Practical Nonlinear Model Predictive Control with Hammerstein Model Applied to a Test Rig of Refrigeration Compressors

This paper discusses the implementation and presents the results of a suboptimal nonlinear model predictive controller used to control the suction and discharge pressures of compressors under test in a rig. The objective of this rig is to emulate operational conditions to which refrigeration compressors can be subjected when applied in a refrigeration system, such as household refrigerators and freezers, and allow quick measurements of some of the compressor characteristics under those conditions. There is a coupling between suction and discharge pressures and the behavior of such variables is nonlinear with respect to the valve openings, thus the plant to be controlled can be characterized as multivariable and nonlinear. Even though in industry it is common to use linear controllers to control nonlinear plants, the use of nonlinear controllers can bring advantages in terms of performance and robustness. The controller implemented in this paper is the practical nonlinear model predictive control algorithm, which is a general framework that can be used for the implementation of nonlinear model predictive controllers considering almost any class of nonlinear model. Even though model predictive control is harder to be implemented than classical controllers, such as PID, it poses the process control problem in the time domain, so the concepts involved are intuitive and at the same time the tuning is relatively easy, even for the multivariable case. In addition, model predictive control allows constraints, such as valve opening limitations and pressure limits, to be handled during the design phase. This paper considers a specific nonlinear model architecture, the nonlinear Hammerstein model, which is composed of a static nonlinear element in series with a linear dynamic part. Since this model is conceptually simple and presents good results in most of the practical situations, it is widely used in practice when a nonlinear model is desired. The dynamics of the real test rig were identified using this nonlinear model structure and the identification results are discussed. The practical nonlinear model predictive controller was implemented in the real test rig, being tested in a variety of operating conditions. The results of the controller are compared with the ones obtained with a classical PID controller. The modeling approach presented good results and the results obtained in this study show that it is possible to use nonlinear model predictive control algorithms in refrigeration test rigs, and that this use can contribute to increasing the productivity and operational efficiency of compressor tests

Linear active disturbance rejection control of waste heat recovery systems with organic Rankine cycles

In this paper, a linear active disturbance rejection controller is proposed for a waste heat recovery system using an organic Rankine cycle process, whose model is obtained by applying the system identification technique. The disturbances imposed on the waste heat recovery system are estimated through an extended linear state observer and then compensated by a linear feedback control strategy. The proposed control strategy is applied to a 100 kW waste heat recovery system to handle the power demand variations of grid and process disturbances. The effectiveness of this controller is verified via a simulation study, and the results demonstrate that the proposed strategy can provide satisfactory tracking performance and disturbance rejection

State-Space Interpretation of Model Predictive Control

A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optimal filter gain is parametrized explicitly in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC)

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm

Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure

A data-based approach for multivariate model predictive control performance monitoring

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system