GPC Algorithm Research Articles

Nonlinear generalized predictive control with virtual unmodeled dynamics decomposition compensation and data driven

In this study, a novel nonlinear generalized predictive control (NGPC) method is proposed to tackle the tracking control problem which considers a mathematical model in conjunction with the virtual unmodeled dynamics decomposition compensation technology. First, data-modeling technology is adopted to take advantage of the input and output data information from the process to decompose the virtual unmodeled dynamics into the form of posterior measurement unmodeled dynamics and an unknown increment. Then, feedforward compensator is designed. The essential difference between the proposed data modeling algorithm and existing methods is that the effect of the measurement unmodeled dynamics is eliminated by the feedforward compensator. For the increment of the virtual unmodeled dynamics, a novel estimation algorithm is established through a data driven approach; A nonlinear compensator for the unknown increment of the virtual unmodeled dynamics is designed to suppress the effect of the increment of the virtual unmodeled dynamics on the closed-loop system. Second, the developed compensator is combined with the GPC algorithm to construct a nonlinear generalized predictive controller. Theoretical analysis proves that the proposed modeling and control algorithm have bounded-input bounded-output (BIBO) stability. Finally, an experimental study is conducted, which indicates the effectiveness of the proposed algorithm.

Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm

With the increase in the world population and the rapid developments in technology, the energy demands on modern electricity grids are also rising. In order to meet these demands, power systems are increasingly using renewable energy resources (RESs) in addition to traditional fossil fuel-powered generation units and thus, the structures being implemented in electricity grids are more complex. Consequently, the planning and operation of modern power systems presents important problems, one of which is that of the optimal power flow (OPF). With the integration of RESs, which are usually intermittent in nature, the OPF becomes a more difficult problem to solve. In this study, the OPF problem was designed under different operating cases, considering thermal, wind, solar, small-hydro, and tidal energy systems and load demand uncertainties. The adaptive fitness-distance balance selection-based stochastic fractal search (AFDB-SFS) algorithm was proposed to solve this designed OPF problem. The results for the proposed approach from the experimental studies were statistically evaluated and compared with the results obtained from competitive optimization algorithms in the literature. The comparison demonstrated that the proposed AFDB-SFS algorithm was able to outperform the other algorithms in finding the optimal solution, and convergence speed to the optimal solution. According to the experimental study results, the proposed AFDB-SFS algorithm was able to optimize cost by 5.7362%, 0.0954%, 7.6244%, 0.17871%, 2.4307%, 0.12585%, 2.01729%, 1.7408%, 1.95317%, 3.5486%, 2.2007%, and 1.5203% better than the AO, GBO, GPC, HGS, HHO, RUN, TSO, LSHADE, LSHADE-EPSIN, LSHADE-CNEPSIN, LSHADE-SPACMA, and MadDE optimization algorithms in the proposed OPF problem. The source codes of the AFDB-SFS algorithm (proposed method) can be accessed at this link: https://ch.mathworks.com/matlabcentral/fileexchange/118485-afdb-sfs.

Status Quo of Numerical Simulation of Friction Stir Welding After Deformation under Big Data Analysis

Friction stir spot welding is a new type of connection technology developed on the basis of the friction stir welding process. It uses a high-speed rotating stirring head to generate heat by friction with the plate to make the welding material reach a thermoplastic softened state, forming a lap joint similar to resistance spot welding. The purpose of this article is to analyze and analyze the current situation of numerical simulation of friction stir welding after deformation under big data analysis. Algorithm mining commonly used for analysis of big data. This paper proposes to use the classic algorithm FP-growth algorithm. The FP-growth algorithm has the widest application range. It compresses the transaction database into a FP tree for processing. It also uses the Apriori algorithm, which eliminates the need to generate candidate frequent itemsets, which improves the efficiency of use. In addition, this article uses data mining technology and data fusion technology commonly used in big data analysis, and their application strengthens the analysis of the current situation of numerical simulation after friction stir welding deformation under big data analysis. Experimental research shows that the dynamic FSSW technology used in this paper simulates the numerical change after friction stir welding deformation, and the simulated data effect is better than the traditional GPC algorithm by about 20%.

Improved Algorithm for Nonlinear Programming with Inequality Constraints Based on Big Data Framework

With the development of network technology and electronic information technology, the nonlinear programming problem (NPP) with inequality constraints in the BD framework has become one of the research hotspots and one of the trends that need improvement. Due to the introduction of BD technology, there is still a lack of research on the situation where the objective function is a NPP. The purpose of this article is to use the analysis and computing power of BD technology to simulate and calulate nonlinear programming (NP) with inequality constraints and improve it. In this paper, with the help of stability conditions, matrix splitting and iteration theory, the alternate direction method is used to solve the NPP with inequality constraints, and the principle of compressed mapping is used to finally prove the convergence of the alternate direction method. Matrix splitting techniques and Gauss-Seidel iterative ideas construct an iterative algorithm for solving linear systems. The iterative format of the alternating direction method for solving NPPs with inequality constraints and the equivalent matrix format are given to prepare for further proof of the convergence of the algorithm. Experimental research shows that the NPP model with inequality constraints designed by the dynamic BP technology error correction GPC algorithm used in this article has faster response time and smaller overshoot, and the control effect is better than 20% of the traditional GPC algorithm.

Optimization of back pressure control of direct air-cooling unit based on GPC

In view of the fact that the actual back pressure is difficult to track the set value quickly and stably, this paper applies the predictive control algorithm (GPC) which can respond quickly, has good robustness and is easy to realize in the industrial production site to optimize the back pressure control. The control performance of the back pressure control system with PID, fuzzy PID and GPC is compared and analyzed through the simulation test. The simulation results show that the GPC algorithm has better dynamic performance than PID and fuzzy PID control in the case of set point disturbance and internal disturbance, and has stronger robustness in the face of model mismatch. To sum up, GPC algorithm is more suitable for the back pressure control system under complex working conditions, and the optimization control method has a certain guiding significance for the actual production site back pressure control strategy improvement.

An Adaptive Active Queue Management Based on Model Predictive Control

In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations.

The Application of GPC Algorithm on Network Predictive Control Based on Toeplitz Equation

Network latency was usually uncertain or random, and the packet loss and temporal disorders could also be attributed to a certain degree of time delay. This paper briefly described the basic principle of predictive control and deduced the predictive control algorithm based on Toeplitz equation, finally combined with the simulation example to verify the validity and superiority of the new algorithm, from the operation speed of the algorithm, the signal tracking capabilities when time delay existed, and performance for overcoming the influence of model mismatch.

A double-loop structure in the adaptive generalized predictive control algorithm for control of robot end-point contact force

Robot force control is an essential issue in robotic intelligence. There is much high uncertainty when robot end-effector contacts with the environment. Because of the environment stiffness effects on the system of the robot end-effector contact with environment, the adaptive generalized predictive control algorithm based on quantitative feedback theory is designed for robot end-point contact force system. The controller of the internal loop is designed on the foundation of QFT to control the uncertainty of the system. An adaptive GPC algorithm is used to design external loop controller to improve the performance and the robustness of the system. Two closed loops used in the design approach realize the system׳s performance and improve the robustness. The simulation results show that the algorithm of the robot end-effector contacting force control system is effective.

Study on an Improved Generalized Predictive Control Algorithm in 660MW Generating Unit

In this paper, the generalized predictive control algorithm with measurable external disturbances (FF-GPC) is deduced. On this basis, in view of the shortcoming of GPC algorithm that the overshooting is large in some degree with large lag and delay process, an improved generalized predictive control algorithm (FF-IGPC) is proposed. Based on the concept of multi-step "quasi-optimal prediction", the new algorithm puts forward an improvement for the reference trajectory. Taking 660MW coal-fired generating unit as controlled object, the FF-GPC and FF-IGPC algorithm are simulated. The results verified the feasibility of the FF-GPC algorithm, and the good control performances of the FF-IGPC algorithm.

Application of GPC Algorithm Based on Network Delay

Aiming at the problem of the network delay, this paper presents a kind of application of generalized predictive control algorithm in networked control systems. The algorithm applys future control signal predicted by MPC (model predictive control) to compensating for the delay or interruption in forward channel, and the delay in feedback channel with a predictor at the same time. The paper describes the control characteristic and discusses the stability of network control system, and verificates the algorithm's feasible and effective characteristics in networked control systems through the simulation.

Water Level Control System of Boiler Drum Based on GPC

The paper discusses a TS fuzzy model identification method for an industrial drum-type boiler plant using the fuzzy clustering approach. The fuzzy model is constructed from a set of input-output data that covers a wide operating range of the plant. Then this paper studies the application of GPC algorithm in drum level control system Based on the T-S fuzzy model, simulates the control effect of the controller in the MATLAB environment, and analyze the performance of the control system.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demonstrate the effectiveness of this proposed strategy.

AN ADAPTIVE GPC APPROACH TO LOW-FLOW ANAESTHESIA

An adaptive method is developed to control the end-tidal isoflurane concentration using a low-flow method. The isoflurane concentration is calculated based on measurements of the end-tidal isoflurane concentration and mean arterial pressure (MAP) using a model-based multivariable GPC algorithm. The control design allows explicit handling of input and output constrains, accommodates model uncertainty and takes into account possible MAP disturbances caused by surgical stimulation and blood loss. The proposed approach was tested on a complex nonlinear compartmental model of the anaesthesia system. The simulations demonstrate that the proposed control approach provides fast response, accurate set-point tracking and has good disturbance rejection behaviour. The advantages and drawbacks of low-flow over high flow anaesthesia techniques were evaluated using control-related performance criteria.

Industrial Trials of Paste Drying in Spouted Beds Under QDMC

Spouted bed has been extensively investigated by many authors for years, but the issue of controlling its operation still remains in basic developing stages. The present work consists of the application of an advanced control strategy (QDMC—Quadratic Dynamic Matrix Control) to automate the operation of a large-scale spouted bed dryer. The work on control strategies for spouted bed was motivated by the lack of references on this subject and the encouraging results obtained by Corrêa et al. (Corrêa, N.A.; Freire, J.T.; Corrêa, R.G. Improving operability of spouted beds using a simple optimizing control structure. Brazilian Journal of Chemical Engineering 1999 16 (4), 359–368; Corrêa N.A. Corrêa, R.G.; Freire, J.T. Adaptive control of paste drying in spouted bed using the GPC algorithm. Brazilian Journal of Chemical Engineering 2000 17 (4–7), 639–648; Corrêa, N.A.; Corrêa, R.G.; Freire, J.T. Self-tuning control of egg drying in spouted bed using the GPC algorithm. Drying Technology 2002 20 (4), 813–828.) for a laboratory-scale apparatus. The industrial-scale spouted bed dryer has a height of 4 m and a diameter of 66 cm. The same configuration of data acquisition unit, sensors, and interface developed for the laboratory-scale unit was employed. The dryer is capable of processing up to 20 L/h of pasty material. The powder moisture content (Ys), inferred from measurements of temperature at the exhaustion of the bed bulk, and the powder production rate (Pr), measured on-line by an electronic balance, are the controlled variables. The manipulated variables are: the paste inflow rate (W), regulated by an automatic pump, and the electrical power supply for air heating (P), regulated by a thyristor. The bed pressure drop, the airflow rate, and the information from the environment are monitored by means of an interfaced microcomputer. Experimental runs of drying up to 400 kg/day of an Al2O3 10% aqueous suspension were carried out. The results showed that the QDMC control is robust with respect to the main variables involved and is efficient in maintaining the operation of the bed under the constraints of the system. The major aim concerning the scale-up of spouted bed, which is the determination of the minimum amount of energy required to ensure spout stability, was accomplished by the constrained control strategy.

Developments in Multi-Rate Predictive Control

Much of the work on predictive based multi-rate control has been based on the GPC algorithm (Clarke et al, 1987). However academic practitioners in single rate predictive control tend to favour approaches with better stability and performance guarantees. This paper demonstrates how those approaches might be deployed in a multi-rate framework and discusses some issues that arise.

PID Based Low Frequency Sampling GPC Algorithm

Model predictive control(MPC) is widely used in the metallurgical industry in recent years. Based on PID control, a low frequency response sequence is obtained by time-domain extraction of an unit step response sequence from "a generalized controlled process"(a real process plus a PID feedback loop). The parameters of this generalized controlled process can be estimated by approaching the low frequency response sequence. The high frequency property of a process is dealt with by a PID algorithm based on the past output errors, whereas the low frequency property is controlled by a predictive control algorithm. Consequently the computational burden of predictive control is greatly lightened, and sufficient time is available for online identification of model parameters due to the low frequency sampling of MPC algorithm. The effectiveness of this method is confirmed by a simulation example for a process with a nonlinear term.

Adaptive Neural Network-Based Predictive Control for Nonlinear Dynamical Systems

Abstract In the paper, we propose a predictive control scheme using a neural network-based prediction model for nonlinear processes. To identify the system dynamics, we approximate the nonlinear function with an affine function of some of its arguments and construct a special type of prediction model using three-layered feedforward neural networks. Using some available input-output data pairs of the plant, we estimate the weights of neural networks by the Gauss-Newton based Levenberg-Marquard method. To cope with load disturbances and reduce the effect of unmodelled dynamics in the control system, we implement an on-line adaptation algorithm. Comparative simulations are given to show superiority of the proposed predictive control method to the adaptive GPC algorithm for some processes.

ニューラルネットワークを用いた非線形GPC

ニューラルネットワークを用いた非線形GPC

Exponential ARX model-based long-range predictive control strategy for power plants

Abstract For nonlinear thermal power plants whose dynamics vary with load demand, a load-dependent exponential ARX (Exp-ARX) model, which can effectively describe the nonlinear properties of the plants, is presented. The Exp-ARX model requires only off-line identification. Based on the model, a constrained multivariable generalized predictive control (CMGPC) strategy is designed and implemented in a simulation of 375 MW thermal power plants. This CMGPC algorithm does not resort to on-line parameter estimation and can more exactly predict the future outputs of the nonlinear plants, so it shows better reliability and control performance than the usual GPC algorithm.

Adaptive control of paste drying in spouted bed using the GPC algorithm

Spouted bed is an equipment reasonable investigated for drying of many and different materials ranging from granules to pastes and suspensions. This work presents an implementation and tests of an advanced control strategy (GPC) on a spouted bed dryer. Water was used as the ideal paste for tests. A microcomputer PC-486 type was used to maintain humidity and temperature set points of air in the dryer by manipulating electric power of heat exchanger and paste feed flow rate. An instrumentation was set-up with different sensors, interface and final control elements for the process. The tests were carried out with pure water as ideal paste. Stability and performance analysis of the control strategy were accomplished. A stable controller was obtained which encouraged us to investigate this control strategy in the drying of other pastes too.