor can run at desired speed at constant load. In addition, non-minimum phase characteristics and system constrains can effectively be handled by proposed GPC algorithm. The simulation results are provided to show that the proposed GPC strategy results in better performance than that of the conventional well tuned cascade PI- PI control strategy. The proposed controller has also been implemented to control the speed of a PMBLDCM and the results show an improvement over conventional scheme.