João Miranda Lemos

Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler

Abstract This paper describes the application of the MUSMAR predictive adaptive controller to the regulation of super heated steam temperature in a commercial boiler. The boiler considered produces 150 t / h of steam at maximum load, used both for electric energy production in a turbine and industrial use. The combination of predictive and adaptive techniques, relying on multiple models redundantly estimated, allows a continuous adjustment of the controller tuning for tracking plant dynamics variations. This paper describes experiments actually performed on the plant with adaptive predictive control, in particular in the presence of load changes. A reduction of steam temperature fluctuations with respect to an optimized cascade of PI controllers is observed.

Tracking Groups of Pedestrians in Video Sequences

This paper describes an algorithm for tracking groups of objects in video sequences. The main difficulties addressed in this work concern total occlusions of the objects to be tracked as well as group merging and splitting. A two layer solution is proposed to overcome these difficulties. The first layer produces a set of spatio temporal strokes based on low level operations which manage to track the active regions most of the time. The second layer performs a consistent labeling of the detected segments using a statistical model based on Bayesian networks. The Bayesian network is recursively computed during the tracking operation and allows the update of the tracker results everytime new information is available. Experimental tests are included to show the performance of the algorithm in ambiguous situations.

Multipredictive adaptive control of arc welding trailing centerline temperature

This application paper addresses the use of adaptive predictive control on arc welding trailing centerline temperature control. For tackling the high level of uncertainty in the process the multivariable multipredictive adaptive regulator (MUSMAR) adaptive algorithm, relying on separate estimation of predictive models is used. Experimental results presented include characterization of plant uncertainty and of the effect in control performance of various available knobs, in particular in the presence of plates with variable geometry.

Diode/magnetic tunnel junction cell for fully scalable matrix-based biochip

Magnetoresistive biochips have been recently introduced for the detection of biomolecular recognition. In this work, the detection site incorporates a thin-film diode in series with a magnetic tunnel junction (MTJ), leading to a matrix-based biochip that can be easily scaled up to screen large numbers of different target analytes. The fabricated 16×16 cell matrix integrates hydrogenated amorphous silicon (a-Si:H) diodes with aluminum oxide barrier MTJ. Each detection site also includes a U-shaped current line for magnetically assisted target concentration at probe sites. The biochip is being integrated in a portable, credit card size electronics control platform. Detection of 250nm diameter magnetic nanoparticles by one of the matrix cells is demonstrated.

ADAPTIVE AND NON-ADAPTIVE MODEL PREDICTIVE CONTROL OF AN IRRIGATION CHANNEL

The performance achieved with both adaptive and non-adaptive Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the different channel pools to multivariable control using only proximal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptive version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and downstream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown.

Adaptive Receding Horizon Control of a Distributed Collector Solar Field

This paper presents an adaptive receding horizon control algorithm for a distributed collector solar field which explicitly explores its distributed parameter character. The plant considered is a distributed collector solar field, being described by a nonlinear hyperbolic partial differential equation (PDE) which models the temperature dynamics. A lumped parameter model is obained by applying Orthogonal Collocation. This model is then used as a basis for controller design. Stability is ensured for the lumped parameter model by resorting to Control Lyapunov function methods. Simulation results using a detailed physically based simulator of the solar field are provided.

Observer based nonuniform sampling predictive controller for a solar plant

Abstract The work presented in this paper exploits the transport characteristic of a solar plant where the transport velocity (a flow) is the manipulated variable, i.e. the control input. The solar field is modelled by a partial differential equation. A non-uniform sampling in time is performed in order to obtain a discrete linear model. Due to the transport dynamics of the plant the resulting transfer function has a finite impulse response and the optimal control derived from a black-box approach of such a systems yield pure feed-forward compensators. The main contribution of this paper is the use of a state-space description of the plant in conjunction with the nonuniform sampling that allows to introduce the feedback mechanism through the state observer. The controller results from the optimization of a multi-step quadratic cost function. Experimental results performed with the solar plant are shown.

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

In biomedical systems, feedback control can be applied whenever adequate sensors, actuators, and sufficiently accurate mathematical models are available. The key issue is the capacity of the control algorithm to tackle the large levels of uncertainty, both structured and unstructured, associated with patient dynamics. In the particular case of intravenous anesthesia considered here, manipulated variables are drug infusion rates, administered by syringe pumps, and the measured signal outputs are the levels of hypnosis or depth of anesthesia (DoA) and of neuromuscular blockade (NMB).

Long-range adaptive control with input constraints

Abstract A robust long-range adaptive controller incorporating soft input constraints is considered. For the regulation case, the convergence of the algorithm is illustrated by means of simulations on ARMAX plants with unmodelled dynamics. An example of application is presented for the control of neuromuscular blockade induced by short-acting relaxants in patients under surgery.

Modeling and Classifying Human Activities From Trajectories Using a Class of Space-Varying Parametric Motion Fields

Many approaches to trajectory analysis, such as clustering or classification, use probabilistic generative models, thus not requiring trajectory alignment/registration. Switched linear dynamical models (e.g., HMMs) have been used in this context, due to their ability to describe different motion regimes. However, these models are not suitable for handling space-dependent dynamics that are more naturally captured by nonlinear models. As is well known, these are more difficult to identify. In this paper, we propose a new way of modeling trajectories, based on a mixture of parametric motion vector fields that depend on a small number of parameters. Switching among these fields follows a probabilistic mechanism, characterized by a field of stochastic matrices. This approach allows representing a wide variety of trajectories and modeling space-dependent behaviors without using global nonlinear dynamical models. Experimental evaluation is conducted in both synthetic and real scenarios. The latter concerning with human trajectory modeling for activity classification, a central task in video surveillance.