scholarly journals FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS

2019 ◽  
Vol 5 (5) ◽  
pp. 0408-0414
Author(s):  
S. B. ROVEA ◽  
RODOLFO FLESCH
Keyword(s):  
Predictive Control ◽  
Control Structure ◽  
Optimization Problems ◽  
Linear Structure ◽  
Computational Effort ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Generalized Predictive Control ◽  
Model Parameters ◽  
Sampling Instant

This paper proposes a fast predictive control structure with online model update according to process parametric variations. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates the recursive least squares identification method with a variable forgetting factor to estimate at each iteration the parameters of a linear structure model used for multi-step ahead prediction. For a system with constraints on the process variables, the resulting optimization problem of GPC is solved using quadratic programming based on the Alternate Direction Method of Multipliers, which allows the control signal to be obtained with small computational effort. In order to validate the proposed algorithm an experimental case study that considers the speed control of a direct current motor and the proposed controller embedded in a microcontroller STM32F303K8T6 is presented. Experimental results use as baseline the GPC with fixed model parameters and show that the proposed fast adaptive predictive control structure is able to keep almost the same transient response for all the considered operating points of the motor, while GPC presents high oscillations at operating conditions far from the one used to obtain the nominal model. Even though the proposed controller needs to solve two optimization problems at each sampling instant, it can run about 60 times in a second in the microcontroller used in this study

Research on Networked Control Systems Based on Adaptive Predictive Control

2013 ◽  
Vol 441 ◽  
pp. 833-836
Author(s):  
Zai Ping Chen ◽  
Xue Wang
Keyword(s):  
Time Delay ◽  
Control Systems ◽  
Predictive Control ◽  
Control Strategy ◽  
Networked Control Systems ◽  
Networked Control ◽  
Random Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Adaptive Predictive Control

According to the random time-delay exist in sensor-controller channel and controller-actuator channel in networked control systems, an adaptive predictive control strategy was proposed. In this control strategy, an improved generalized predictive control algorithm is adopted to compensate the networked random time-delay. In addition, using the recursive least squares with a variable forgetting factor algorithm to indentify the model parameters of controlled object on-line, through the way, it could adjust the systems with unknown parameters adaptively. Simulation results show that the adaptive predictive control proposed could solve random time-delay of networked control systems effectively.

Simulation and Research for Generalized Predictive Control

2013 ◽  
Vol 694-697 ◽  
pp. 2205-2210
Author(s):  
Xiao Li Yu
Keyword(s):  
Predictive Control ◽  
Least Square ◽  
The Self ◽  
Dynamic Responses ◽  
Generalized Predictive Control ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Input Signals ◽  
Self Tuning ◽  
Time Invariant

This paper presents analysis and experiments for Generalized Predictive Control (GPC) algorithm based on software simulation. First, we illustrate the time invariant GPC algorithm in detail. Then, we describe the principle for the control parameter selection of GPC based on empirical results. The Recursive Least Square (RLS) algorithm will be used to identify model parameters in the self-tuning GPC. The performance of GPC algorithm is validated by simulation results, which show that the algorithm has rapid and accurate dynamic responses for input signals, such as step signal and square wave. When the model parameters are unknown, with the assistance of RLS, the self-tuning GPC algorithm also presents good performance and robustness capability, even when white Gaussian noise exists.

scholarly journals Efficient Multivariable Generalized Predictive Control for Autonomous Underwater Vehicle in Vertical Plane

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Xuliang Yao ◽  
Guangyi Yang
Keyword(s):  
Energy Consumption ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Optimization Problems ◽  
Vertical Plane ◽  
Underwater Vehicle ◽  
Generalized Predictive Control ◽  
Time Step ◽  
Control Approach ◽  
Output Constraints

This paper presents the design and simulation validation of a multivariable GPC (generalized predictive control) for AUV (autonomous underwater vehicle) in vertical plane. This control approach has been designed in the case of AUV navigating with low speed near water surface, in order to restrain wave disturbance effectively and improve pitch and heave motion stability. The proposed controller guarantees compliance with rudder manipulation, AUV output constraints, and driving energy consumption. Performance index based on pitch stabilizing performance, energy consumption, and system constraints is used to derive the control action applied for each time step. In order to deal with constrained optimization problems, a Hildreth’s QP procedure is adopted. Simulation results of AUV longitudinal control show better stabilizing performance and minimized energy consumption improved by multivariable GPC.

A New Adaptive Generalized Predictive Control Algorithm for Nonlinear Processes

2010 ◽  
Author(s):  
Ma’moun Abu-Ayyad ◽  
Rickey Dubay ◽  
Bambang Pramujati
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Strategies ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Steel Cylinder ◽  
Unique Method ◽  
Predictive Controller ◽  
Process Prediction ◽  
Sampling Instant

This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems. This method is termed adaptive generalized predictive control which uses a multi-dimensional surface of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The adaptive generalized predictive controller was tested in simulation and its control performance compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results both demonstrate that the adaptive generalized predictive controller demonstrated improved closed-loop performance. The formulation of the nonlinear surface provides the mechanism for the adaptive approach to be readily applied to other advanced control strategies making the methodology generic.

Nonlinear Generalized Predictive Control Approach for Challenging Processes

2009 ◽  
Author(s):  
Ma’moun Abu-Ayyad ◽  
Lakshamirinyan Chinta Venkateswararao ◽  
Rickey Dubay
Keyword(s):  
Predictive Control ◽  
Control Strategies ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Original Structure ◽  
Real Time Control ◽  
Steel Cylinder ◽  
Control Approach ◽  
Dynamic Matrix ◽  
Sampling Instant

This paper presents the implementation of the fundamental concept of the infinite modeling methodology to the generalized predictive control (GPC) algorithm. This method was termed as infinite modeling generalized predictive control (IMGPC) which uses the nonlinear characteristics of the process such as the process gain and time constant to recalculate the dynamic matrix every sampling instant. Computer simulations were performed on nonlinear plants with different degrees of nonlinearity demonstrating that the infinite modeling approach is readily implemented providing improved control performance comparing to the original structure of GPC. Practical work included real-time control application on a steel cylinder temperature control system. Simulation and experimental results demonstrate that the methodology of infinite modeling is applicable to other advanced control strategies making the methodology generic.

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