Control Point Of View Research Articles

On dynamical system modeling of learned primal-dual with a linear operator K : stability and convergence properties

Learned Primal-Dual (LPD) is a deep learning based method for composite optimization problems that is based on unrolling/unfolding the primal-dual hybrid gradient algorithm. While achieving great successes in applications, the mathematical interpretation of LPD as a truncated iterative scheme is not necessarily sufficient to fully understand its properties. In this paper, we study the LPD with a general linear operator. We model the forward propagation of LPD as a system of difference equations and a system of differential equations in discrete- and continuous-time settings (for primal and dual variables/trajectories), which are named discrete-time LPD and continuous-time LPD, respectively. Forward analyses such as stabilities and the convergence of the state variables of the discrete-time LPD to the solution of continuous-time LPD are given. Moreover, we analyze the learning problems with/without regularization terms of both discrete-time and continuous-time LPD from the optimal control viewpoint. We prove convergence results of their optimal solutions with respect to the network state initialization and training data, showing in some sense the topological stability of the learning problems. We also establish convergence from the solution of the discrete-time LPD learning problem to that of the continuous-time LPD learning problem through a piecewise linear extension, under some appropriate assumptions on the space of learnable parameters. This study demonstrates theoretically the robustness of the LPD structure and the associated training process, and can induce some future research and applications.

Voltage Support With PV Inverters in Low-Voltage Distribution Networks: An Overview

Large solar photovoltaic (PV) penetration using inverters in low voltage (LV) distribution networks may pose several challenges, such as reverse power flow and voltage rise situations. These challenges will eventually force grid operators to carry out grid reinforcement to ensure continued safe and reliable operations. However, smart inverters with reactive power control capability enable PV systems to support voltage quality in the distribution network better. This paper gives an overview of the current state-of-the-art control strategies for handling voltage problems through PV inverters and other devices. In addition, the (control) technical issues of PV systems integrated into the LV distribution network are considered from a control point of view. By comparing the control issues of PV integration into the grid, the paper aims to help distribution system operators to expand the volume of PV generation in the distribution system in an efficient and safe manner. Additionally, it will help control engineers and researchers select proper control strategies for PV systems as well as other distributed renewable sources.

Stability and control of VSC-based HVDC systems: A systematic review

The technological development in the area of power electronics has paved the way for the construction of High Voltage Direct Current (HVDC) systems. The utilization of HVDC grids alongside conventional High Voltage Alternating Current (HVAC) grids poses several challenges, especially, from stability and control points of view. Indeed, moving towards such systems in the context of conventional Alternating Current (AC) power systems cannot be possible without ensuring the overall stability of hybrid HVAC/HVDC grids. This paper analyzes different aspects of the stability of Voltage Source Converter (VSC)-based HVDC grids and presents various methods of improving stability based on a systematic and comprehensive review. In addition, this paper provides a concise classification of various control methods to improve the operation of such grids and the advantages of each method.

Wykorzystanie rachunku niecałkowitego rzędu w sterowaniu suwnicą 3D

The 3D crane is an object used in various industries. From a control point of view, it is a dynamic, non-linear and multidimensional system. In this paper, two control systems are designed: with a classical PID controller and with a fractional-order PID controller. A comparative analysis of the designed control systems is presented.

Extended State Observer Based Robust Feedback Linearization Control Applied to an Industrial CSTR

In the chemical and petrochemical industries, the Continuous Stirred Tank Reactor (CSTR) is, without doubt, one of the most popular processes. From a control point of view, the mathematical model describing the temporal evolution of the CSTR has a strongly nonlinear cross-coupled character. Moreover, modeling errors such as external disturbances, neglected dynamics, and parameter variations or uncertainties make its control task a very difficult challenge. This problem has been the subject of a wide number of control strategies. This article attempts to propose a viable, robust nonlinear decoupling control scheme. The idea behind the proposed approach lies in the design of two nested control loops. The inner loop is responsible for the compensation of the nominal model's nonlinear cross-coupled terms via a static nonlinear feedback; while the outer loop, designed around an Extended State Observer (ESO), which the additional state gathers the global effect of modeling errors, is charged with instantaneously estimating and then compensating the ESO extended state. This way, the CSTR complex dynamics are reduced to a series of decoupled linear subsystems easily controllable using a simple Proportional-Integral (PI) linear control to ensure the robust pursuit of reference signals respecting the desired performance. The presented control validation was performed numerically by an objective comparison to a classical PID controller.

On the Optimization Landscape of Dynamic Output Feedback Linear Quadratic Control

The convergence of policy gradient algorithms hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into these algorithms can often be acquired from analyzing those of linear quadratic control. However, most of the existing literature only considers the optimization landscape for static full-state or output feedback policies (controllers). We investigate the more challenging case of dynamic output-feedback policies for linear quadratic regulation (abbreviated as dLQR), which is prevalent in practice but has a rather complicated optimization landscape. We first show how the dLQR cost varies with the coordinate transformation of the dynamic controller and then derive the optimal transformation for a given observable stabilizing controller. One of our core results is the uniqueness of the stationary point of dLQR when it is observable, which provides an optimality certificate for solving dynamic controllers using policy gradient methods. Moreover, we establish conditions under which dLQR and linear quadratic Gaussian control are equivalent, thus providing a unified viewpoint of optimal control of both deterministic and stochastic linear systems. These results further shed light on designing policy gradient algorithms for more general decision-making problems with partially observed information.

Shearing Work Analysis and Control Design of Rotary Shears in Material Processing Lines

Abstract Rotary shears are common part of the material processing lines (MPL). During the operation, these shears are loaded with impact cutting torque, which takes only short time but reaches values compared to motor-rated torque. Therefore, it is a technical challenge to ensure the speed stability during the cut. The presented paper deals with the analysis of the rotary shears’ operation and material cutting process from the control point of view and presents a cutting torque compensation possibility. Three types of speed controllers without and with cutting torque compensation are compared.

Affordances from a control viewpoint

ABSTRACT Perceiving an armchair prepares us to sit. Reading the first line in a text prepares us to read it. This article proposes that the affordance construct used to explain reactive potentiation of behavior similarly applies to reactive potentiation of cognitive actions. It defends furthermore that, in both cases, affordance-sensings do not only apply to selective (dis)engagement, but also to the revision and the termination of actions. In the first section, characteristics of environmental affordance-sensings such as directness, stability, action potentiation, valence, and phenomenology are reexamined in light of contemporary cognitive science. In the second section, it is proposed that cognitive affordance-sensings can also be characterized along these dimensions. Called “metacognitive feelings” in the metacognitive literature, their function is to select, engage, revise and post-evaluate cognitive actions. A third section discusses alternative views, and responds to objections.

Research on Robotic Flexible Peg-in-hole Assembly Technology

Peg-in-hole assembly is one of the more typical tasks in the machining industry. Although robotic peg-in-hole assembly technology has been widely used in practical production, achieving efficient and reliable flexible peg-in-hole assembly by robots still poses significant challenges. The analysis of robotic flexible peg-in-hole assembly from a control point of view is presented. Firstly, the process of robot flexible peg-in-hole assembly is introduced. Then the application of traditional model-based assembly control is described, on the basis of which learning-based intelligent assembly control is discussed. The combination of intelligent methods and traditional methods will become an important future development trend, injecting new vitality into robotic flexible peg-in-hole assembly.

Detailed assessment of hybrid ventilation control system in a mixed-mode building in cold climate

This paper investigates a hybrid ventilation control method for a mixed-mode office landscape in a cold climate, i.e. Norway. The mixed-mode building utilizes a hybrid ventilation system that combines natural ventilation through automatic window opening with a balanced mechanical ventilation system controlled by demand control ventilation (DCV) method. For natural ventilation, the windows consist of two parts: the upper part which is openable and the lower part that is not openable and equipped with external solar shading. From a control point of view, the article elaborates a control algorithm for the proposed hybrid ventilation based on various parameters including outdoor air temperature, indoor air temperature, CO2 level, maximum air change per hour (ACH), operation schedules, and heating/cooling setpoints. The simulation results obtained from the hybrid ventilation and mechanical ventilation cases are compared based on thermal comfort, CO2 level, and energy savings. The primary results show that the proposed hybrid ventilation control method can satisfy the thermal comfort and CO2 level requirements while reducing the energy use for fan and ventilation cooling by approximately 55% and 45%, respectively. While the impact on energy consumption for space and ventilation heating is negligible.

Stability of oscillator Ising machines: Not all solutions are created equal

Nonlinear dynamical systems such as coupled oscillators are being actively investigated as Ising machines for solving computationally hard problems in combinatorial optimization. Prior works have established the equivalence between the global minima of the cost function describing the coupled oscillator system and the ground state of the Ising Hamiltonian. However, the properties of the oscillator Ising machine (OIM) from a nonlinear control viewpoint, such as the stability of the OIM solutions, remain unexplored. Therefore, in this work, using nonlinear control-theoretic analysis, we (i) identify the conditions required to ensure the functionality of the coupled oscillators as an Ising machine, (ii) show that all globally optimal phase configurations may not always be stable, resulting in some configurations being more favored over others and, thus, creating a biased OIM, and (iii) elucidate the impact of the stability of locally optimal phase configurations on the quality of the solution computed by the system. Our work, fostered through the unique convergence between nonlinear control theory and analog systems for computing, provides a new toolbox for the design and implementation of dynamical system-based computing platforms.

Autologous and Allogeneic Cytotherapies for Large Knee (Osteo)Chondral Defects: Manufacturing Process Benchmarking and Parallel Functional Qualification.

Cytotherapies are often necessary for the management of symptomatic large knee (osteo)-chondral defects. While autologous chondrocyte implantation (ACI) has been clinically used for 30 years, allogeneic cells (clinical-grade FE002 primary chondroprogenitors) have been investigated in translational settings (Swiss progenitor cell transplantation program). The aim of this study was to comparatively assess autologous and allogeneic approaches (quality, safety, functional attributes) to cell-based knee chondrotherapies developed for clinical use. Protocol benchmarking from a manufacturing process and control viewpoint enabled us to highlight the respective advantages and risks. Safety data (telomerase and soft agarose colony formation assays, high passage cell senescence) and risk analyses were reported for the allogeneic FE002 cellular active substance in preparation for an autologous to allogeneic clinical protocol transposition. Validation results on autologous bioengineered grafts (autologous chondrocyte-bearing Chondro-Gide scaffolds) confirmed significant chondrogenic induction (COL2 and ACAN upregulation, extracellular matrix synthesis) after 2 weeks of co-culture. Allogeneic grafts (bearing FE002 primary chondroprogenitors) displayed comparable endpoint quality and functionality attributes. Parameters of translational relevance (transport medium, finished product suturability) were validated for the allogeneic protocol. Notably, the process-based benchmarking of both approaches highlighted the key advantages of allogeneic FE002 cell-bearing grafts (reduced cellular variability, enhanced process standardization, rationalized logistical and clinical pathways). Overall, this study built on our robust knowledge and local experience with ACI (long-term safety and efficacy), setting an appropriate standard for further clinical investigations into allogeneic progenitor cell-based orthopedic protocols.

Sampling Gaussian stationary random fields: A stochastic realization approach

Generating large-scale samples of stationary random fields is of great importance in the fields such as geomaterial modeling and uncertainty quantification. Traditional methodologies based on covariance matrix decomposition have the difficulty of being computationally expensive, which is even more serious when the dimension of the random field is large. This paper proposes an efficient stochastic realization approach for sampling Gaussian stationary random fields from a systems and control point of view. Specifically, we take the exponential and squared exponential covariance functions as examples and make a decoupling assumption when there are multiple dimensions. Then a rational spectral density is constructed in each dimension using techniques from covariance extension, and the corresponding autoregressive moving-average (ARMA) model is obtained via spectral factorization. As a result, samples of the random field with a specific covariance function can be generated very efficiently in the space domain by implementing the ARMA recursion using a Gaussian white noise input. Such a procedure is computationally cheap due to the fact that the constructed ARMA model has a low order. Furthermore, the same method is integrated to multiscale simulations where interpolations of the generated samples are achieved when one zooms into finer scales. Both theoretical analysis and simulation results show that our approach performs favorably compared with covariance matrix decomposition methods.

Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

Development of an Optimal Control Strategy for Temperature Regulation and Thermal Storage Operation of a Solar Power Plant Based on Fresnel Collectors

Concentrating solar power (CSP) plants with thermal energy storage (TES) systems are a promising sustainable technology to meet the increasing global energy consumption and reduce the greenhouse gases emission. From a control point of view, these plants have state constraints, nonlinear dynamics, and discontinuities which must be taken into account when a control system is designed so that a good performance over the whole operation range is achieved. In this regard, an optimal control strategy aiming to regulate the captured solar energy, and to manage properly the charge and discharge of the TES systems to maximize the revenue according to a variable energy tariff, is proposed in this work. Both goals are tackled considering a decentralized strategy with a rigorous perspective to show which properties can effectively be guaranteed using such an approach. Extensive numerical simulations with realistic energy tariff prices and weather data of Brazil are presented to illustrate the performance of the proposed control methodology. Furthermore, a comparative study with a classic control strategy demonstrates the improvements obtained with the proposed controller in the revenue of the produced energy in such a scenario. Potential gains up to 13.5%, in terms of annual revenue, are estimated in case the optimal strategy is adopted.

A new concept of water management diagnosis for a PEM fuel cell system

This paper presents a new diagnosis view of fuel cell water management. The faults related to water management in the fuel cell are addressed from the control point of view and their occurrence is considered as a consequence of a temperature sensor fault. To ensure proper operation of the polymer electrolyte membrane fuel cell (PEMFC), the stack temperature and inlet pressure are controlled by a model-free control. A fault in the temperature sensor that causes an imbalance in water content in the stack is detected by a new fault detection approach in model-free context. The validation of the proposed strategy is performed on a 1.2 kW fuel cell with real time detection of temperature sensor faults, leading to water condensation in the cells or membrane dehydration. Fuel cell control and diagnosis are achieved without any requirement of an accurate system analytical model and additional sensors. However, faults can be detected in quasi static operation only and not un transient state.

Statics and Dynamics of Continuum Robots Based on Cosserat Rods and Optimal Control Theories

This article explores the relationship between optimal control and Cosserat beam theory from the perspective of solving the forward and inverse dynamics (and statics as a subcase) of continuous manipulators and snake-like bioinspired locomotors. By invoking the principle of minimum potential energy and the Gauss principle of least constraint, it is shown that the quasi-static and dynamic evolutions of these robots are the solutions of optimal control problems in the space variable, which can be solved at each step (of loading or time) of a simulation with the shooting method. In addition to offering an alternative viewpoint on several simulation approaches proposed in the recent past, the optimal control viewpoint allows us to improve some of them while providing a better understanding of their numerical properties. The approach and its properties are illustrated through a set of numerical examples validated against a reference simulator.

Transition Path Theory for Langevin Dynamics on Manifolds: Optimal Control and Data-Driven Solver

We present a data-driven point of view for rare events, which represent conformational transitions in biochemical reactions modeled by overdamped Langevin dynamics on manifolds in high dimensions. We first reinterpret the transition state theory and the transition path theory from the optimal control viewpoint. Given a point cloud probing the manifold, we construct a discrete Markov chain with a -matrix computed from an approximated Voronoi tesselation via the point cloud. We use this -matrix to compute a discrete committor function whose level set automatically orders the point cloud. Then based on the committor function, an optimally controlled random walk on point clouds is constructed and utilized to efficiently sample transition paths, which become an almost sure event in time instead of a rare event in the original reaction dynamics. To compute the mean transition path efficiently, a local averaging algorithm based on the optimally controlled random walk is developed, which adapts the finite temperature string method to the controlled Monte Carlo samples. Numerical examples on sphere/torus including a conformational transition for the alanine dipeptide in vacuum are conducted to illustrate the data-driven solver for the transition path theory on point clouds. The mean transition path obtained via the controlled Monte Carlo simulations highly coincides with the computed dominant transition path in the transition path theory.

Voltage Control of a Quasi Z-Source Converter Under Constant Power Load Condition Using Reinforcement Learning

The goal of this paper is to design an adaptive optimal controller for voltage control of DC–DC Z-Source (ZS) converters under a challenging load situation called Constant Power Load (CPL) without a need for any knowledge about their dynamics. These converters have been developed to remedy the drawbacks of the conventional converters and several works have been recently carried out to increase the voltage gain and decrease the voltage stress in these converters. Meanwhile, from a control point of view, voltage control of a very high voltage gain ZS DC–DC converter is unquestionably more sophisticated than the traditional converters (like Boost). Firstly, due to the non-minimum phase functionality, ZS converters present an additional challenge to controller design processes compared with traditional converters. Secondly, their output voltage sensitivity is high, and small oscillations in the control signals can lead to large oscillations in the output voltage. On the other hand, CPLs can further threaten the performance of the converter’s voltage controller due to incremental negative resistance. To alleviate these problems Reinforcement Learning (RL), one of the promising optimal and adaptive model-free control algorithms, can be used. Contrary to traditional controllers which manipulate PWM’s duty cycle, here the proposed controller directly controls the power switches inside the converter. Due to the fact that the action space is discrete and finite, this gives rise to significant simplifications in the architecture of the RL-based controller and better convergence in the training phase. Voltage control of various ZS converter topologies can be carried out by the proposed algorithm. In this paper, for instance, it is experimentally applied to a quasi ZS converter under CPL condition, and its performance compared to other classical controllers such as single-loop PI controller, dual-loop PI controller, and finite state model predictive controller is reported which confirms the better performance of the proposed algorithm in terms of voltage stability and transient tracking error.

Adams-based hierarchical features fusion network for image dehazing

Recent developments in Convolutional Neural Networks (CNNs) have made them one of the most powerful image dehazing methods. In particular, the Residual Networks (ResNets), which can avoid the vanishing gradient problem effectively, are widely deployed. To understand the success of ResNets, recent mathematical analysis of ResNets reveals that a ResNet has a similar formulation as the Euler method in solving the Ordinary Differential Equations (ODE’s). Hence, image dehazing which can be formulated as an optimal control problem in dynamical systems can be solved by a single-step optimal control method, such as the Euler method. This optimal control viewpoint provides a new perspective to address the problem of image restoration. Motivated by the advantages of multi-step optimal control solvers in ODE’s, which include better stability and efficiency than single-step solvers, e.g. Euler, we propose the Adams-based Hierarchical Feature Fusion Network (AHFFN) for image dehazing with modules inspired by a multi-step optimal control method named the Adams–Bashforth method. Firstly, we extend a multi-step Adams–Bashforth method to the corresponding Adams block, which achieves a higher accuracy than that of single-step solvers because of its more effective use of intermediate results. Then, we stack multiple Adams blocks to mimic the discrete approximation process of an optimal control in a dynamical system. To improve the results, the hierarchical features from stacked Adams blocks are fully used by combining Hierarchical Feature Fusion (HFF) and Lightweight Spatial Attention (LSA) with Adams blocks to form a new Adams module. Finally, we not only use HFF and LSA to fuse features, but also highlight important spatial information in each Adams module for estimating the clear image. The experimental results using synthetic and real images demonstrate that the proposed AHFFN obtains better accuracy and visual results than that of state-of-the-art methods.