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In this study, we consider a model free control technique for a class of minimum phase nonlinear systems with unknown dynamics. We propose a timescale transformation approach that can be used to estimate the drift and the high frequency gain of the output dynamics of nonlinear systems with a well de ned relative degree. The proposed controller provides an e ective output regulation mechanism that avoids the need for direct disturbance model estimation and high gain output feedback controllers. A simulation study is conducted to demonstrate the e ectiveness of the proposed approach.

In this paper, we introduce a continuous-time adaptive algorithm for online estimation of the parameters of exponentially damped sinusoidal signals. The proposed algorithm can also reconstruct the original signal from its corrupted measurements in real-time and without extra computational cost. For sufficiently slow adaptation gains, we establish the local stability of the filter and local exponential convergence of the estimated parameters to their true values. We also obtain a linear approximation of the adaptation laws which can be utilized for tuning the adaptation gains. Simulation studies are also provided to corroborate our claims.

In this study, we propose a dual-mode finite-time extremum seeking control system for the solution of real-time optimization problems for a class of unknown nonlinear dynamical systems. The technique provides fast convergence of the closed-loop to the optimum of a measured objective function in the absence of exact knowledge of the process dynamics. The finite-time approach provides a control system suitable for a large class of process control problems where exact models are not available. A simulation study is presented to demonstrate the effectiveness of the technique.

Background According to Takotsubo cardiomyopathy (TCM) diagnostic criteria, pheochromocytoma and paraganglioma (PPGL) should be excluded as secondary causes. However, it is unclear whether PPGL screening in the setting of acute TCM is reliable. We aimed to assess the prevalence of PPGL and the reliability of PPGL screening in a cohort of patients with TCM. Method: We retrospectively reviewed data from patients admitted in a tertiary hospital between August 2012 and January 2021 with a diagnosis of TCM proven by typical echocardiography findings and no acute occlusion on coronarography, who were screened for PPGL by plasma metanephrines/normetanephrines (M/NM). If the first screening was positive, a second plasma M/NM sample was used for confirmation. Time between TCM diagnosis and PPGL screening was collected. Results Among 64 patients identified with TCM, 42 underwent plasma M/NM screening. 81. 0% were female, and the mean age was 67.4 ± 9.9 years old. Ten patients (23.8%) had a positive result. Of those, 8 patients had a weakly positive result (1-2×upper limit of normal [ULN]) and 2 had a strongly positive result (>2×ULN). Of those 10 patients, 6 underwent a second screening: 5 had a normal result and 1 had a weakly positive result (plasma NM 1. 06×ULN). This patient underwent a third screening that was negative. In addition, an abdominal CT showed normal adrenal glands, and their chromogranin A levels were normal. Four patients had a positive initial PPGL screening, but they did not undergo a second screening: 3 died of causes unrelated to TCM before a second plasma M/NM sample was taken and one patient was lost to follow-up (his plasma NM was weakly positive at 1.29×ULN). They all had an abdominal CT that showed normal adrenal glands. Average time between TCM diagnosis and PPGL screening in all patients and in patients with a positive initial screening was 4.57 ± 3.91 and 3.38 ± 2.12 days, respectively. A linear regression model suggested an inverse relationship between plasma M/NM and time between TCM diagnosis and screening, with R2 = 0.2275. Conclusion TCM occurring as the initial presentation of PPGL is rare. All patients with a positive screening ended up being false positives in our study. The ratio of false positive results when screening for PPGL in the setting of acute TCM seems to be higher than in the general population, which could be explained by a hyperadrenergic state. It is unclear whether a shorter delay between TCM diagnosis and PPGL screening may be associated with more false positives. More research is needed to determine what is the optimal timing for PPGL screening in acute TCM and if higher cut-offs should be used to minimize false positive results. Presentation: No date and time listed

We consider an adaptive control problem for a homogeneous population of systems that operate in close conditions. Drawing a connection to Design of Experiments (DoE), we study an extremum seeking controller that operates the population economically by either minimizing a group cost or maximizing a group utility. The controller is formalized in full detail within a dynamic setting that extends the previous treatment. The applicability and effectiveness of the strategy is commented upon and supported through different examples. We argue that this class of control systems should be addressed as a design pattern where possible in view of its capacity to enable both simple and effective online optimizing control strategies.

This paper proposes an extremum-seeking control design that achieves finite-time stability of the optimum an unknown measured cost function. The finite-time extremum-seeking control technique is shown to achieve finite-time practical stability of the optimum of the unknown cost function. The main characteristic of the proposed extremum seeking control approach is that the target averaged system considered achieves finite-time stability of the unknown optimum. A simulation study is presented to demonstrate the effectiveness of the approach.

This manuscript proposes a distributed Newton seeking for the solution of distributed optimization problems with locally measured but unknown cost functions. The approach implements a Newton step for both the primal and dual problems that can be implemented in a completely decentralized fashion. Unlike existing techniques, no exchange of derivative information between agents is required. In addition, no explicit inversion of the Hessian information is required to generate the required Newton step. The local gradients and Hessians are estimated using a perturbation based extremum seeking control technique. A simulation study demonstrates the effectiveness of the technique.

Recent advances in machine learning and artificial intelligence have provided astounding performance gains in several domains, including computer vision, natural language processing and recommender systems, in particular when massive data is involved, and large computational power is needed. Similarly, modern control theory techniques have found very successful application in the analysis and control design of complex systems, for example in robotics and in the automotive industry. The connection between machine learning and control theory is more and more pertinent in view of the promise to surpass the potentialities of each discipline.

In this paper, we present a modification of the fast phasor extremum-seeking control for the fast optimization of a class of Wiener-Hammerstein nonlinear dynamical systems. The proposed technique provides a significant improvement of the closed-loop system's performance. This study introduces a new adaptive amplitude technique that is used to adaptively adjust the perturbation amplitude to a small predetermined value in a neighbourhood of the system's unknown optimal equilibrium. An analysis of the system demonstrates that semi-global practical stability analysis of the overall system to the unknown optimum is achieved. The effectiveness of the proposed approach is illustrated using numerical examples. The approach is also implemented for the optimal operation of a lean burn combustion system.

We design a Newton-like phasor-based Extremum Seeking Control (ESC) aimed at multi-variable nonlinear control systems. We first augment the derivative estimator underlying the phasor approach to capture the Hessian of the plant index. Then we use these estimates to steer the system along a Newton-like direction. We assess the closed-loop stability of the proposed scheme and demonstrate its effectiveness in a data center cooling scenario.

This paper proposes an extremum-seeking regulator design based on a Lie bracket averaging technique for time-varying nonlinear systems in the presence of unknown time-varying disturbance dynamics. The approach adopts a post-processing output regulation method for the regulation of nonlinear systems to the unknown minimum of a measured objective function. The stability analysis demonstrates that one can achieve practical output regulation of the unknown optimum equilibrium. A simulation study demonstrates the ability of the proposed technique to solve very general output regulation problems in the absence of exact knowledge of the process dynamics, the disturbance dynamics or a corresponding internal model.

In most adaptive control algorithms, parameter estimate errors are not guaranteed to converge to zero. This lack of convergence adversely affects the global performance of the algorithms. The effect is more pronounced in control problems where the desired reference setpoint or trajectory depends on the system's unknown parameters. This paper presents a parameter estimation routine that allows exact reconstruction of the unknown parameters in finite-time provided a given excitation condition is satisfied. The robustness of the routine to an unknown bounded disturbance or modelling error is also shown. To enhance the applicability of the finite-time (FT) identification procedure in practical situations, a novel adaptive compensator that (almost) recover the performance of the FT identifier is developed. The compensator guarantees exponential convergence of the parameter estimation error at a rate dictated by the closed-loop system's excitation. It was shown how the adaptive compensator can be used to improve upon existing adaptive controllers. The modification provided guarantees exponential stability of the parametric equilibrium provided the given PE condition is satisfied. Otherwise, the original system's closed-loop properties are preserved. The results are independent of the control structure employed. The true parameter value is obtained without requiring the measurement or computation of the velocity state vector. Moreover, the technique provides a direct solution to the problem of removing auxiliary perturbation signals when parameter convergence is achieved. The effectiveness of the proposed methods is illustrated with simulation examples. There are two major approaches to online parameter identification of nonlinear systems. The first is the identification of parameters as a part of state observer while the second deals with parameter identification as a part of controller. In the first approach, the observer is designed to provide state derivatives information and the parameters are estimated via estimation methods such as least squares method [19] and dynamic inversion [6]. The second trend of parameter identification is much more widespread, as it allows identification of systems with unstable dynamics. Algorithms in this area include parameter identification methods based on variable structure theory [22, 23] and those based on the notion of passivity [13]. In the conventional adaptive control algorithms, the focus is on the tracking of a given reference trajectory and in most cases parameter estimation errors are not guaranteed to

When faced with making a decision, it is only natural that one would aim to select the course of action which results in the “best" possible outcome. However, the ability to arrive at a decision necessarily depends upon two things: a well-defined notion of what qualities make an outcome desirable, and a previous decision1 defining to what extent it is necessary to characterize the quality of individual candidates before making a selection (i.e., a notion of when a decision is “good enough"). Whereas the first property is required for the problem to be well defined, the later is necessary for it to be tractable. The process of searching for the “best" outcome has been mathematically formalized in the framework of optimization. The typical approach is to define a scalar-valued cost function, that accepts a decision candidate as its argument, and returns a quantified measure of its quality. The decision-making process then reduces to selecting a candidate with the lowest (or highest) such measure.

Soil C is the largest C pool in forest ecosystems that contributes to C sequestration and mitigates climate change. Tree diversity enhances forest productivity, so diversifying the tree species composition, notably in managed forests, could increase the quantity of organic matter being transferred to soils and alter other soil properties relevant to the C cycle.A ten‐year‐old tree diversity experiment was used to study the effects of tree identity and diversity (functional and taxonomic) on soils. Surface (0–10 cm) mineral soil was repeatedly measured for soil C concentration, C:N ratio, pH, moisture, and temperature in twenty‐four tree species mixtures and twelve corresponding monocultures (replicated in four blocks).Soil pH, moisture, and temperature responded to tree diversity and identity. Greater productivity in above‐ and below‐ground tree components did not increase soil C concentration. Soil pH increased and soil moisture decreased with functional diversity, more specifically, when species had different growth strategies and shade tolerances. Functional identity affected soil moisture and temperature, such that tree communities with more slow‐growing and shade‐tolerant species had greater soil moisture and temperature. Higher temperature was measured in communities with broadleaf‐deciduous species compared to communities with coniferous‐evergreen species.We conclude that long‐term soil C cycling in forest plantations will likely respond to changes in soil pH, moisture, and temperature that is mediated by tree species composition, since tree species affect these soil properties through their litter quality, water uptake, and physical control of soil microclimates., Soil abiotic properties were affected by tree diversity and identity. Tree composition had indirect effects on soil carbon through soil microclimate.

Finishing is one of the fundamental steps of textile production and still, nowadays, it largely depends on empirical knowledge. Aim of finishing processes is to impart the required functional properties to the fabric and, in particular, decatizing is the process that lends the fabrics dimensional stability, enhances the luster, and improves the so-called fabric hand, corresponding to the sense of touching a textile. In order to properly treat the textile while minimizing the process energy consumption, suitable decatizing operating conditions, such as air temperature and humidity in the steaming section, have to be set. In this paper, because of the limited knowledge of certain process parameters and the difficulty of developing, implementing, and using effective a-priori process models, the problem of determining the set-points for the main controlled process variables is formulated as a constrained optimization problem and this is faced by means of a model-free approach. More precisely, we consider a constrained Extremum Seeking Control (ESC) scheme, using a geometric approach. In this preliminary study, first, we develop a Matlab-based simulation environment for the textile decatizing process and then we exploit it to design and test the ESC scheme. The in silico results confirm the effectiveness of the proposed approach.

In this paper, we propose a discrete-time version of extremum-seeking control (ESC) which can be implemented by several agents communicating over a network. We consider the problem where several agents each measure a local cost and must cooperate to minimize the total cost which is the sum of the local costs. To solve this problem, we combine a dynamic average consensus algorithm with a proportional integral ESC technique in discrete-time. The consensus algorithm provides each agent with an estimate of the total cost and this estimate is used by the ESC to minimize the total cost. The main contribution of this paper is to show that the distributed agents are able to minimize the total cost despite none of the agents directly measuring it. We provide a proof of the convergence of this algorithm and a simulation example to demonstrate its effectiveness.

In this study, we propose an extremum-seeking approach for the approximation of optimal control problems for unknown nonlinear dynamical systems. The technique combines a phasor extremum seeking co ...

The use of a model independent control approach to tackle the problem of wind farm power maximization under constant and varying free stream wind speed and direction is considered. In this paper, a time-varying extremum seeking control (TVESC) for discrete-time systems is utilized in a distributed and a collaborative manner. Each controller monitors the actions of a wind turbine and the objective is to maximize the farm wide power capture. To address this task, the distributed controllers ensure that the turbines share local information over an undirected connected communication network. Also, with the use of a discretized version of a continuous-time dynamic average consensus estimator, the controllers help the turbines estimate the mean of the overall power generated in the wind farm. The dynamics of each turbine power estimate is parameterized and the unknown gradient is estimated as a time-varying parameter using a tailored estimation routine. This information is used in the design of the extremum seeking controller. The problem to be solved is addressed via numerical simulations, results are provided to show the effectiveness of this technique.

In this paper, the solution of large-scale real-time optimization problems of multi-agent systems (MAS) is tackled in a distributed and a cooperative manner without the requirement of exact knowledge of network connectivity. Each agent in the communication network measures a local disagreement cost in addition to its local cost. The agents must work collaboratively to ensure that the system's unknown overall cost (i.e., the sum of the local cost of all the agents) is minimized. In order to minimize this cost, the local disagreement cost of all the agents must first be minimized. This minimization requires the solution of a consensus estimation problem and ensures that the agents reach agreement on their decision variables. To address this challenging problem, a distributed proportional-integral extremum seeking control technique is proposed, one that solves both problems simultaneously. Three simulation examples are included, they demonstrate the effectiveness and robustness of the proposed technique.

A cooperative extremum seeking control technique is employed to overcome the need for accurate models of aerodynamic wake interactions among wind turbines thereby providing an effective technique for farm wide power capture. In this paper, a time-varying extremum seeking control (TVESC) approach is employed in a distributed fashion to maximize the power production of an array of wind turbines under constant and varying free stream wind speeds and directions. Each wind turbine (identified as an agent) has access to the measurement of its unknown local power generation. The goal is to maximize the unknown total power capture in the wind farm. A cooperative approach is employed where the agents work together to maximize this overall objective function. This approach requires every agent to exchange information with its neighbours over an undirected connected communication network. Each agent is also expected to estimate the mean of the overall cost function and this is achieved using a dynamic average consensus estimator. The dynamics of each agent’s cost estimate is parametrized and a parameter estimation routine is used to estimate the unknown gradient information. A distributed extremum seeking controller is designed to ensure that the overall cost is maximized. This problem is tackled via numerical simulations, results are provided to show the effectiveness of this technique.

This paper considers the solution of a minmax optimization problem for a class of unknown nonlinear systems using a perturbation-based proportional-integral extremum seeking control design technique. It is assumed that the equations describing the dynamics of the maximizing and minimizing players are unknown and the cost function to be minimized is unknown but available for measurement to both players. The proposed control system is shown to stabilize the minimizing player in the presence of destabilizing attacks. A simulation example is used to illustrate the effectiveness of the proposed technique.

In this study, a distributed extremum-seeking control approach is proposed to solve a class of constrained real-time optimization problems. Each agent operates over a sensor network. The agents have access to the measurement of a local cost and local constraints which it can communicate to neighbouring agents over the network. A dynamic consensus algorithm is used to provide all agents with an estimate of the total network cost and global constraints. A local extremum seeking controller is used to manipulate local input variables. It is shown that the distributed extremum-seeking control system achieves the optimization of the total network cost.

Biodiversity affects ecosystem functioning in forests by, for example, enhancing growth and altering the forest structure towards greater complexity with cascading effects on other processes and trophic levels. Complexity in forest canopy could enhance light interception and form a link between diversity and productivity in polyculture forests, but the effect of canopy structure on light interception is rarely directly measured. We modelled the canopy surface structure of a tree diversity experiment by photographing it using unmanned aerial vehicle (UAV) and combining the photos into a digital elevation model with photogrammetry tools. We analysed the effects of tree diversity and functional diversity on canopy structural complexity and light interception with a structural equation model. Our results show that: a) increased structural complexity of the canopy reduces light interception, whereas b) tree diversity increases the structural complexity of the canopy, and has a dual impact on light interception. Tree diversity decreased light interception through the structural complexity of the canopy but increased it probably through canopy packing and crown complementarity. However, the effects of both tree diversity and structural complexity of canopy were smaller than the effect of the functional identities of the tree species, especially the differences between deciduous and evergreen trees. We conclude that more complexity in canopy structure can be gained through increased tree diversity, but complex canopy structure does not increase light interception in young forests.

This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while preserving the input dither signal. The technique incorporates a mechanism for adjusting the amplitude of the extremum seeking control dither signal. This mechanism ensures that any violation of constraints due to the dither signal is removed while maintaining the probing signal active. An amplitude update routine is also proposed. The amplitude update is coupled with a saturation bias estimation algorithm that correctly accounts for the inherent bias associated with systems operated at or near saturation conditions. The amplitude update is designed to remove the dither signal when the system approaches the optimum. It also ensures that a lower bound of the amplitude is enforced to guarantee that excitation conditions are maintained.

International Conference on Interfaces and Human Computer Interaction 2019, International Conference on Game and Entertainment Technologies 2019 and International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2019, ISBN:978-1-5108-9242-2

This manuscript proposes a distributed Newton seeking for the solution of distributed optimization problems with locally measured but unknown cost functions. The approach implements a Newton step for both the primal and dual problems that can be implemented in a completely decentralized fashion. Unlike existing techniques, no exchange of derivative information between agents is required. In addition, no explicit inversion of the Hessian information is required to generate the required Newton step. The local gradients and Hessians are estimated using a perturbation based extremum seeking control technique. A simulation study demonstrates the effectiveness of the technique.

This paper proposes a proportional-integral extremum-seeking control technique. The technique is a generalization of the standard perturbation based techniques which provides fast transient performance of the closed-loop system to the optimum equilibrium of a measured cost function. Under the assumption that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown, it is shown that, for a class of minimum-phase nonlinear system with a cost function of relative degree one, the closed-loop ESC can practically stabilize the unknown equilibrium optimum. The stabilization result provides an alternative approach to output feedback control of nonlinear systems.The effectiveness of the proposed approach is demonstrated using simulation examples.

Two real-time nonlinear model predictive control (NMPC) algorithms for a transport–reaction system are designed. The system is modeled by a hyperbolic partial differential equation and discretized by means of a two-time-level semi-implicit semi-Lagrangian scheme. For the resulting lumped-parameter system, a constrained optimal control problem is formulated and state constraints are implemented in the form of barrier functions. The NMPC algorithms perform a single step or several steps of an iterative solution routine of the optimal control problem at every sampling point. With this suboptimal solution strategy, a fixed maximum evaluation time and execution in real time are guaranteed. An analysis of the nominal stability is provided for one NMPC scheme. The robustness of the controllers is evaluated for an example problem, where a nonisothermal plug-flow reactor with irreversible exothermic reactions is considered. The control objectives are to limit the maximum reactor temperature (avoid hot spots) and t...