Closed-loop Control Problem Research Articles

Adaptive Internal Model Backstepping Control for a Class of Second-Order Electromagnetic Micromirror with Output Performance Constraints and Anomaly Control.

This paper investigates the asymptotic tracking problem for a class of second-order electromagnetic micromirror model with output performance constraints and anomaly control, which is subject to model parameter uncertainties and external disturbances. Specifically, this paper formulates the trajectory tracking control problem of an electromagnetic micromirror as a closed-loop control trajectory tracking problem based on the general solution framework of output regulation. Moreover, the extended internal model is introduced to reformulate the closed-loop control problem into a state stabilization problem of the augmented system. Based on the augmented system, an internal model backstepping controller is proposed by integrating the barrier Lyapunov Functions (BLF) and the Nussbaum gain function with the backstepping structure.This controller not only satisfies the output performance constraints of the micromirror, but also maintains the control performance in anomalous control situations. The final performance simulation demonstrates the efficacy of the proposed controller.

Recent advances in the precision control strategy of artificial pancreas.

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Method of gaze extraction in bionic vision

Aiming at the low efficiency of information processing in a robot vision system, our work studies how to extract fixed points during tasks so that significant and interesting target areas in the scene can be located. First, the architecture of a gaze extraction model of bionic vision is proposed, which consists of a spatial visual saliency model and a task-driven fixed point extraction model. The task-driven fixed point extraction process is represented as a closed-loop control problem. To couple exploration with the sampling process, a Q learning algorithm based on a random strategy is adopted, which is a top-down task-driven gaze extraction in the time dimension. It is fused with the spatial-based visual saliency model to form a spatiotemporal hybrid gaze extraction model to determine the fixed point in the final image. Finally, our qualitative image visualization experiment indicates that the fixed point extraction results of the model in two consecutive frames are close to the truth value obtained by an eye tracker. The quantitative area under the curve and average angular error data confirms the effectiveness of the model in predicting and extracting fixed points.

Nonlinear Model Predictive Control of a Robotic Soft Esophagus

Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE), which provides a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) is designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 is implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models are discovered from the TOF and pressure sensor data. Peristalsis waves of speed 20 mm\s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies. The major contribution claimed in this paper is the application of Sparse identification of Nonlinear Dynamics SINDYC-MPC to solve the closed-loop control problem of RoSE for achieving desired peristaltic waves.

Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems

The problem of closed-loop control for high dimensional nonlinear systems is considered. Three different types of kernel interpolation surrogates for the optimal feedback policy are compared. The data for this comes from open-loop controlled solutions. Here, additional information about the system originating from the Pontryagin Maximum Principle is exploited. The starting position for the open-loop control is adaptively selected by a geometric greedy selection criterion, and a so-called vectorial kernel orthogonal greedy algorithm is performed to set up the surrogate. With this procedure we can overcome the curse of dimensionality and still receive a very precise, robust and real-time capable feedback control.

End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning

With the advent of the socio-technical manufacturing paradigm, the way in which rescheduling decisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly dynamic conditions. To cope with uncertain production environments, a drastic increase in the type and degree of automation used at the shop floor for handling unforeseen events and unplanned disturbances is required. In this work, the on-line rescheduling task is modelled as a closed-loop control problem in which an artificial autonomous agent implements a control policy generated off-line using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control policy is stored in a deep neural network, which is used to select repair actions in order to achieve a small set of repaired goal states. The rescheduling agent is trained using Proximal Policy Optimisation based on a wide variety of simulated transitions between schedule states using colour-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end deep learning of successful rescheduling policies to encode task-specific control knowledge that can be understood by human experts.

LQG control and linear policies for noisy communication links with synchronized side information at the decoder

We consider a multidimensional closed-loop control problem where a stochastic linear time-invariant plant is connected via a communication system that includes an encoder, vector additive Gaussian noise (AGN) channel, decoder to a controller. For this setup, we first examine the impact of the communication link when causal side information (CSI) that is correlated to the plant is (possibly) allowed at the decoder and the performance criterion is the classical linear quadratic (LQ) cost. The CSI is modeled as a time-invariant vector-valued Gaussian process and when available at the decoder, it happens concurrently to the signal obtained from the vector AGN channel. Under the assumption that our encoder is linear, we leverage the separation principle of control and estimation, to obtain the following new results. (i) For multivariate Gaussian processes, we completely characterize a lower bound on the communication cost term of the optimal LQ Gaussian (LQG) cost in the form of a convex optimization problem and identify the best linear policies that achieve this bound. (ii) For scalar-valued Gaussian processes, we derive an analytical solution to the lower bound of the optimal communication cost term and show that for linear policies, CSI at the encoder does not improve the specific bound. (iii) For scalar-valued Gaussian processes, we also derive a linear time-invariant low delay joint source-channel coding (JSCC) scheme without bandwidth constraints, when CSI is available at the encoder, which demonstrates the case at which the specific lower bound communication cost expression is achievable. Further, we give a suboptimal linear time-invariant JSCC scheme when CSI is available only at the decoder. We supplement our results with various simulation studies.

Loss Minimization of Induction Motors in a Transient Regime

This paper presents a novel design of an energy optimization approach for Induction Motor (IM) drives. Differently from former optimal control studies, the proposed control strategy is based on a designed cost function given as a weighting sum of power and energy models. The problem to be considered is to achieve the loss minimization of IM drives for any possible constraints imposed by the machine power quantity, such as ramp inputs in accelerate and decelerate modes and during different torque levels. The suggested energy optimization strategy, using the concept of Rotor Field Oriented Control (RFOC), allows a low-order current-fed IM model by means of a high-gain current control loop, which considers the stator currents as new inputs to the model and the rotor flux as a main state variable. An optimal rotor flux trajectory is determined by an off-line algorithm based on an optimal closed-loop control problem resulting in the Hamilton Jacobi Bellman equation. The resolution leads to an analytical solution which is implemented to RFOC and performs loss energy minimization during transient speed. Aiming to check the validity of the proposed strategy, a comparison study is conducted between RFOC operating with the optimal rotor flux and RFOC using a rated flux norm. The simulation and experimental results obtained for a 1.5 KW laboratory IM demonstrate the effectiveness of the proposed strategy.

Decoupled nonlinear adaptive control of position and stiffness for pneumatic soft robots

This article addresses the problem of simultaneous and robust closed-loop control of joint stiffness and position, for a class of antagonistically actuated pneumatic soft robots with rigid links and compliant joints. By introducing a first-order dynamic equation for the stiffness variable and using the additional control degree of freedom, embedded in the null space of the pneumatic actuator matrix, an innovative control approach is introduced comprising an adaptive compensator and a dynamic decoupler. The proposed solution builds upon existing adaptive control theory and provides a technique for closing the loop on joint stiffness in pneumatic variable stiffness actuators. Under a very mild assumption involving the inertia and actuator matrices, the solution is able to cope with uncertainties of the model and, when the desired stiffness is constant or slowly varying, also of the pneumatic actuator. Position and stiffness decoupling is achieved by the introduction of a first-order differential equation for an internal state variable of the controller, which takes into account the time derivative of pressure in the stiffness dynamics. A formal proof of the stability of the position and stiffness tracking errors is provided. An appealing property of the approach is that it does not require higher derivatives of position or any derivatives of stiffness. The solution is validated with respect to several use-cases, first in simulation and then via a real pneumatic soft robot with McKibben muscles. A comparison with respect to existing techniques reveals a more robust position and stiffness tracking skill.

Solving the problem of minimax closed-loop control of liquid-propellant launch vehicle fuel consumption control

The paper provides mathematical formalization and a method of solving the problem of minimax (guaranteed) closed-loop terminal control of fuel consumption of a liquid-propellant launch vehicle power plant. The initial discrete-continuous nonlinear model of the controlled object is linearized along the given reference phase path and is approximated by a linear discrete-time multistep dynamical system. The approximated system includes the state vector, the control vector and the disturbance vector that defines the error of formation of the approximated model. Taking into account the geometrical constrains of control and disturbance vectors in the approximated system, we formulate the main problem of minimax closed-loop terminal control of propellant consumption of the launch vehicles propulsion system. This problem consists in solving a number of auxiliary tasks of minimax open-loop terminal control. To solve each of these tasks we use an instrument of development and analysis of generalized attainability domains of the approximated linear discrete dynamical system. These techniques are implemented by modifying the general recurrent algebraic method. To solve the problems under consideration we propose an approach and an appropriate numerical algorithm that is reduced to the implementation of a finite sequence of only one-step algebraic and optimization operations. The efficiency of the proposed approach to solving the problem under consideration is demonstrated and verified by a computer simulation example. This simulation example consists in controlling the process of propellant consumption for Soyuz-2.1b launch vehicles third stage propulsion system.

Detection and isolation of Tollmien-Schlichting waves in shear flows using blind source separation

Abstract A blind source separation (BSS) method for the detection and isolation of Tollmien-Schlichting (TS) waves in sub-critical transitional shear flows is presented. The method is based on an adaptation of the celebrated independent component analysis (ICA) technique to the problem at hand, using appropriate modeling of the flow field and the disturbances acting on it. This modeling is founded on the representation of the acquired flow measurements as mixtures (generated by an a priori unknown mixing process) of disturbance sources. Only the disturbance mixtures, as measured by the sensors embedded in the flow field, are input to the new method. Linear stability theory (LST) is used to model the measured mixtures of sources acquired by sensors placed in the shear flow field. A physics-based design criterion, assuming prior knowledge of the TS wavelengths, is derived for proper sensor placement in order to successfully separate TS wave disturbances. The criterion is verified both numerically, for wall-bounded shear flows, and experimentally, via a wind-tunnel experimental study of flow over a flat plate. The new disturbance detection and isolation approach is expected to prove useful in various applications, including closed-loop flow control problems.

Motion Planning on a Class of 6-D Lie Groups via a Covering Map

This paper presents an approach to motion planning for left-invariant kinematic systems defined on the six-dimensional frame bundles of symmetric spaces of constant cross-sectional curvature. A covering map is used to convert the original differential equation into two coupled equations each evolving on a three-dimensional Lie group. These lower dimensional systems lend themselves to a minimal global representation that avoids singularities associated with the use of exponential coordinates. Open-loop and closed-loop kinematic control problems are addressed to demonstrate the use of this mapping for analytical and numerical motion planning methods. The approach is applied to a spacecraft docking problem using two different types of actuation: 1) a fully actuated continuous low-thrust propulsion system; and 2) an underactuated single impulsive thruster and reaction wheel system.

Dynamic Model Development and Characterization of Gear Bearing Transmission Systems: Theory and Experiments

The Gear Bearing Drive (GBD) is a newly developed actuator concept based on NASA's high-reduction gear bearing technology and brushless outrunner motor technology. Compared to conventional precision drive systems such as harmonic drives, the GBD offers similar advantages such as high torque in a compact assembly but with the potential of better stiffness characteristics and lower transmission friction and more predictable output speed response. In this paper, the open-loop behavior of a GBD prototype is experimentally characterized to obtain a dynamic model for its velocity/position response. Two analytical models for the transmission stiffness are developed using the bending flexure of gear-teeth and transmission geometry that accurately capture the stiffness characteristics. Finally, a sensitivity study is presented that reproduces the response of this transmission under various input and output loads. Analysis of the system dynamics suggests that the GBD behavior is dominated by linear dynamics that are characterized by finite friction dissipation and geometric errors in its gear mesh resulting from imperfect manufacturing. In addition, the transmission shows no unpredictable open-loop dynamics or transmission resonance within its speed range, making its closed-loop control problem a simple task.

Robust closed-loop control of spike-and-wave discharges in a thalamocortical computational model of absence epilepsy

In this paper, we investigate the abatement of spike-and-wave discharges in a thalamocortical model using a closed-loop brain stimulation method. We first explore the complex states and various transitions in the thalamocortical computational model of absence epilepsy by using bifurcation analysis. We demonstrate that the Hopf and double cycle bifurcations are the key dynamical mechanisms of the experimental observed bidirectional communications during absence seizures through top-down cortical excitation and thalamic feedforward inhibition. Then, we formulate the abatement of epileptic seizures to a closed-loop tracking control problem. Finally, we propose a neural network based sliding mode feedback control system to drive the dynamics of pathological cortical area to track the desired normal background activities. The control system is robust to uncertainties and disturbances, and its stability is guaranteed by Lyapunov stability theorem. Our results suggest that the seizure abatement can be modeled as a tracking control problem and solved by a robust closed-loop control method, which provides a promising brain stimulation strategy.

Feedback-Based Target Localization in Underwater Sensor Networks: A Multisensor Fusion Approach

This paper investigates the problem of target localization in underwater sensor networks, subjected to limited measurement range and accuracy. The localization process is mainly divided into two phases, i.e., distance estimation and position solving. In the first phase, some sensor nodes cannot acquire the direct distance measurements of target, due to the high-dynamic and strong-noise characteristics of underwater environment. Based on this, we formulate the distance measurement as a closed-loop control problem, and then a proportional-integral estimator is designed for sensors to acquire the distance information through indirect measurements. With the estimated distance information, a consensus-based unscented Kalman filtering (UKF) algorithm is proposed in the second phase to localize the target, where direct and indirect measurements are fused to reduce the influence of malicious data. Moreover, stability conditions are provided to show that the distance estimator can stabilize the closed-loop system, while the boundedness analyses are demonstrated to guarantee the localization accuracy. Finally, simulation results reveal that the proposed distance estimator can extend the measurement range of sensors by comparing with the single direct measurement. Meanwhile, the consensus-based UKF algorithm can effectively improve the localization accuracy.

Closed-loop Rescheduling using Deep Reinforcement Learning

Fil: Palombarini, Jorge Andres. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Centro de Investigaciones y Transferencia de Villa Maria. Universidad Nacional de Villa Maria. Centro de Investigaciones y Transferencia de Villa Maria; Argentina

Hybrid time-variant reliability estimation for active control structures under aleatory and epistemic uncertainties

Considering that multi-source uncertainties from inherent nature as well as the external environment are unavoidable and severely affect the controller performance, the dynamic safety assessment with high confidence is of great significance for scientists and engineers. In view of this, the uncertainty quantification analysis and time-variant reliability estimation corresponding to the closed-loop control problems are conducted in this study under a mixture of random, interval, and convex uncertainties. By combining the state-space transformation and the natural set expansion, the boundary laws of controlled response histories are first confirmed with specific implementation of random items. For nonlinear cases, the collocation set methodology and fourth Rounge-Kutta algorithm are introduced as well. Enlightened by the first-passage model in random process theory as well as by the static probabilistic reliability ideas, a new definition of the hybrid time-variant reliability measurement is provided for the vibration control systems and the related solution details are further expounded. Two engineering examples are eventually presented to demonstrate the validity and applicability of the methodology developed.

Modeling PD Closed-loop Control Problems with Fuzzy Differential Equations

This paper reports a fuzzy differential equations approach for the modeling of initial condition uncertainty for a proportional derivative closed-loop control of a direct current motor. Uncertainties are considered on the precision of the sensing devices installed on a driver. The closed-loop system is designed for a plant modeled with fuzzy differential equations. Satisfactory analytic and numerical results for the position regulation problem for ideal case and also considering perturbed initial conditions are reported.

Virtual sensing technique for feedforward active noise control

Active noise control (ANC) is one of techniques to reduce unwanted acoustic noise and is based on the superposition property of acoustic waves. If an anti-noise wave is exactly generated to have the same amplitude and inverse phase of the unwanted acoustic noise wave, ANC can reduce the noise level at the desired location, where an error microphone is placed to monitor the error signal. In the case when the error microphone cannot be placed at the desired noise reduction location, virtual sensing techniques are useful. In this paper, we examine one of the virtual sensing techniques in an experiment setup using the parametric array loudspeaker as the secondary source. Experiment results show that improved noise reduction can be obtained at the virtual microphone location.

An Open-loop criterion for the solvability of a closed-loop guidance problem with incomplete information: Linear control systems

The method of open-loop control packages is a tool for stating the solvability of guaranteed closed-loop control problems under incomplete information on the observed states. In this paper, a solution method is specified for the problem of guaranteed closed-loop guidance of a linear control system to a convex target set at a prescribed point in time. It is assumed that the observed signal on the system’s states is linear and the set of its admissible initial states is finite. It is proved that the problem under consideration is equivalent to the problem of open-loop guidance of an extended linear control system to an extended convex target set. Using a separation theorem for convex sets, we derive a solvability criterion, which reduces to solving a finite-dimensional optimization problem. An illustrative example is considered.