Miguel Ayala Botto

Simultaneous state and input estimation of hybrid systems with unknown inputs

This paper addresses the problem of the simultaneous state and input estimation for hybrid systems when subject to input disturbances. The proposed algorithm is based on the moving horizon estimation (MHE) method and uses mixed logical dynamical (MLD) systems as equivalent representations of piecewise affine (PWA) systems. So far the MHE method has been successfully applied for the state estimation of linear, hybrid, and nonlinear systems. The proposed extension of the MHE algorithm enables the estimation of unknown inputs, or disturbances, acting on the hybrid system. The new algorithm is shown to improve the convergence characteristics of the MHE method by reducing the delay of convergent estimates, while assuring convergence for every possible sequence of input disturbances. To ensure convergence the system is required to be incrementally input observable, which is an extension to the classical incremental observability property.

Modeling of Flexible Beams for Robotic Manipulators

This work treats the problem of modeling robotic manipulators withstructural flexibility. A mathematical model of a planarmanipulator with a single flexible link is developed. This modelis capable of reproducing nonlinear dynamic effects, such as thebeam stiffening due to the centrifugal forces induced by therotation of the joints, giving it the capability to predictreliable dynamic behaviors for a wide range of applications. Onthe other hand, the model complexity is reduced, in order to keepit amenable for analysis and controller design. The models foundin current literature for control design of flexible manipulatorarms present dynamic limitations for the sake of real timeimplementation in a control scheme. These limitations are theresult of premature linearizations in the formulation of thedynamics equations. In this paper, these common linearizations arepresented and their dynamic limitations uncovered. An alternativereliable model is then presented. The model is founded on twobasic assumptions: inextensibility of the neutral fiber, andmoderate rotations of the cross sections in order to account forthe foreshortening of the beam due to bending. Simulation andexperimental results show that the proposed model has the closestdynamic behavior to the real beam.

Predictive control based on neural network models with I/O feedback linearization

This paper presents an approach for the constrained non-linear predictive control problem based on the input-output feedback linearization (IOFL) of a general non-linear system modelled by a discrete-time affine neural network model. Using the resulting linear system in the formulation of the original non-linear predictive control problem enables to restate the optimization problem as the minimization of a quadratic function, which solution can be found using reliable and fast quadratic programming (QP) routines. However, the presence of a non-linear feedback linearizing controller maps the original linear input constraints onto non-linear and state dependent constraints on the controller output, which invalidates a direct application of QP routines. In order to cope with this problem and still be able to use QP routines, an approximate method is proposed which simultaneously guarantees a feasible solution without constraints violation over the complete prediction horizon within a finite number of steps, ...

A switching detection method based on projected subspace classification

In this paper an innovative switching detection method for piecewise linear systems is presented. The principle used for switching detection is based on finding projected subspaces from batches of input-output data, which are taken from the full data set. The method runs off-line, incrementally over all the data and, at each time, a different batch is used to compute the projected subspace. In this way, the segmentation and classification of data are entirely based on the information retrieved from the projected subspace, i.e. the subspace dimension and basis. The output of the method is a matrix of weights that assigns each pair of input-output measured data to the respective local system. Simulation experiments show the effectiveness of the proposed approach.

Robust stability of feedback linearised systems modelled with neural networks: dealing with uncertainty

Abstract This paper presents a systematic procedure to analyse the stability robustness to modelling errors when a neural network model is integrated in an approximate feedback linearisation control scheme. The propagation through the control loop of the structured uncertainty from the neural network model parameters enables the construction of a polytopic uncertainty description for the overall linear closed-loop system. By using computationally efficient algorithms the solution of a set of linear matrix inequalities provides a Lyapunov function for the uncertain system, therefore proving robust stability of the overall control system. A nonlinear multivariable water vessel system is chosen as the case study for the application of this control strategy.

A novel predictive control based framework for optimizing intermodal container terminal operations

Due to the increase in world-wide containerized cargo transport port authorities are facing considerable pressure to increase efficiency of existing facilities. Container vessels with 18,000 TEUs (twenty-foot equivalent units) are expected soon to create high flow peaks at container terminals. In this paper we propose a new framework for managing intermodal container terminals, based on the model predictive control methodology. A model based on queues and container categorization is used by a model predictive controller to solve the handling resource allocation problem in a container terminal in an optimal way, while respecting constraints on resource availability. The optimization of the operations is performed in an integrated way for the whole terminal rather than only for an individual subprocess. Containers are categorized into empty and full containers, and divided in classes according to their final destination. With more detailed information available, like container final destination, it is possible to establish priorities for the container flows inside the terminal. The order in which the container classes should be loaded into a carrier can now be addressed taking into account the carrier future route. The model ability to track the number of containers per class makes this framework suitable for describing terminals integrated in an intermodal transport network and a valuable tool for coordinating the transport modal shift towards a more sustainable and reliable transport. The potential of the proposed framework is illustrated with simulation studies based on a high-peak flow scenario and for a long-term scheduled scenario.

Discrete-time robust pole-placement design through global optimization

Abstract A robust pole placement controller design method is presented for discrete-time systems with parametric model uncertainties contained within known bounds. The design methodology is based on the minimization of a cost function by using genetic algorithms. It allows for a thorough assessment of robust performance in addition to robust stability. The effectiveness of this technique is shown for a real-time experimental laboratory-scale helicopter system. Copyright

Model predictive control for a sustainable transport modal split at intermodal container hubs

The increase of international commerce and the expected container vessels capacity with 18, 000 TEU (twenty-foot equivalent unit) will put a considerable pressure on container hubs. High flow peaks will appear at gateway hubs in the transport network compromising the cargo transportation towards the hinterland and decreasing the network transport capacity. Moreover, authorities are forcing transport operators to operate in more sustainable ways. For container hubs this is translated into making a preferable choice for barge and train modalities before opting for truck modality. In this work we present a framework based on Model Predictive Control (MPC) to address the so-called transport modal split problem for the outgoing cargo at container hubs. We use two features (destination and due time) to categorize the cargo present at a container hub and develop a dynamic model to make predictions of cargo volume over time. The controller decision takes into account transporting cargo towards the final destination while opting for sustainable transport modalities. The approach is able to assign cargo in advance to the existing connections at the hub in order to overcome predicted cargo peaks in the future. The framework can also be used to choose between different connection schedules. Giving decision freedom to container hubs is a step towards a synchromodal and more flexible transport network. These statements are illustrated with two simulation examples.

Setting cooperative relations among terminals at seaports using a multi-agent system

Seaports are gateways between the over sea and the hinterland commerce, where different cargo types are handled at dedicated terminals. Currently, seaports are facing traffic congestion leading to a decrease in its performance. Prior to increase the existing infrastructures in terms of transport capacity between the seaport and the hinterland it is important to improve cooperation among terminals. A multi-agent system to guarantee cooperation among terminals within a seaport is proposed in this paper. A control agent is assigned to each terminal and is responsible for the cargo assignment to the transport capacity at its disposal such that cargo arrives on time at the agreed location. Control agents solve in parallel an optimization problem formulated in accordance to the Model Predictive Control (MPC) strategy. Cooperation among control agents is established using a coordinator agent that updates the transport capacity assigned to each control agent based on the marginal costs provided by all control agents. The proposed framework does not require the exchange of private information and assumes an altruist behavior for all control agents. The proposed approach can perform similarly to a central approach. The framework performance is illustrated with simulation studies considering a seaport composed of 3 container terminals.

Robust control of dynamical systems using neural networks with input-output feedback linearization

This paper presents a control algorithm that combines three valuable features in robust and non-linear control, namely modelling using neural networks, input–output feedback linearization and LMI-based robust controller design. In the first step of the algorithm an affine description of a feedforward neural network model is derived. By performing an input–output feedback (IOF) linearization an uncertainty description of the IOF linearized system is derived based on the parametric uncertainties of the affine model. Then the LMI-based robust controller is designed by means of an optimization procedure. A key step in this procedure is the derivation of a polytopic boundary for the state-space matrices of the IOF linearized system based on the estimated parameters of the neural network and their uncertainty bounds.