Generalized Predictive Control Research Articles

An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS) model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN) is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM) is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC) with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.

Nonlinear continuous-time generalized predictive control of solar power plant

This paper presents an application of nonlinear continuous-time generalized predictive control (GPC) to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A brief description of the solar power plant and its simulator is given. After that, basic concepts of predictive control and continuous-time generalized predictive control are introduced. A new control strategy, named nonlinear continuous-time generalized predictive control (NCGPC), is then derived to control the process. The simulation results show that the NCGPC gives a greater flexibility to achieve performance goals and better perturbation rejection than classical control.

GPC-Based Gust Response Alleviation for Aircraft Model Adapting to Various Flow Velocities in the Wind Tunnel

A unified autoregressive (AR) model is identified, based on the wind tunnel test data of open-loop gust response for an aircraft model. The identified AR model can be adapted to various flow velocities in the wind tunnel test. Due to the lack of discrete gust input measurement, a second-order polynomial function is used to approximate the gust input amplitude by flow velocity. Afterwards, with the identified online aeroelastic model, the modified generalized predictive control (GPC) theory is applied to alleviate wing tip acceleration induced by sinusoidal gust. Finally, the alleviation effects of gust response at different flow velocities are estimated based on the comparison of simulated closed-loop acceleration with experimental open-loop one. The comparison indicates that, after gust response alleviation, the wing tip acceleration can be reduced up to 20% at the tested velocities ranging from 12 m/s to 24 m/s. Demonstratively, the unified control law can be adapted to varying wind tunnel velocities and gust frequencies. It does not need to be altered at different test conditions, which will save the idle time.

Control and synchronization in chaotic systems based on fast linear predictive control

A kind of fast linear generalized predictive control (GPC) algorithm is proposed based on the extended state observer for chaotic (hyperchaotic) systems. The linear extended state observer is employed to estimate and compensate the nonlinear dynamics and the existing uncertainties of the chaotic (hyperchaotic) systems so that an integrator can be obtained to serve as the model for GPC design. Using this scheme, the computational complexity can be substantially reduced. A step coefficient matrix can be derived analytically and a future output prediction can be explicitly calculated by only using the last two samples of the output. Therefore, the self-tuning algorithms and the Diophantine equation can be completely avoided. The proposed method can be used to control nonlinear targets in a straightforward manner. Simulation results show the effectiveness of this linear algorithm.

Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input–output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.

Off-line robustification of Generalized Predictive Control for uncertain systems

Abstract An off-line methodology was proposed for enhancing the robustness of an initial Generalized Predictive Control (GPC) by convex optimization of the Youla parameter. However, this procedure of robustification 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 structured polytopic uncertainties. The main 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 set of non convex quadratic constraints. The globally optimal solution is found by means of the GloptiPoly3 software. Therefore, this robustification provides stability robustness towards unstructured uncertainties for the nominal system, while guaranteeing stability properties over a specified polytopic domain of uncertainties. Finally, an illustrative example is given

Generalized predictive control tuning by controller matching

This paper presents a tuning method for the model predictive control (MPC) based on the transfer function formulation, also known as generalized predictive control (GPC). The aim of the method is to find the tuning parameters of GPC to obtain the same behavior as an arbitrary linear-time-invariant (LTI) controller (favorite controller). The approach consists of two steps. The first step matches GPC gain to that of the favorite controller by equating the respective coefficients of the transfer function of the control law to those of the favorite controller. This step is followed by finding the weighting matrices in the cost function that will result in the GPC gain which is obtained in the first step. This proposed tuning approach does not require either loop-shifting techniques to deal with non-strictly-proper favorite controllers or equal prediction and control horizons as in conventional inverse optimality problems. In this paper, we also extend the method to the feed-forward case, which is seldom considered in standard reverse-engineering tuning methods. The feasibility conditions of the matching of a GPC with a favorite controller are analyzed and the limitation in control space the GPC can span with different tuning settings is shown. The proposed tuning method is demonstrated on a binary distillation column example.

Process Control of Dense Medium Separation Based on Improved Implicit Generalized Predictive Control Algorithm

In order to solve the model mismatch between density and liquid level of heavy medium suspension during the dense medium separation process, implicit Generalized Predictive Control (GPC) was applied to realize the on-line identification for model parameters and the decoupling control for density and liquid level of heavy medium suspension. And then improving the implicit GPC algorithm, it can ensure good control performance by constraining control increment to avoid solving the inverse matrix and reduce the computational complexity. The simulation result shows that improved implicit GPC is more effective for the model characteristic of the dense medium separation process with large lag and strong coupling, it has strong anti-interference capability, and the system output is still stable in the case of model mismatch.

GPC Control Strategy and Simulation of a ZigBee Based Wireless Networked Control System

Aiming at the random delays and packet dropouts in ZigBee network, a Generalized Predictive Control (GPC) algorithm is proposed. The control sequence combine with timestamp, feedback sequence and queuing mechanisms to design a predictive control strategy. The GPC algorithm derivation, system design and implementation method are given, and then the algorithm is validated in a networked control system simulation toolbox based on TrueTime. The proposed algorithm shows better performance than conventional PID algorithm for a DC servo motor in control system based on ZigBee technology.

Hybrid Position Controller for an Indirect Field-oriented Induction Motor Drive

This paper presents the study and implementation of a hybrid position controller for an indirect field-oriented induction motor drive. In order to improve the position tracking performance of an induction motor drive, a design procedure of a hybrid controller is developed based on the conventional Proportional (P) controller and the Generalized Predictive Control (GPC) controller. The fuzzy logic is utilized to achieve the hybridization between the P and GPC controllers. The P is adopted because its parameter values can be simple chosen with an aggressive tuning (fast response), while the GPC controller enhances the robustness and has a moderate controller action. Then, the position control loop is regulated with the hybrid controller, while the speed and currents loops incorporate PI (Proportional Integral) controllers. The proposed controller, simulations, implementation data, and test results with step, trapezoidal, triangular position profiles and step change in load, are given, discussed and verified. It is shown that the proposed position hybrid controller has a fast tracking capability to industrial robotics applications and it is robust to load disturbance.

Nonlinear generalized predictive control based on online least squares support vector machines

For a class of nonlinear discrete systems with unknown parameters, an adaptive direct generalized predictive control method based on online least squares support vector machines (OLS–SVM) is proposed. In the method, the OLS–SVM is used to design the controller directly, and an improved projection algorithm based on the tracking error is introduced to adjust the weights of the OLS–SVM adaptively, so the inverse matrix is avoided in the process of online real-time control. It is proved that the proposed method can make the tracking error converge to a small neighborhood of the origin. Simulation results have shown the correctness and effectiveness of the proposed method.

A novel exergy-event based model predictive control strategy for energy saving

Industrial processes are among the biggest energy consumers and also the highest energy-wasting fields and many applied methods have been proposed to save energy more in these processes. The main purpose of event-based control is to reduce the computing load and communication efforts only after an event occurs which could save energy. Energy consumption and control performance are major challenges in event-based control. Exergy is the basic criterion to analyze a process in terms of the energy associated with control performance. MPC a widely used in industry, is utilized to solve an optimal control problem based on control performance aiming to save more energy. Moreover, MPC enables the controller to cope explicitly with MIMO plants and constraints such as state constraints and actuator constraints. The exergy-event based strategy under the general model predictive control (MPC) framework from viewpoint of energy saving is investigated in this paper. It will be shown that the proposed architecture is guaranteed to reduce the energy and exergy losses, the computational burden and communication effort of the whole system.

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture

This work addresses effective utilization of flue gases through the proper pH control in raceway reactors. The pH control problem has been addressed with an event-based control approach using a Generalized Predictive Controller (GPC) with actuator deadband. Applying this control strategy it is possible to reduce the control effort, and at the same time saving control resources. In the pH process case, the event-based controller with actuator deadband can be tuned to supply only necessary amount of CO2 to keep the pH close to its optimal value. On the other hand, the evaluated control algorithm significantly improves the pH control accuracy, what has a direct influence on biomass production. In order to test the performance of the event-based GPC controller, several experiments have been performed on a real raceway reactor. Additionally, several control performance indexes have been used to compare the analyzed technique with commonly used on/off controller.

Control Algorithm Research Based on Heating System of Generalized Predictive PID

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.

A Control Strategy for Continuously Adjusting Attack Angle in Wind Tunnel Experiments

In wind tunnel experiments, in order to adjust the attack angle continuously, the support mechanism movement should be steady and smooth. However, the electro-hydraulic servo system is a typical nonlinear, time-varying and uncertainty system, and the wind tunnel environment is very complicated. To address these problems, an on-line identification and generalized predictive control (GPC) strategy is proposed in this study. Firstly, the Labview and AMESim are integrated to build an electro-hydraulic system simulation model. Secondly, the controlled auto-regressive moving average (CARIMA) model of the electro-hydraulic system is developed. Thirdly, the influence on the system performance owing to the control parameters, model parameters, and external disturbance are widely discussed and deeply analyzed. At last, a test platform is constructed with the National Instruments (NI) embedded real time technology. The proposed control strategy is tested and verified on this test platform. The experimental results show that the angular velocity control precision reaches 0.01°/s. It implies that this control strategy has a good performance for nonlinear velocity control. Thus it satisfies the requirement of the continuously adjusting attack angle in wind tunnel experiments.

Predictive PID Control Based on GPC Control of Inverted Pendulum

Having regard to the large application of the inverted pendulum in robotic system, this study is interested in controlling this process with two strategies of controls. The first proposed control is the state feedback with an observer based on the Generalized Predictive Control (GPC) algorithm. In the second proposed control we used the characteristic of predictive control GPC to improve the performance of the classical PID controller. The obtained results have been discussed and compared; the simulation results obtained by the predictive PID control are mentioned.

Robust generalized predictive control of the Orthoglide robot

Purpose – The purpose of this paper is to address the synthesis and experimental application of a generalized predictive control (GPC) technique on an Orthoglide robot. Design/methodology/approach – The control strategy is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on GPC strategy, which is robustified against measurement noise and neglected dynamics using Youla parameterization. Findings – The experimental results show the benefits of the robustified predictive control strategy on the dynamical performance of the Orthoglide robot in terms of tracking accuracy, disturbance rejection, attenuation of noise acting on the control signal and parameter variation without increasing the computational complexity. Originality/value – The paper shows the implementation of the robustified predictive control strategy in real time with low computational complexity on the Orthoglide robot.

Autonomous takeoff control system design for unmanned seaplanes

Unmanned seaplanes are special kinds of fixed-wing airplanes which can achieve autonomous takeoff and landing on water. Due to complicated hydrodynamic forces and unpredictable sea states, researches on modeling, dynamic analysis and controller design are still facing great challenges. In this paper, the dynamic characteristics and motion stability of the unmanned seaplane are firstly analyzed based on a nonlinear mathematical model. The proposed autonomous takeoff control system consists of two main parts: T-S fuzzy identification and generalized predictive control (GPC). A linear CARIMA model obtained through T-S fuzzy identification is used to represent the dynamic characteristics in different motion stages. Wave forecasting is considered in the GPC algorithm to improve the anti-waves capability and avoid unstable phenomena in high sea states. Simulations are performed in three different wave conditions, including calm water, regular wave and irregular wave. Moreover, GPC controller is compared with other disturbance rejection control solutions. The simulation results show that the proposed controller has good performances for autonomous takeoff of the unmanned seaplane.

Design of wide‐area damping systems based on the capabilities of the supporting Information Communication Technology infrastructure

The dependence of the functionality of the wide-area monitoring and control (WAMC) systems on the capabilities of their underlying Information Communication Technology (ICT) infrastructure indicates that the parameterisation of the WAMC application algorithm should be coordinated with the design of the supporting ICT system. This study presents a systematic approach to design wide-area damping systems considering phasor measurement unit (PMU) communication delays and data frame losses. The proposed design method is applied to calibrate a wide-area damping system based on generalised predictive control (GPC) for demonstration purposes. In addition, a framework to guide the parameterisation of the GPC- based wide-area damping system is also provided. This research offers a better perspective to predict the capabilities of the underlying ICT infrastructure in the WAMC systems. Moreover, the proposed design method can be generalised to design delay-robust damping systems with compensation limits. Finally, it also offers an opportunity to associate the parameterisation of the damping system with the capabilities of its supporting ICT infrastructure and the reliability of the intended damping control function.