Monitoring Control Updating Period In Fast Gradient Based NMPC

In this paper, a method is proposed for on-line monitoring of the control updating period in fast-gradient-based Model Predictive Control (MPC) schemes. Such schemes are currently under intense investigation as a way to accommodate for real-time requirements when dealing with systems showing fast dynamics. The method needs cheap computations that use the algorithm on-line behavior in order to recover the optimal updating period in terms of cost function decrease. A simple example of constrained triple integrator is used to illustrate the proposed method and to assess its efficiency.Comment: 6 pages, 8 Figure

On Adaptive Measurement Inclusion Rate In Real-Time Moving-Horizon Observers

This paper investigates a self adaptation mechanism regarding the rate with which new measurements have to be incorporated in Moving-Horizon state estimation algorithms. This investigation can be viewed as the dual of the one proposed by the author in the context of real-time model predictive control. An illustrative example is provided in order to assess the relevance of the proposed updating rule.Comment: 6 pages. 4 Figure

A Parametric Non-Convex Decomposition Algorithm for Real-Time and Distributed NMPC

A novel decomposition scheme to solve parametric non-convex programs as they arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of a fixed number of alternating proximal gradient steps and a dual update per time step. Hence, the proposed approach is attractive in a real-time distributed context. Assuming that the Nonlinear Program (NLP) is semi-algebraic and that its critical points are strongly regular, contraction of the sequence of primal-dual iterates is proven, implying stability of the sub-optimality error, under some mild assumptions. Moreover, it is shown that the performance of the optimality-tracking scheme can be enhanced via a continuation technique. The efficacy of the proposed decomposition method is demonstrated by solving a centralised NMPC problem to control a DC motor and a distributed NMPC program for collaborative tracking of unicycles, both within a real-time framework. Furthermore, an analysis of the sub-optimality error as a function of the sampling period is proposed given a fixed computational power.Comment: 16 pages, 9 figure

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields

Optimal trajectory management for aircraft descent operations subject to time constraints

The growth in traffic increased the pressure on the environmental sustainability of air transport. In this context, many research effort has been devoted to minimise the environmental impact in the different phases of flight. Continuous descent operations, ideally performed with the engines at idle from the cruise altitude to right before landing, have shown to reduce fuel, noise nuisance and gaseous emissions if compared to conventional descents. However, this type of operations suffer from a well known drawback: the loss of predictability from the air traffic control point of view in terms of overfly times at the different waypoints of the route. Due to this loss of predictability, air traffic controllers require large separation buffers, thus reducing the capacity of the airport. Previous works investigating this issue showed that the ability to meet a controlled time of arrival at a metering fix could enable continuous descent operations while simultaneously maintaining airport throughput. In this context, the planning and guidance functions of state-of-the-art flight management systems need to be modernised. On-board trajectory planners capable to generate an optimal trajectory plan satisfying time constraints introduced during the flight are seldom, mainly because the real-time optimisation of aircraft trajectories is still elusive. Furthermore, the time scale and spatial resolution of the wind forecasts used by these trajectory planners are far from being adequate to generate accurate flight time predictions. Finally, there exist guidance strategies capable to accurately comply with time constraints enforced at a certain fix in the trajectory plan, yet they are not specifically designed to minimise the environmental impact. This PhD thesis aims at investigating fast optimisation techniques to enable real-time updates of the optimal trajectory plan subject to time constraints during the descent; wind networking concepts to generate more accurate and up-to-date wind forecasts and, consequently, time predictions; and more robust an efficient guidance strategies to reduce the environmental impact at the maximum extent while complying with the time constraints of the trajectory plan. First, the feasible time window at a metering fix that could be achieved during a descent requiring neither thrust nor speed brakes usage is quantified as a function of the aircraft states (altitude, distance to the metering fix and airspeed), aiming to assess the feasibility of guidance strategies that take advantage of time and energy management concepts. Then, the performance of four of these guidance strategies is compared in terms of environmental impact mitigation and ability to satisfy operational constraints. Results from the comparison reveal that model predictive control, a strategy based on a frequent re-calculation of the optimal trajectory plan during the execution of the descent, is the most robust in terms of energy and time deviation at the metering fix, providing at the same time excellent environmental impact mitigation figures. However, the execution time required to solve a rigorous trajectory optimisation problem at each re-calculation instant remains a critical limitation for practical applications. In order to address this issue, a variant of the model predictive control strategy that allows for fast updates of the optimal trajectory plan based on parametric sensitivities is proposed, which shows analogous results yet halving the time needed to update the optimal trajectory plan. Finally, the potential benefits of using wind observations broadcast by nearby aircraft to reconstruct the wind profile downstream right before updating the optimal trajectory plan when using model predictive control is also investigated. Promising results show that the combination of model predictive control with wind networking concepts could enable optimal descents without degrading the capacity of the airport.El creixement del trànsit ha augmentat la pressió sobre la sostenibilitat ambiental del transport aeri. En aquest àmbit s'han dedicat molts esforços en recerca per reduir l'impacte ambiental en les diferents fases del vol. Les operacions de descens continu, en les quals l'aeronau descendeix amb els motors a ralentí des de l'altitud de creuer fins just abans d'aterrar, han demostrat ser una solució atractiva per reduir el combustible, el soroll i les emissions en la fase de descens. Desafortunadament, aquest tipus d'operacions tenen un inconvenient molt important: la pèrdua de predictibilitat des del punt de vista dels controladors de trànsit aeri, en termes de temps de sobrevol als diferents punts de pas de la ruta. Per aquesta raó, els controladors necessiten aplicar més separació entre aeronaus, reduint així la capacitat de l'aeroport. Treballs anteriors han demostrat que si les aeronaus fossin capaces de satisfer restriccions de temps de sobrevol a un o més punts de pas, seria possible implementar operacions de descens continu sense degradar la capacitat de l'aeroport. Malauradament, avui en dia existeixen pocs sistemes de gestió de vol capaços de generar trajectòries òptimes que satisfacin restriccions de temps, principalment perquè l’optimització de trajectòries en temps real continua sent una tasca difícil. A més, la resolució espacial i temporal dels models de vent utilitzades per els planificadors de trajectòria no son suficients per generar prediccions de temps de sobrevol prou fiables. Finalment, les estratègies de guiatge que fins i tot avui en dia permetrien satisfer amb exactitud restriccions de temps de sobrevol, no estan dissenyades específicament per minimitzar l’impacte ambiental. Aquesta tesi té com a objectiu explorar algoritmes de d'optimització ràpids i robustos que permetin actualitzar la trajectòria òptima en temps real durant l'execució del descens, satisfent al mateix temps restriccions de temps de sobrevol; també s'investigaran nous conceptes de que permetin generar models de vent molt exactes a partir d'observacions emeses per aeronaus veïnes; i estratègies de guiatge més intel·ligents que minimitzin l'impacte ambiental de les operacions de descens continu subjectes a restriccions de temps de sobrevol. En primer lloc, es quantifica la finestra de temps disponible al punt on s'aplica la restricció de temps de sobrevol, en funció dels estats de l'aeronau (altitud, velocitat i distància al punt) i assumint que els motors es mantenen ralentí i que no s'utilitzen aerofrens durant tot el descens. Els resultats de l'experiment indiquen que es podrien utilitzar estratègies de guiatge que gestionessin l'energia cinètica i potencial de l'aeronau per satisfer restriccions de temps sense necessitat de gastar més combustible. A continuació, es compararen quatre d'aquestes estratègies. Els resultats d'aquests segon experiment indiquen que el control predictiu, una estratègia que contínuament actualitza la trajectòria òptima durant el descens, es molt robusta en termes d'errors de temps i energia, i que també redueix l'impacte ambiental. Malauradament, es tarda massa a actualitzar la trajectòria òptima cada cop que s’actualitza, fet que limita la implementació d'aquesta estratègia. Per tal d'afrontar aquesta limitació, es proposa una variant que utilitza sensitivitats paramètriques per reduir el temps d'execució a l'hora d'actualitzar la trajectòria òptima, sense degradar significativament la seva exactitud. Finalment, s'investiguen els possibles beneficis d'aprofitar observacions de vent enviades per les aeronaus del volant per millorar el model de vent i, conseqüentment, l'exactitud de la trajectòria calculada. Resultats prometedors demostren que si s’implementés model predictiu com a estratègia de guiatge i les aeronaus cooperessin per compartir observacions de vent, es reduiria l'impacte ambiental sense degradar la capacitat de l'aeroport.Postprint (published version

Predictive Control Strategies for Automotive Engine Coldstart Emissions

In this study, a comprehensive investigation is carried out to study the effectiveness of model-based predictive control strategies to solve a formidable automotive control problem, that is, reducing the amount of cumulative hydrocarbon (HC) tailpipe emissions or HCcum over the first few minutes of an automotive engine operation which is known as the coldstart period. More than 80% of the total HC emissions for a typical driving cycle are generated during the coldstart period. There is a physical trade-off between increasing the exhaust gas temperature (Texh) and reducing engine-out hydrocarbon emission (HCraw-c), which are two key variables affecting the engine performance during the coldstart operation. The design of an effective coldstart controller is associated with lots of difficulties because the behavior of the engine in the coldstart period is highly transient, uncertain, and nonlinear, and also, the key factors are in confliction with each other. In the light of promising reports on the performance of model predictive controllers (MPCs), here, different variants of MPCs are taken into account to find out whether they can effectively cope with the difficulties associated with the coldstart problem for a given automotive engine. The major advantage of MPCs refers to their power to handle different constraints while trying to minimize an objective function to come up with optimal controlling signals. Other than the standard version of MPCs, in this work, some novel versions of such controllers are proposed, which are best suited for the considered control problem. The considered versions of MPCs are: nonlinear MPC (NMPC), preference-based model predictive controller (PBNMPC), and receding horizon sliding controller (RHSC). Also, a powerful classical optimal controller based on the Pontryagin’s minimum principle (PMP) is taken into account to ascertain the veracity of the considered predictive controlling methods. Through an exhaustive simulation, the efficacy of proposed predictive controlling techniques is demonstrated, and also, it is indicated how well such controllers can optimize the related objective function at the heart of coldstart control problem while handling a set of the operating constraints