Generalized Predictive Control Research Articles

Fuzzy Generalized Predictive Control for Nonlinear Brushless Direct Current Motor

Abstract In the study, a novel fuzzy generalized predictive control (GPC) scheme is proposed for the stability control of nonlinear brushless DC motor (BLDCM). First, the fuzzy predictive model of BLDCM is presented via Takagi–Sugeno fuzzy model. Then, based on the controlled autoregressive moving average (CARMA) model transformed by Takagi–Sugeno fuzzy model of BLDCM, a new fuzzy GPC scheme for the nonlinear BLDCM is designed by combining fuzzy techniques and GPC theory, and the rigorous mathematical derivation is given. Finally, numerical simulations are implemented to verify the effectiveness and superiority of the proposed scheme. It also provides reference for other nonlinear even chaos control in actual project.

Temperature Control for Nano-Scale Films by Spatially-Separated Atomic Layer Deposition Based on Generalized Predictive Control

This paper established a closed-loop temperature control system for a spatially-separated atomic layer deposition (S-ALD) reactor using generalized predictive control (GPC) algorithm. The GPC-based closed-loop control system rapidly and precisely stabilized the reactor temperature in the presence of thermal field disturbances. Compared with the proportion-integration differentiation (PID) control commonly used for S-ALD, the closed-loop GPC system attenuated reaction temperature oscillations when producing two-element nano-scale thin films. Furthermore, the proposed GPC system demonstrated the superiority in multi-element nano-laminates depositing efficiency by reducing the settling time with a substrate moving between different reactors. Electrical and optical properties of films verified the feasibility of the proposed GPC system. Finally, experimental results presented that the microstructure of the deposited ALD nanometer thin film was improved with the developed GPC system, compared with the PID strategy.

Generalized Predictive Control of Temperature on an Atomic Layer Deposition Reactor

This brief establishes a closed-loop control system for a radiant heating atomic layer deposition (ALD) reactor using generalized predictive control (GPC). The GPC-based closed-loop control system can rapidly and precisely stabilize the temperature in the presence of external disturbances. Compared with the conventional open-loop control and the proportional–integral–differential control, the closed-loop GPC system enhances the processing efficiency between every ALD run by substantially reducing settling time with impact by external disturbances. The attenuated temperature fluctuation leads to more uniform thin-film morphologies that fulfill the technical requirements of ALD processes.

The PV MPPT Control Strategy Based on Generalized Predictive Control

The excursion of maximum power point always exists due to nonlinear and time-varying characteristics of photovoltaic (PV) generation. To solve this problem, the maximum power point tracking (MPPT) control strategy based on generalized predictive control (GPC) is put forward. Firstly, the theory and feature of PV cell is analyzed, and the MPPT control model is constituted based on Boost circuit. Then the MPPT algorithm based on GPC is illustrated from such sections as prediction model, rolling optimization, online identification and feedback correction, the algorithm steps are given as well. Besides, the MPPT simulations are conducted by the methods based on GPC and PID in comparison. The comparison results testify that GPC algorithm is faster and more accurate to track the maximum power point. GPC algorithm has the characteristics like stability and robustness, and can be applied to solar generation system.

Generalized Predictive Control Applied to Motor Drive Axis

The topic of this article is the adaptive generalized predictive control (GPC) applied to the control of the speed of a digital axis. The system is used in CNC machine tools. Usually, the control of digital axes must obey quickly and effectively to changes in the input variable and continuously adapting with the machining conditions, and oppose the influence of external disturbances. However, The Online Recursive Least Squares with Forgetting factor was adopted to estimate inreal-time the system parameters and adjust instantaneously GPC controller parameters based on a modeling CARIMA (Controlled Auto-Regressive Integrated Moving Average). The results of this control architecture have achieved the desired local performance with good rejection of load disturbances and a good robustness for the parametric variations due to operating point changes.

Time-delay compensation method for networked control system based on time-delay prediction and implicit PIGPC

A network time-delay compensation method based on time-delay prediction and implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed. The least squares support vector machine (LSSVM) is used to predict the current time-delay, the parameters of the least squares support vector machine are optimized by particle swarm optimization (PSO) algorithm, and the predicted time-delay is used instead of the actual time-delay as the parameters of the network time-delay compensation controller. In order to improve the compensation effect of implicit generalized predictive controller (GPC), this paper puts forward an implicit generalized predictive control algorithm with proportional-integral-based (PI) structure and designs the controller based on implicit PIGPC. Through the simulation results, the effectiveness of this design in the paper is verified.

Identification and optimization for hydraulic roll gap control in strip rolling mill

In order to improve the control performance of strip rolling mill, theoretical model of the hydraulic gap control (HGC) system was established. HGC system offline identification scheme was designed for a tandem cold strip mill, the system model parameters were identified by ARX model, and the identified model was verified. Taking the offline identified parameters as the initial values, online identification using recursive least square was carried out with model parameters changing. For the purpose of improving system robustness and decreasing the sensitivity due to model errors, the HGC system based on generalized predictive control (GPC) was designed, and simulation experiments for traditional controller and GPC controller were conducted. The results show that both controllers acquire good control effect with model matching. When the model mismatches, for the traditional controller, the overshot will increase to 76.7% and the rising time will increase to 165.7 ms, which cannot be accepted by HGC system; for the GPC controller, the overshot is less than 8.5%, and the rising time is less than 26 ms in any case.

Multivariable Generalized Predictive Control Design: Application for Couple of DC Motors

The paper presents method for design stable generalized predictive controller in the frequency domain and its application on model of couple DC motors. The frequency domain predictive methods are easier for application and can improve performance of control circuit. This way the presented predictive control method can be used in practice on real devices like FPGA without needs of complicated numerical calculations. Numerical results based on identification of real process are given to illustrate the effectiveness of the proposed method

DESIGN OF PREDICTIVE CONTROLLER FOR SMOOTH SET POINT TRACKING FOR FAST DYNAMIC SYSTEM

Model Predictive Control is generally used for slow dynamic system. Here efforts are made to implement MPC controller for Fast dynamic System. Speed control of DC motor is taken as fast dynamic system for which the MPC controller would be implemented. To control the speed of the DC motor Generalized Predictive Control (GPC) algorithm is used. In this paper, ARIX model based GPC control is implemented in 2-DOF structure. Transfer function of the DC motor is derived using LABVIEW and system identification tool of MATLAB. From the response of the system, it can be seen that the GPC has improved the performance of the system rather than PID control algorithm from disturbance rejection point of view.

Adaptive predictive control based on adaptive neuro-fuzzy inference system for a class of nonlinear industrial processes

In present paper, a novel adaptive predictive control method is proposed for a class of nonlinear systems via adaptive neuro-fuzzy inference system (ANFIS). In the proposed method, a kind of nonlinear generalized predictive controller (GPC) is utilized where the model is achieved using an adaptive intelligent system. The dynamics of the system are classified into two linear and nonlinear parts. Linear part is approximated using least squares estimation technique, and the nonlinear part is identified using an ANFIS-based identifier. Therefore, the future behavior of the system is predicted based on the intelligent identification method in order to be used for designing the controller. The controller is updated based on these two identified models of the system's parts. The proposed method has the ability of real time implementation, and also there is no need of pre-training phase of the network. The controller performance is investigated by carrying out different simulations on two nonlinear process benchmark problems. For this purpose, a liquid level control system and a continuous stirred tank reactor (CSTR) are considered. Simulation results show the fidelity of proposed method for unknown nonlinear systems in presence of noisy and disturbed conditions.

Independent Model Generalized Predictive Controller Design for Antilock Braking System

Wheel slip control is a significant research topics in the field of car stability. Model predictive control is one of the most advanced controller which has received great attention and application in industries. In this paper independent model generalize predictive control (IMGPC) is introduced for antilock braking system. This controller is implemented on a linear model of anti-lock braking system, and through the numerical simulation, it is demonstrated that it can control the system in presence of sever noise and disturbances. The simulation results show that the proposed controller has better performance in comparison with other conventional linear controllers. General Terms Predictive control, Anti-lock braking system .

Experimental verification of a GPC-LPV method with RLS and P1-TS fuzzy-based estimation for limiting the transient and residual vibration of a crane system

The paper deals with the problem of reducing the residual vibration and limiting the transient oscillations of a flexible and underactuated system with respect to the variation of operating conditions. The comparative study of generalized predictive control (GPC) and fuzzy scheduling scheme developed based on the P1-TS fuzzy theory, local pole placement method and interval analysis of closed-loop system polynomial coefficients is addressed to the problem of flexible crane control. The two alternatives of a GPC-based method are proposed that enable to realize this technique either with or without a sensor of payload deflection. The first control technique is based on the recursive least squares (RLS) method applied to on-line estimate the parameters of a linear parameter varying (LPV) model of a crane dynamic system. The second GPC-based approach is based on a payload deflection feedback estimated using a pendulum model with the parameters interpolated using the P1-TS fuzzy system. Feasibility and applicability of the developed methods were confirmed through experimental verification performed on a laboratory scaled overhead crane.

A Nonlinear System of Generalized Predictive Control

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 predictive control algorithm for a class of nonlinear system, and improves the control quality of the system. DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7215

Generalized Predictive Control for superheated steam temperature regulation in a supercritical coal-fired power plant

The design and implementation of a Generalized Predictive Control (GPC) strategy for the superheated steam temperature regulation in a supercritical (SC) coal-fired power plant is presented. A Controlled Auto-Regressive MovingAverage (CARMA) model of the plant is derived from using the experimental data to approximately predict the plant's future behavior. This model is required by the GPC algorithm to calculate the future control inputs. A new GPC controller is designed and its performance is tested through extensive simulation studies. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the GPC controller is found to be more stable. The stable steam temperature leads to more efficient plant operation and energy saving, as demonstrated by the simulation results. Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.

Optimization of Generalized Predictive Control (GPC) Tuning Parameters By Response Surface Methodology (RSM)

Response Surface Methodology(RSM) was successfully applied to a process simulator for optimization of Generalized Predictive Control(GPC) tuning parameters. Wireless experimental input/output data obtained from process simulator. GPC algorithm which is written in MATLAB is utilized to wireless temperature control experiments achieved by using MATLAB/Simulink program.The efficiency of the GPC is observed by calculating the integral of the square of the error (ISE) and the integral of the absolute value of the error (IAE) from experimental results which was optimized by the application of RSM. The three independent variables, which had been found the most effective variables on the GPC by screening experiments, were determined as NU, N2 and λ as minimum prediction horizon, maximum prediction horizon and control weighting, respectively. The quadratic models were developed through RSM in terms of related independent variables to describe the ISE and IAE as the two response. Based on statistic analysis, optimum GPC tuning parameters of NU (X1), N2 (X2) and λ (X3) for minimize the ISE were determined to be 1.7922, 1.9453 and 0.0642 and for minimize the IAE were determined to be 1.8880, 1.9752 and 0.0612, respectively. Calculated optimum points of GPC tuning parameters are close to based on ISE and IAE results. The data evaluated from the quadratic model were good agreement with those measured experimentally. The wireless temperature control is successfully applied to the process simulator and wireless control technique is proposed for various application areas.

Multi‐model direct generalised predictive control for automatic train operation system

The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFC) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie–Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFC. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFC. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

Improved Robustness of Generalized Predictive Control for Uncertain Systems

Abstract An off-line methodology has been developed to improve the robustness of an initial generalized predictive control (GPC) through convex optimization of the Youla parameter. However, this method is restricted with the case of the systems affected only by unstructured uncertainties. This paper proposes an extension of this method to the systems subjected to both unstructured and polytopic uncertainties. The basic idea consists in adding supplementary constraints to the optimization problem which validates the Lipatov stability condition at each vertex of the polytope. These polytopic uncertainties impose a non convex quadratically constrained quadratic programming (QCQP) problem. Based on semidefinite programming (SDP), this problem is relaxed and solved. Therefore, the robustification provides stability robustness towards unstructured uncertainties for the nominal system, while guaranteeing stability properties over a specified polytopic domain of uncertainties. Finally, we present a numerical example to demonstrate the proposed method.

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.

Generalized predictive control robust for position control of induction motor using field-oriented control

This paper presents the study and implementation of a different field-oriented control strategy using a generalized predictive control (GPC) technique applied to the mechanic position loop aiming to obtain a system that acts in the fractional horsepower motor driver running at near zero frequency. The position and speed loops were identified to verify the system behavior and, from the model found design the GPC controller. Simulation and experimental results are shown and discussed to demonstrate the merit of the proposed approach and the performance and robustness of the algorithms have been evaluated.

Wide-Area Damping Controller for Power System Interarea Oscillations: A Networked Predictive Control Approach

Wide-area damping controller (WADC) requires communication networks to transmit remote signals. The usage of communication networks will introduce time delays into the control loop of the WADC. Ignoring this time delay would deteriorate the damping performance provided by the WADC or even cause the whole system instability. This paper employs networked predictive control (NPC) to design a WADC for the generator exciter to enhance the damping of interarea oscillations in a large-scale power system. The NPC incorporates with a generalized predictive control (GPC) to generate optimal control predictions, and a network delay compensator to detect and compensate both constant and random delays. Moreover, model identification is used to obtain an equivalent reduced-order model of the large-scale power system and deal with the model uncertainties and variation of operating conditions. Case studies are based on the New England 10-machine 39-bus system. Effectiveness of the proposed WADC is verified by simulation studies and compared with a conventional WADC and a GPC-based WADC without delay compensation.