Gain-scheduled Model Predictive Control Research Articles

Gain-Scheduled Model Predictive Control for Cart–Inverted-Pendulum with Friction and Disturbances

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.

Gain scheduling model predictive control for dynamic positioning of floating caissons at different draughts

SummaryThis article presents a model predictive control for dynamic positioning (DP) of floating caisson at different draughts. This article focuses on the maneuvres related to the control of the spatial situation of the caisson in connexion with the final caisson reference point and the process of descending to the seabed desired location. Different models of the caisson in order to perform realistic simulations and to test the robustness of the controller are considered. A gain scheduling model predictive control (GSMPC) has been implemented, with parameter switch among four model predictive control (MPC) controllers. In this way, a specific MPC for the dynamics of each draught will be used in each caisson model corresponding to a draught. Furthermore, a Kalman filter has been implemented to remove the disturbances. Finally, in the simulations results it is compared the performance of the proposed controller with a clasic one and also it is checked how the proposed controller is able to position the system in all draughts with stability and accurate dynamic control of the system.

Dynamic Improvement of Hydraulic Excavator Using Pressure Feedback and Gain Scheduled Model Predictive Control

This work focuses on a gain scheduled Model Predictive Controller (MPC) for the dynamic performance enhancement of an excavator boom. The method can be generalized and applied to similar machines with the installation of pressure and boom angle transducers without modifying the hydraulic circuit of the system. In the proposed method, the transient part of pressure signals is isolated and used to counteract the actuator vibrations through a valve-based compensator. A full-scale model of an excavator's arm has been used for the experimental verification of the proposed approach. The experiments are conducted under various conditions with the proposed controller and compared with the conventional system. Since the stability of these types of machines is extremely important from the safety point of view, a stability analysis has been carried out and closed-loop stability of the system is ensured with the proposed controller. A significant reduction of 58% and 41% have been achieved in the cost function under maximum velocity extension and lowering phase respectively, where maximum boom oscillations are observed. Furthermore, an average vibration reduction of 46 % is obtained using the proposed method.

Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System

This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.

Advanced Operation of the Steam Methane Reformer by Using Gain-Scheduled Model Predictive Control

A rigorous computationally effcient closed-loop system with a gain-scheduled model predictive controller (MPC) is developed for the first time, where a first-principle model of the steam methane re...

Gain-scheduling model predictive control of a Fresnel collector field

Model predictive control strategies have been applied successfully when controlling solar plants. If the control algorithm uses a linear model associated only to an operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate.In this paper, a gain scheduling model predictive control strategy is designed for the Fresnel collector field located at the Escuela Superior de Ingenieros de Sevilla. Simulation results are provided comparing the proposed strategy with another linear MPC controller showing a better performance. Furthermore, two real tests are presented showing the effectiveness of the proposed strategy.

Real-Time Production Optimization of Steam-Assisted-Gravity-Drainage Reservoirs Using Adaptive and Gain-Scheduled Model-Predictive Control: An Application to a Field Model

Summary Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from deep oil-sands deposits. The efficiency of the SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool (difference in saturation and actual temperature) along the length of the horizontal well pair. Heterogeneity in reservoir properties might lead to suboptimal subcool levels without the application of closed-loop control. Recently, model-predictive control (MPC) has been proposed for real-time feedback control of SAGD well pairs based on real-time production, temperature, and pressure data along with other well and surface constraint information; however, reservoir dynamics has been represented using extremely simplified and unrealistic models. Because SAGD is a complex, spatially distributed, nonlinear process, an MPC framework with models that account for nonlinearity over an extended control period is required to achieve optimized subcool and steam conformance. In this research, two novel work flows are proposed to handle nonlinear reservoir dynamics in MPC. The first approach is adaptive MPC, and includes continuous re-estimation of the model at each control interval. This allows the evolution of the coefficients of a fixed-model structure such that the updated system-identification model in the MPC controller reflects current reservoir dynamics adequately. Another approach, gain-scheduled MPC, decomposes the subcool-control problem in a parallel manner, and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by using appropriate variations in input parameters based on the operating region. The work flows are implemented using a history-matched numerical model of a reservoir in northern Alberta. Steam-injection rates and liquid-production rate are considered input variables in MPC, constrained to available surface facilities. The well pair is divided into multiple sections, and the subcool of each section is considered an output variable. Results are compared with actual field data (in which no control algorithm is used), and are analyzed on the basis of two criteria: (1) Do all subcools track the set point while maintaining stability in input variables? and (2) Does the net present value (NPV) of oil improve with adaptive and gain-scheduled MPC? In general, we conclude that both adaptive and gain-scheduled MPC provide superior tracking of subcool set points and, hence, better steam conformance caused by adequate representation of reservoir dynamics by re-estimation of coefficients and multiple controllers, respectively. In addition, the results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69 and 10.36% in case of adaptive and gain-scheduled MPC, respectively. The proposed work flows can improve the NPV of an SAGD reservoir by optimizing the well-operational parameters while considering constraints of surface facilities and minimizing environmental footprint.

Gain Scheduling Model Predictive Control of the New TCP-100 Parabolic Trough Field

Advanced control strategies can play an important role in improving the efficiency of solar plants. In particular, linear model predictive control strategies have been applied successfully when controlling solar trough plants. However, if the control algorithm uses a linear model associated only to one operating point, when the plant is working far from the design conditions, the performance of the controller may deteriorate.In this paper, a gain scheduling model predictive control strategy is designed for the new PTC TCP-100 research facility at the PSA, currently under construction, is presented. Simulation results are provided using a mathematical model of the solar field, showing the good performance of the proposed strategy.

ゲインスケジューリングモデル予測制御によるHEVの燃費最適化

ゲインスケジューリングモデル予測制御によるHEVの燃費最適化

Gain-scheduling model predictive control for unmanned airship with LPV system description

This paper presents a gain-scheduling model predictive control (MPC) for linear parameter varying (LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral control of unmanned airship. The unmanned airship is modeled by an LPV-type system and transformed into a polytopic uncertain description with actuator saturation. By introducing a parameter-dependent state feedback law, the set invariance condition of the polytopic uncertain system is identified. Based on the invariant set, the gain-scheduling MPC controller is presented by solving a linear matrix inequality (LMI) optimization problem. The proposed gain-scheduling MPC algorithm is demonstrated by simulating on the unmanned airship system.

Methodology for Designing and Comparing Robust Linear versus Gain-Scheduled Model Predictive Controllers

A methodology is proposed to design a robust Gain-Scheduled Model Predictive Control (MPC) strategy and to quantify the relative advantages of this controller versus a Linear MPC strategy. For the purpose of analysis and controller design, the process is represented by a nonlinear state-affine model identified from input−output data. This model can be split in linear and nonlinear terms where the linear part is used for controller design and the nonlinear part is accounted for as model uncertainty. Then, robust stability and robust performance tests are formulated based on linear matrix inequalities where the manipulated variables weight of the controllers is tuned to maximize performance. The uncertainty bounds used for the robustness tests are obtained in an iterative fashion by using the frequency response of the manipulated variable with respect to the feedback error. The control strategy performance is quantified by the ratio between the error norm and the disturbance norm. Finally, a case study involving a multiple-input−multiple-output bioreactor is presented. The study is able to predict for which range of operation the Gain-Scheduling MPC surpasses the performance of the Linear MPC.

Min–max MPC algorithm for LPV systems subject to input saturation

In this paper, a new model predictive control (MPC) algorithm is developed for polytopic linear parameter-varying (LPV) systems subject to input saturation. A set invariance condition for discrete-time LPV systems with input saturation is identified and the invariant set is determined by solving an LMI optimisation problem, which directly incorporates input saturation. Based on this set invariance condition, a min–max MPC algorithm is proposed for the LPV systems with input saturation. A gain-scheduling MPC algorithm is also proposed for the design of a parameter-dependent controller. Numerical examples demonstrate the effectiveness of the algorithms.