Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects

System Identification for the design of behavioral controllers in crowd evacuations

Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distancekeeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input?output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures.Ministerio de Economía y Competitivida

Disturbance models for offset-free nonlinear predictive control

Offset-free model predictive control refers to a class of control algorithms able to track asymptotically constant reference signals despite the presence of unmeasured, nonzero mean disturbances acting on the process and/or plant model mismatch. Generally, in these formulations the nominal model of the plant is augmented with integrating disturbances, i.e. with a properly designed disturbance model, and state and disturbance are estimated from output measurements. To date the vast majority of offset-free MPC applications are based on linear models, however, since process dynamics are generally inherently nonlinear, these may perform poorly or even fail in some situations. Better results can be achieved by making use of nonlinear formulations and hence of nonlinear model predictive control (NMPC) technology. However, the obstacles associated with implementing NMPC frameworks are nontrivial. In this work the offset-free tracking problem with nonlinear models is addressed. Firstly some basic concepts related to the observability of nonlinear systems and state estimation are reviewed, focusing on the digital filtering and putting a strong accent on the role of the disturbance model. Thus, a class of disturbance models in which the integrated term is added to model parameters is presented together with an efficient and practical strategy for its design and subsequent implementation in offset-free NMPC frameworks

Sviluppo di strategie di monitoraggio ed identificazione di controllori predittivi

Process optimization represents an important task in the management of industrial plants. It is strictly related to plant economics, so there is a high interest in the definition of reliable optimization schemes. At the moment, one of the most successful optimization algorithm is represented by MPC. This is an acronym standing for ``Model Predictive Controller''. MPC optimizes the process to which it is applied by ``predicting'' the state of the system over a future time window, using a process model as a part of its internal structure. MPC has been used since the last two decades, and at the moment it represents a proven optimization scheme, with thousands of applications in chemical and petrochemical industry. It is important to check regularly for MPC performances in order to guarantee optimal operation in spite of unknown disturbances and/or changes in the process dynamics. Such an operation is named “Performance Monitoring” or simply “Monitoring”. Despite its importance, at the moment there has not been an extensive analysis of this task in the literature, which is relatively poor compared to other fields related to MPC. A monitoring technique should be able to discern the cases in which the optimization scheme is working in optimal or sub-optimal conditions, and, in this latter case, it should recognize the causes of performance degradation. In the literature, the causes of performance degradation are usually two, i.e. inadequate estimation of unknown disturbances and a mismatch between the internal model and the real process. In the first case, the operations that are needed to correct the mistake consist in a better definition of the noise level of the system and in the calculation of a new estimator using the correct disturbance information. In the second case, the only way to improve the performances of the system is the definition of a new process model. This is a complex task, which takes a long time and requires a particular attention, and it is named “Identification”. Several different identification techniques were presented in the literature. They use input and output data sets coming from the system to compute a process model In this thesis, identification techniques for systems which can present difficulties have been introduced, i.e. unstable systems and ill-conditioned systems, and a monitoring technique for optimization schemes, tailored on MPC structure, has been discussed. Unstable systems cannot be usually identified with a class of identification schemes that perform a particular regression on data, because numerical problems arise due to the presence of high powers of the ``unstable'' system dynamic matrix. This work introduces an extension of the structure of this class of identification schemes which permits to handle data coming from an unstable system. Ill-conditioned processes give problems because data coming from this kind of processes are aligned in a particular direction, called “strong direction”. For this reason, a high level of information is present in the data set over that direction, but a low information level is present over other directions, resulting in models which cannot describe the system adequately in all directions. This work presents the guidelines of a successful identification method in which data are collected in closed-loop. Finally, the problem of MPC monitoring in this work is addressed analyzing the difference between the value of the real outputs coming from the system and the value of outputs predicted by the internal MPC model, which is usually indicated as ``Prediction error''. This analysis takes into account the statistical properties of the previously mentioned prediction error, in order to define if the optimization scheme works in sub-optimal conditions. Then, if this analysis shows the presence of some issues, the cause that generates these issues is determined by checking the rank of a particular matrix obtained from data, that is the observability matrix of an extended closed-loop system

Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future