Open-loop Feedback Research Articles

Multicriteria Optimization of Stochastic Robust Control of the Tracking System

A multicriteria optimization of stochastic robust control with two degrees of freedom of a tracking system with anisotropic regulators has been developed to increase accuracy and reduce sensitivity to uncertain object parameters. Such objects are located on a moving base, on which sensors for angles, angular velocities and angular accelerations are installed. Improvements in the accuracy of control with two degrees of freedom include closed-loop feedback control and open-loop feedback control through the use of reference and perturbation effects. The multicriteria optimization of the stochastic robust control tracking system with two degrees of freedom with anisotropic controllers is reduced to the iterative solution of a system of four coupled Riccati equations, the Lyapunov equation, and the determination of the anisotropy norm of the system by an expression of a special form, which is numerically solved using the homotopy method, which includes vectorization matrices and iterations according to Newton's method. The objective vector of robust control is calculated in the form of a solution of a vector game, the vector gains of which are direct indicators of the quality that the system should achieve in different modes of its operation. The calculation of the vector gains of this game is related to the simulation of a synthesized system with anisotropic regulators for different modes of operation with different input signals and object parameter values. The solutions of this vector game are calculated on the basis of a set of Pareto-optimal solutions taking into account the binary relations of preferences on the basis of the metaheuristic algorithm of multi-swarm Archimedes optimization. Based on the results of the synthesis of stochastic robust control of a tracking system with two degrees of freedom with anisotropic controllers, it is shown that the use of synthesized controllers made it possible to increase the accuracy of system control, reduce the time of transient processes by 3–5 times, reduce the variance of errors by 2.7 times, and reduce the sensitivity of the system to the change of object parameters compared to typical regulators.

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

This paper shows solicitude for the generalized projective synchronization of Caputo fractional-order uncertain memristive neural networks (FOUMNNs) with multiple delays. By extending the constant scale factor to the time-varying function matrix, we establish an extraordinary synchronization mode called time-varying function matrix projection synchronization (TFMPS), which is a generalized version of traditional matrix projection synchronization, modified projection synchronization, complete synchronization, and anti-synchronization. To achieve the goal of TFMPS, we design a novel mixed controller including the open loop feedback control and impulsive control, which employs the state information in the time-delayed interval and the sampling information at the impulse instants. It has a prominent advantage that impulse intervals are not restricted by time delays. To establish the connection between the error system and the auxiliary system, a generalized fractional-order comparison theorem with time-varying coefficients and impulses is established. Applying the stability theory, the comparison theorem, and the Laplace transform, new synchronization criteria of FOUMNNs are acquired under the mixed impulsive control schemes, and the derived synchronization theorem and corollary can effectively expand the correlative synchronization achievements of fractional-order systems.

Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method

High dynamic and precise control of process variables within permissible limits is the key to high product quality in the injection molding industry. Among these variables, the injection flow rate of molten polymer is particularly significant. In this paper, an effective optimal open-loop and state feedback controller design method is designed to realize the optimal tracking control of the melt polymer flow front position flow of melt polymer fluid during injection molding. To this end, the dynamic model of the filling process is firstly established, taking into account the nonlinearity of hydraulic control system and the effect of molten polymer flow on the dynamics of the injection ram, and then the dynamic optimal tracking control problem of flow front position of molten polymer in injection molding machine is proposed. Then, an open-loop controller and a multi-level feedback control law are well designed, wherein the control signal and the control feedback kernels are parameterized by control parameterization. The controller design problems are transformed into sequential optimal parameter decision problems. The explicit expressions of the gradient information of the objective function and constraints on the parameters of the decision variables are solved by using the state sensitivity equation analysis method, and the gradient information is combined with the nonlinear programming algorithm to solve the optimization problems efficiently. Finally, the viability and effectiveness of the proposed design method are verified by numerical simulations.

Research on road tracking and anti-roll game control scheme of commercial vehicle based on Nash equilibrium

In view of the problem that the active suspension conflicts with the road tracking system while resisting roll when the commercial vehicle encounters emergency road conditions in the process of unmanned driving, this article is based on the Nash non-cooperative open-loop feedback game theory, taking the road tracking system, and the active anti-roll system as the participants of the game. An interactive control scheme which can not only accurately steer but also take into account the vertical roll stability is proposed. Firstly, the coupled model of Horizontal pendulum-anti-roll for commercial vehicles is established and extended to the vehicle-road three-degree of freedom closed model. Then, based on the derivation of the linear quadratic optimal control model (LQ), the non-cooperative open-loop Nash game theory is used to obtain the interactive optimal control afferent the road tracking system and the active anti-roll system. Finally, the experimental scheme is verified by hardware in the loop experiment. The results show that the interactive control scheme of road tracking and anti-roll based on Nash non-cooperative game for commercial vehicles allows the vehicle to take the vertical stability into account while turning actively, thus ensuring the safety and stability of the vehicle during driving.

Design and Implementation of Closed loop control for Flyback Converter

One of the high frequency switching power supply circuits is the flyback converter. Due to its inexpensive cost and electrical isolation properties, it is mostly employed in low power applications. It is well known that high frequency switching results in losses and lowers inverter efficiency, particularly at low powers. Consequently, a flyback DC-DC converter that is run in a closed control loop is explained in this study. Closed-loop control is the proposed topology for the flyback converter. For the same input/output conditions, the findings from the closed-loop converter are compared to those from the open-loop feedback topology. A closed-loop flyback has been found to be more effective than an open-loop flyback. MATLAB simulates the outcomes. One of the high frequency devices is the flyback converter.

PROJECTION SYNCHRONIZATION OF FUNCTIONAL FRACTIONAL-ORDER NEURAL NETWORKS WITH VARIABLE COEFFICIENTS

In this paper, the projection synchronization problem of functional fractional-order neural networks with variable coefficients and Caputo derivatives is studied. Firstly, a simple global projection synchronization scheme is designed according to the open-loop and adaptive feedback control. Secondly, by constructing a suitable Lyapunov function and utilizing the properties of delayed fractional-order differential inequalities, some criteria for the global projective synchronization of the variable coefficient functional neural networks with Caputo derivatives are obtained. Finally, a numerical example with many numerical simulations is employed to demonstrate the correctness and validity of the proposed method in this paper.

From chaos to encryption using fractional order Lorenz-Stenflo model with flux-controlled feedback memristor

To add complexity to a chaotic system, a new five-dimensional fractional-order chaotic system is proposed based on the Lorenz-Stenflo model with a feedback memristor. By analyzing the phase portraits, equilibrium points, bifurcation analysis, and Poincaré maps, the system generates a two-wing attractor with symmetrical coexistence, which implies that the newly developed chaotic system has abundant dynamical characteristics. The Routh–Hurwitz stability criterion, eigenvalues, and Lyapunov exponents were calculated for a memristive-based system, suggesting that the developed system is unstable and hyperchaotic. The chaotic system is executed with analogue circuits for both the open-loop and closed-loop feedback memristive systems. The transfer function technique was used for the fractional operator. The simulation results showed excellent agreement between the circuit and numerical simulations. Finally, random data information from a chaotic system is utilized to process multimedia encryption. A new cryptographic scheme is presented with the idea of an image as a key, which is introduced and tested with security analysis in support of the provision that images and chaotic systems together can form a viable key.

Optimal chance-constrained pension fund management through dynamic stochastic control

We apply a dynamic stochastic control (DSC) approach based on an open-loop linear feedback policy to a classical asset-liability management problem as the one faced by a defined-benefit pension fund (PF) manager. We assume a PF manager seeking an optimal investment policy under random market returns and liability costs as well as stochastic PF members’ survival rates. The objective function is formulated as a risk-reward trade-off function resulting in a quadratic programming problem. The proposed methodology combines a stochastic control approach, due to Primbs and Sung (IEEE Trans Autom Control 54(2):221–230, 2009), with a chance constraint on the PF funding ratio (FR) and it is applied for the first time to this class of long-term financial planning problems characterized by stochastic asset and liabilities. Thanks to the DSC formulation, we preserve the underlying risk factors continuous distributions and avoid any state space discretization as is typically the case in multistage stochastic programs (MSP). By distinguishing between a long-term PF liability projection horizon and a shorter investment horizon for the FR control, we avoid the curse-of-dimensionality, in-sample instability and approximation errors that typically characterize MSP formulations. Through an extended computational study, we present in- and out-of-sample results which allows us to validate the proposed methodology. The collected evidences confirm the potential of this approach when applied to a stylized but sufficiently realistic long-term PF problem.

Control of Microparticle Assembly

A colloidal system is a large collection of micrometer-sized particles suspended in a liquid, and the state of the system can be measured in real time, using imaging techniques and image processing. The assembly of the particles is driven by interactions between the particles and the surrounding liquid, as well as by external fields, including electromagnetic, flow, and gravitational fields. The dynamics of the many-body system are high-dimensional, nonlinear, and stochastic. However, low-order models are derived in some cases, often using physics-based order parameters, to facilitate studying the system dynamics. With an understanding of the system dynamics, and by manipulating the aforementioned interactions, one can control the assembly process in real time using open-loop and closed-loop feedback control. Theoretical studies and experimental demonstrations of colloidal self-assembly control have been reported, with methods ranging from heuristic rules to model-based optimal feedback control.

Flow measurement and instrumentation flow control characteristics of throttling venturi valve with adjustable area

In this study, a throttling venturi valve with adjustable area was designed to control the thrust of a monopropellant thruster using hydrogen peroxide. The flow rate control characteristics of the throttling venturi valve were investigated based on the pintle stroke and upstream pressure of the venturi. Three kinds of experiments were conducted: pressure and flow rate measurement according to pintle stroke and venturi upstream pressure, determination of critical pressure ratios under various conditions, flow rate control performance through open-loop control and feedback control of an actuator. The pressures were measured at the upstream, throat, and downstream of the venturi. It was observed that the flow rate changed in proportion to the stroke and upstream pressure. Below a stroke of 10 mm, the critical pressure ratio gradually decreased as the stroke and upstream decreased. However, above a stroke of 10 mm, the critical pressure ratio converged to a value between 0.7 and 0.8 regardless of the upstream pressure. The results of automatic flow rate control tests using open-loop control and feedback control showed that the measured flow rate satisfactorily followed the target flow rate profile.

Approximately Optimal Control of Nonlinear Dynamic Stochastic Problems with Learning: The OPTCON Algorithm

OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to economic models. It delivers approximate numerical solutions to optimum control (dynamic optimization) problems with a quadratic objective function for nonlinear economic models with additive and multiplicative (parameter) uncertainties. The algorithm was first programmed in C# and then in MATLAB. It allows for deterministic and stochastic control, the latter with open loop (OPTCON1), passive learning (open-loop feedback, OPTCON2), and active learning (closed-loop, dual, or adaptive control, OPTCON3) information patterns. The mathematical aspects of the algorithm with open-loop feedback and closed-loop information patterns are presented in more detail in this paper.

EMSx: a numerical benchmark for energy management systems

Inserting renewable energy in the electric grid in a decentralized manner is a key challenge of the energy transition. However, at local scale, both production and demand display erratic behavior, which makes it challenging to match them. It is the goal of Energy Management Systems (EMS) to achieve such balance at least cost. We present EMSx, a numerical benchmark for testing control algorithms for the management of electric microgrids equipped with a photovoltaic unit and an energy storage system. EMSx is made of three key components: the EMSx dataset, provided by Schneider Electric, contains a diverse pool of realistic microgrids with a rich collection of historical observations and forecasts; the EMSx mathematical framework is an explicit description of the assessment of electric microgrid control techniques and algorithms; the EMSx software EMSx.jl is a package, implemented in the Julia language, which enables to easily implement a microgrid controller and to test it. All components of the benchmark are publicly available, so that other researchers willing to test controllers on EMSx may reproduce experiments easily. Eventually, we showcase the results of standard microgrid control methods, including Model Predictive Control, Open Loop Feedback Control and Stochastic Dynamic Programming.

Psychophysics Experiment to Check the Temperature Impacts Over Human Fingertips for the Application of Textural Applications in Haptics Technology

Psychophysical methods in haptic technology help in comparative study and eventually be a data set to achieve realism over skin sensation. Textural based haptic applications are widely developed using tactile displays over human fingertips. The tactile displays work on open-loop admittance feedback system and are controlled with flexible parameters by ignoring the impact of noise or disturbance variables. Human skin undergoes various noise factors like temperature, humidity, sweat, and influence of alternative senses. This paper presents the newly adopted method of psychophysics to study the influence of environmental conditions over perceiving textural surfaces. The paper adopts the detection mode of psychophysics which uses perception time as an output parameter for understanding perception memory of the human skin. We have recorded the period of the perception in three environmental conditions over human subjects under a single blindfold method to study the behaviour of human skin at fingertips. The perception time of stimulus is analysed with arithmetic average roughness value (Ra) to understand the tolerance factor required during tactile based textural applications. The proposed method is simple to structure and improves in creating the dataset required to consider the noise factor for an open-loop admission feedback system.

Optimal Quantum Control Theory

This article explains some fundamental ideas concerning the optimal control of quantum systems through the study of a relatively simple two-level system coupled to optical fields. The model for this system includes both continuous and impulsive dynamics. Topics covered include open- and closed-loop control, impulsive control, open-loop optimal control, quantum filtering, and measurement feedback optimal control.

Intelligent Wireless EEG Measurement System With Electrical and Optogenetic Stimulation for Epileptic Seizure Detection and Suppression

A two-channel real-time intelligent electroencephalography (EEG) measurement system with electrical and optical stimulations is presented for seizure detection and suppression. Based on software control through a wireless interface, this system provides closed- and open-loop feedback control with programmable waveform for electrical and optical stimulations. Matlab graphical user interface is used to monitor the intracranial EEG, perform the algorithm for seizure detection, and adjust the required intensity, frequency, duty cycle, and duration of electrical and optical stimulations. The whole hardware device is embedded on a printed circuit board with the size of 27.3 mm $\times23.6$ mm. C57BL/6 mice with Thy1-ChR2-YFP gene transfer and drug-induced seizure are used to demonstrate epileptic seizure suppression according to electrical and optogenetic stimulation in the proposed wireless EEG measurement system.

A Kalman estimation model of human trust in supervisory control of robotic swarms

Trust is an important factor in the interaction between humans and automation that can mediate the reliance of human operators. In this work, we evaluate a computational model of human trust on swarm systems based on Sheridan (2019)’s modified Kalman estimation model using existing experiment data (Nam, Li, Li, Lewis, & Sycara, 2018). Results show that our Kalman Filter model outperforms existing state of the art alternatives including dynamic Bayesian networks and inverse reinforcement learning. This work is novel in that: 1) The Kalman estimator is the first computational model formulating the human trust evolution as a combination of both open-loop trust anticipation and closed-loop trust feedback. 2) The proposed model considers the operator’s cognitive time lag between perceiving and processing the system display. 3) The proposed model provides a personalized model for each individual and reaches a better level of fitness than state-of-the-art alternatives.

An Evolutionary Approach to Passive Learning in Optimal Control Problems

We consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317–338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research.

具有时延和随机扰动的未知C-G神经网络的有限时间函数投影同步及其在保密通信中的应用

The finite-time function projective synchronization of unknown Cohen-Grossberg neural networks with time delays and stochastic disturbances was investigated. A hybrid control scheme combining open-loop control and feedback control was designed to guarantee that the drive and response networks can be synchronized up to a scaling function in a finite time with parameter identification by means of the finite-time stability theory. Besides, the upper bounds of the settling time of synchronization were estimated. Finally, the corresponding numerical simulation and its application in secure communication were provided to demonstrate the validity of the presented synchronization method.

Data-Driven Optimal Control of Linear Time-Invariant Systems

We consider an optimal control problem for discrete-time linear time-invariant systems subject to unknown state-space model, constrained inputs and noisy output measurements. Since traditional model-based optimal control problem formulations and methods are not applicable to the problem under consideration, we propose a data-driven robust formulation based on the explicit model description derived from a single measured trajectory of the system. Then we propose an open-loop optimal feedback control scheme and show that its efficient implementation requires solution of a number of optimal estimation problems and a deterministic optimal control problem, all in data-driven formulations. The main contributions of this paper are the separation of the estimation and control processes in the data-driven context and the resulting robust feedback control scheme.

Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process

Within this work a nonlinear model predictive controller (MPC) was implemented in a Penicillium chrysogenum fed-batch process and compared to a PI(D) and an open loop feedback control scheme, referenced as model based control (MBC).The controllers were used to maintain predefined set-points of biomass specific glucose uptake rates, product precursor and nitrogen concentrations by manipulating the glucose, precursor and nitrogen feeds.As the critical component concentrations are not available for direct measurement a particle filter including measured oxygen uptake rate (OUR) and carbon evolution rate (CER) was deployed to estimate biomass, nitrogen and product precursor concentrations. State estimation and predictive control actions were based on a kinetic model which was retrieved from literature and adapted to the examined process and control tasks by simplifying the description of the hyphal compartmentalization and adding nitrogen as well as the measurable OUR and CER.Besides simulations, verification experiments of the developed control schemes were executed. Although the kinetic model used for state estimation and prediction does not reflect the overall biological complexity it could be successfully used to estimate and control the glucose uptake and the unmeasured component concentrations. During experimental verification, nonlinear process dynamics caused unstable PI(D) behavior. In comparison to PI(D) and MBC, the MPC efficiently avoided formation of by-products, which resulted in efficient substrate utilization and an overall product gain of 14%.