Model-based Predictive Control Design Research Articles

Procedures to Design Multi-Model-Based Predictive Controller Applied to BLDC Drive

One of the main reasons that prevent the use of model-based predictive control (MPC) in power electronics and motor drive is the MPC design. The processes related to these fields have fast dynamics, and most of the MPC design guides involve the control of slow dynamic processes, which allow high sampling times and, consequently, a large time for the control action calculation. Moreover, practically only the linear model-based predictive controller can have small computational cost, which causes difficulty for MPC application in nonlinear processes as electrical motors. Therefore, this paper presents a MPC design theory for first-order control models, as the brushless direct current (BLDC) motor drive, assuming direct speed control (without current inner loop). Thus, the MPC cost function tune is obtained according to prediction model parameters, the study of optimization gain curves and, optionally, an extended closed-loop root locus analysis. To deal with the motor nonlinearities, both multi-model approach and six-step modulation are employed. This paper also discusses current peak transients, salient pole BLDC motor modeling, integral action included in the MPC control action and solutions for Hall effect sensor nonlinear speed measurement. Simulation and experimental results confirm the proposed design theory as well as the other discussed issues.

Experimental investigation of alternative robust model predictive control of a heat exchanger

Advanced control of heat exchangers is an important task for control engineers, as these devices belong to the key equipment in chemical, petrochemical, food and pharmaceutical industries, and they are energy-intensive processes. This study presents novel robust model-based predictive control (MPC) of a heat exchanger. Influence of uncertain parameters was taken into account to design robust model-based predictive controller. Resulting optimization problem with constraints was formulated in the form of linear matrix inequalities, and the convex optimization problem was solved using semi-definite programming. The proposed alternative robust MPC design method was implemented using the novel software MUP. Extensive case study of heat exchanger control was done to demonstrate effectiveness of the alternative robust MPC. This novel strategy was compared with known robust MPC approaches. Experimental results confirmed that the alternative robust MPC improved control performance and ensured energy savings during the heat exchanger operation.

Hierarchical model-based predictive control of a power plant portfolio

One of the main difficulties in large-scale implementation of renewable energy in existing power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As a consequence, tomorrow's “smart grids” require highly flexible and scalable control systems compared to conventional power systems. This paper proposes a hierarchical model-based predictive control design for power system portfolio control, which aims specifically at meeting these demands. The design involves a two-layer hierarchical structure with clearly defined interfaces that facilitate an object-oriented implementation approach. The same hierarchical structure is reflected in the underlying optimisation problem, which is solved using Dantzig–Wolfe decomposition. This decomposition yields improved computational efficiency and better scalability compared to centralised methods. The proposed control scheme is compared to an existing, state-of-the-art portfolio control system (operated by DONG Energy in Western Denmark) via simulations on a real-world scenario. Despite limited tuning, the new controller shows improvements in terms of ability to track reference production as well as economic performance.

Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A Gaussian radial basis function (RBF) neural networks-based local linearization autoregressive with exogenous (ARX) model is utilized for describing the dynamics of a class of smooth nonlinear and nonstationary industrial processes. The dynamics of the underlying processes may be treated as the system operating-point-dependent time-varying locally-linear behavior. The RBF-ARX model is a pseudo-linear ARX model identified offline, and its functional coefficients are composed of the operating-point-dependent RBF neural networks. The RBF-ARX model-based predictive control (MPC) design to the nonlinear process is presented, and stability analysis of the nonlinear MPC under some conditions is discussed. Especially, the feasibility and effectiveness as well as the significant performance improvements of the nonlinear MPC design proposed is demonstrated with a real industrial application to the nitrogen oxide (NO$_{\rm x}$) decomposition (de-NO$_{\rm x}$) process in thermal power plants. </para>

ADAPTIVE MODELING FOR CONTROL OF GLYCEMIA IN CRITICALLY ILL PATIENTS

In this paper we propose an optimized adaptive ‘minimal’ modeling approach for predicting glycemia of critically ill patients and a corresponding Model based Predictive Control (MPC) setting for controlling glycemia. Re-estimations of the model, based on a real-life dataset from 19 critically ill patients, are performed every hour or every four hours by only considering recently passed data. The contributions of this study are the determination of the best dataset size for the re-estimations and the proposed MPC design. The results are satisfactory both in terms of forecasting ability and in qualitative controller performance.

An Application of the Model Based Predictive Control in a Hot Rolling Mill

The conventional PI control scheme has been used widely to control the mass flow and strip tension in a hot strip mill. However, the mutual interaction between the looper angle and strip tension prevents the use of large control gains. Slow and linear actions from the controller may induce severe strip quality defects and instability of the process under abnormal operation conditions. To solve these problems a control scheme based on model based predictive control (MPC) is suggested because of its systematic handing of interactions and constraints. The LQMPC algorithm, which is a relatively conventional MPC design, is implemented for the looper and tension control using the closed loop paradigm (CLP).

Generalised predictive control (GPC): A powerful control tool in medicine

The number of applications with the popular generalised predictive control (GPC) algorithm has risen considerably since the early years following its introduction. It is reaching a wider audience including clinicians, perhaps due to recent progress in the understanding of the design of model-based predictive control in general, followed by an awareness of the criteria governing the choice of the GPC ‘tuning parameters’ which can be tailored to a specific application. The development and evaluation of SISO GPC for on-line administration of muscle relaxant drugs in the operating theatre during surgery is considered first. Excellent regulation has been achieved with good robustness properties against surgical disturbances such as diathermy. The work has been extended to the multivariable case involving simultaneous control of muscle relaxation and unconsciousness using blood pressure monitoring. Results of preliminary trials are very encouraging.