Actuator constraints, particularly saturation limits, pose significant challenges in control system implementation, especially for uncertain systems. This paper proposes a novel anti-windup scheme in which an adaptive observer is used to estimate linear parameter uncertainty in the system. The proposed compensator retains the architecture of established non-adaptive schemes, such as Model Recovery Anti-windup, but uses the observer to deal with uncertainty. If the uncertainty is estimated precisely, the system effectively reverts to the Model Recovery Anti-windup framework. The main result establishes conditions under which, if the ideal control signal eventually returns to a level within constraints, the system states will asymptotically converge to those of the nominal system, ensuring recovery of the nominal closed-loop behavior.

Traditional models used in nuclear reactor power modeling face challenges related to system parameter uncertainties, loss of dynamic information due to model simplifications or linearizations, and, in some cases, lack of proper rod calibration for accurate input modeling. This article presents a predictive design for the power of the IAN-R1 nuclear reactor in Colombia, based on the Koopman operator approach using Extended Dynamic Mode Decomposition (EDMD). Since this model relies solely on system measurements, it not only addresses the aforementioned issues but also provides a system that is easily adaptable to changes in core configuration or parameter modifications. The poles of the Koopman model are compared with those of other models to validate its operation, and the results are contrasted with real data on the operation of the reactor, evidencing the good performance of the proposed model.

Fault Detection and Isolation (FDI) is of great interest for the control community since it can drive improved performance in a system by allowing predictive maintenance/repairing and catering for improved operational safety. Fault Detection and Isolation in large-scale smelting furnaces presents several challenges, as it requires the understanding of complex thermal and chemical reactions occurring inside the structure. Furthermore, the impossibility of having full operational information about the process makes the use of model-based methods very complex or unfeasible. This paper introduces a methodology to develop a Data-Driven FDI system for the detection of incipient and intermittent failures in a network made out of 322 thermocouples located on the shell of the furnace. Statistical metrics over Fault Counter Time Windows (FTCW) were used to identify different types of sensor failures, which led to establishing a baseline of known failure events and to create a dataset to train the Machine Learning (ML) classification models. A data-driven approach was proposed based on the sensors physical (neighbouring) redundancy, which led to some type of physical redundancy. A post-processing stage was used to stabilize the model’s response in time, determining that the proposed FDI system successfully detects faults whilst reducing reported false negatives.

Actuator constraints, particularly saturation limits, are an intrinsic and long-standing problem in the implementation of most control systems. Model reference adaptive control (MRAC) is no exception and it may suffer considerably when actuator saturation is encountered. With this in mind, this paper proposes an anti-windup strategy for model reference adaptive control schemes subject to actuator saturation. A prominent feature of the proposed compensator is that it has the same architecture as well-known nonadaptive schemes, namely model recovery anti-windup, which rely on the assumption that the system model is known accurately. Since, in the adaptive case, the model is largely unknown, the proposed approach uses an "estimate" of the system matrices for the anti-windup formulation and modifies the adaptation laws that update the controller gains; if the (unknown) ideal control gains are reached, the model recovery anti-windup formulation is recovered. The main results provide conditions under which, if the ideal control signal eventually lies within the control constraints, then the system states will converge to those of the reference model, that is, the tracking error will converge to zero asymptotically. The article deals with open-loop stable linear systems and highlights the main challenges involved in the design of anti-windup compensators for model-reference adaptive control systems, demonstrating its success via a flight control application.

Calcines' chemical composition analysis is a key process in ferronickel smelting. These values allow for a clear understanding of the smelted product's expected quality, catering for any required chemical upgrading of the raw material or modification in the furnace's set-point if the calcine has undesired characteristics. Offline tests for calcines' chemical composition can take several days, potentially delaying the whole operation. A data-driven approach to chemical composition classification using on-line data is proposed by combining clustering classification through a mixed Principal Component Analysis (PCA) model, data processing and standardization process, with a Machine Learning classification algorithm, i.e. Extreme Gradient Boosting (XGBoost). This allows for an online prediction of calcines' chemical composition based on the furnace's current operating conditions. The proposed method's accuracy scored mean values between 82.1% and 85.9%, which is encouraging in comparison with other proposed methods.

The mining industry has overcome many challenges in recent years with the help of cutting-edge technologies such as smart sensors, structural health monitoring, and AI-powered structural control, amongst others.These tools have revolutionized problem-solving with large amounts of data, allowing for automation and online monitoring of large scale inraestructure, in this particular case eletric arc metal smelting furnaces.Among the multiple relevant elements, furnaces play a crucial role in the smelting process as they are responsible for melting down the raw materials and separating the metal from the impurities, but operate in very harsh conditions, making its monitoring and safe operation a difficult task.Without furnaces, it would be impossible to extract metals like ferronickel, iron, copper, and aluminum from their ores.The temperature and conditions inside the furnace must be carefully controlled to ensure that the metal is of the desired quality and purity.Furnaces are made of refractory materials that can withstand high temperatures, however, these furnaces face significant challenges, including abrasive materials, hign radiation levels, and high temperatures, leading to wear and tear on the refractory materials, which in turn may induce accidents that may be catastrophic.To prevent such incidents, it is essential to monitor the thickness of the refractory walls and the temperature in the furnace's thermocouples; however, to have a direct measurements of the thickness of the refractory wall is not easy work because the operational conditions inside of the furnace do not allow for instrumentation.By developing and validating a thickness prediction model based on historical data and Machine Learnig technniques, we can accurately monitor a furnace's state for up to 11 days from initial operating conditions, allowing for additional insight that may significantly extend the furnace's operating window.The model was created using data from a furnace from Cerromato S.A., a ferronickel producer in the north of Colombia, which is one of the world's biggest producers of ferronickel.

This paper proposes a dynamic anti-windup scheme for a class of iterative learning control (ILC) systems. The anti-windup compensator has the same structure as a class of compensators for 1D systems and is able to guarantee similar properties: (i) that the constrained system with anti-windup compensation is exponentially stable if a certain linear matrix inequality is satisfied; and (ii) if the trajectory to be tracked by the nominal ILC controller is consistent with the control constraints, the anti-windup compensator will ensure that the behaviour of the nominal ILC controller is eventually recovered.

Interconnected systems are widespread in modern technological systems. Designing a reliable control strategy requires modeling and analysis of the system, which can be a complicated, or even impossible, task in some cases. However, current technological developments in data sensing, processing, and storage make data-driven control techniques an appealing alternative solution. In this work, a design methodology of a decentralized control strategy is developed for interconnected systems based only on local and interconnection time series. Then, the optimization problem associated with the predictive control design is defined. Finally, an extension to interconnected systems coupled through their input signals is discussed. Simulations of two coupled Duffing oscillators, a bipedal locomotion model, and a four water tank system show the effectiveness of the approach.

Being able to predict future temperatures on the wall lining is key when controlling and scheduling maintenance for large industrial smelting furnaces. In this paper, we propose and test a machine learning approach for predicting lining temperatures in a ferronickel smelting furnace. This approach was deployed and evaluated in a real-world scenario, i.e., in one of Cerro Matoso S.A.'s (CMSA) industrial plant furnaces. Different techniques were tested, and finally, a multitarget regression (MTR) model showed the best performance. Previous state of the art focused on predicting only one target sensor; in contrast, our model is capable of predicting up to 12 targets. Two MTR models were tested: the incremental structured output prediction tree (iSOUP-Tree) and the stacked single-target Hoeffding tree regressor (SST-HT). The SST-HT method had the best behavior in terms of the average mean absolute error (AMAE) and average root mean square error (ARMSE). The results indicate that the developed MTR models can accurately predict the measured temperature on multiple point sensors. Results of this work are expected to help the process of structural control and health monitoring of the furnace linings located at CMSA's plant.

This paper proposes an anti-windup mechanism for a model reference adaptive control scheme subject to actuator saturation constraints. The proposed compensator has the same architecture as well known non-adaptive schemes, which rely on the assumption that the system model is known fairly accurately. This is in contrast to the adaptive nature of the controller, which assumes that the system (or parts of it) is unknown. The approach proposed here uses of an "estimate" of the system matrices for the anti-windup compensator formulation and modifies the adaptation laws that update the controller gains. It will be observed that if the (unknown) ideal control gain is reached, a type of "model recovery anti-windup" formulation is obtained. In addition, it is shown that if the ideal control signal eventually lies within the control constraints, then, under certain conditions, the system states will converge to those of the reference model as desired. The paper highlights the main challenges involved in the design of anti-windup compensators for model-reference adaptive control systems and demonstrates its success via a flight control simulation.

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.

Adaptive synchronization protocols for heterogeneous multi-agent network are investigated. The interaction between each of the agents is carried out through a directed graph. We highlight the lack of communication between agents and the presence of uncertainties in each system among the conventional problems that can arise in cooperative networks. Two methodologies are presented to deal with the uncertainties: A strategy based on robust optimal control and a strategy based on neural networks. Likewise, an input estimation methodology is designed to face the disconnection that any agent may present on the network. These control laws can guarantee synchronization between agents even when there are disturbances or no communication from any agent. Stability and boundary analyzes are performed. Cooperative cruise control simulation results are shown to validate the performance of the proposed control methods.

This paper presents a distributed adaptive control law for large-scale systems with unknown interconnection parameters. An adaptive control law is designed to follow-up a model reference for a network through a controller that adjusts its parameters according to the dynamics of the reference, the neighborhood and the physical interconnection. This work presents a Model Reference Adaptive Control methodology for heterogeneous systems, such that the synchronization of the agents in the network is achieved even in the case where the interconnection is unknown. Stability properties of the proposed control law are validated via Lyapunov methods and boundedness of the synchronization errors is guaranteed. The authors propose a validation scheme of adaptive control for different references in a context of level control in tank networks and a synchronization analysis of the estimated constants.

This paper proposes an anti-windup like scheme for an LTI plant with rate-limits. The plant is controlled using a model-reference adaptive controller, making the anti-windup design problem highly nonlinear. It is assumed that the rate-limit is modelled as a first order feedback loop for which the state is unavailable, but that the bandwidth of this loop is known. The anti-windup scheme uses a “hedging” term and a “positive μ” term. The structure of the problem makes the rate-limit case considerably more difficult than the magnitude limit case. Nevertheless it is proved that convergence of the system state to the ideal model can be accomplished under conditions similar to those found in anti-windup compensation for purely linear systems.

This document shows a dwell time-based switching technique for affine linear systems. The affine system is defined as a parameter dependent homotopic combination of two base modes, where the intermediate modes create an “artificial” grid of subsystems, producing lower dwell times estimates, specially when the number of modes increases. In some practical applications, the interpolated modes may replace previously designed or existing modes over a defined variable or range. Finsler's lemma is used to develop a relaxed Lyapunov-based condition that ensures the stability of the switched system and reduces the computation time of the developed technique. A user-defined parameter is used to influence the dwell time estimation. Numerical calculations performed over a switched system derived from an adaptive vibration attenuation controller shows the effectiveness of the proposed algorithm.

The cooperative control applied to vehicles allows the optimization of traffic on the roads. There are many aspects to consider in the case of the operation of autonomous vehicles on highways since there are different external parameters that can be involved in the analysis of a network. In this paper, we present the design and simulation of adaptive control for a platoon with heterogeneous vehicles, taking into account that not all vehicles can communicate their control input, and in turn include structured nonlinear uncertainty input parameters.

This paper presents a distributed output regulation algorithm for the leader-follower heterogeneous multi-agent system with unknown leader dynamics. The unknown nonlinear dynamics of the leader agent are reconstructed based on a data-driven transformation that lifts the nonlinear dynamics to a linear space that approximates it. An adaptive distributed observer algorithm is designed to estimate the leader states and its linear model approximation for each follower agent, since it is considered that the leader communicates only to a subset of followers. To design the output synchronization protocol, a local state feedback control is developed for each agent. The proposed algorithm is validated through simulation studies.

This paper develops an anti-windup scheme for stable linear systems subject to rate-limited control inputs. The main contribution of the paper is a simple condition that guarantees the existence of a family of globally stabilising anti-windup compensators. The approach is a natural development of an existing, tried-and-tested scheme, with the supplemental property of global asymptotic stability. It is also shown that IMC-like anti-windup compensators appear as special cases of the more general design procedure and, moreover, that these compensators guarantee external, as well as internal, stability. An example illustrates the results.

This paper presents the Vortex Particle Swarm Optimization (VPSO) algorithm and some considerations for the appropriate selection of parameters. The optimization algorithm is composed of two modes: translational and circular movements. Convergence of the swarm to a given optimal point is performed by linear movements, while the exploration stage is characterized by circular movements (i.e. a vortex-like behavior). These emerging behaviors are observed in different living organisms in nature and are a product of swarm (social) interactions. Particularly in the proposed algorithm, the vortex-like behavior allows escaping from local minima. Parameter selection is proposed based on an approximate analysis of the swarm behavior, and the algorithm performance is studied via simulation results of well-known test functions.

There exist a wide range of techniques that generate collision-free (optimal) trajectories, in an autonomous fashion, for mobile robotics. Probably, the most popular technique is that of artificial potential fields, where the robot is treated as a particle subject to a potential field that is generated by the obstacles and the goal position. The path generation problem is then treated as an optimization problem where gradient descent methods have been traditionally used. Particle swarm optimization has also been widely used to solve optimization problems, and although it has proven to be more efficient in the search of minima, it also suffers from early convergence, i.e. the swarm may get trapped in a local minima. Many of the modifications that cater for this weakness involve added complexity in the construction of the potential field and thus translates into ad-hoc algorithms. A particle swarm approach with the property of escaping local minima by forcing vortex-like dynamics when the gradient of the potential field is close to zero is proposed, proving to be more compact and intuitive than previously proposed algorithms.