Closed-loop Experimental Data Research Articles

Deep Learning Neural Encoders for Motor Cortex.

Intracortical brain-machine interfaces (BMIs) transform neural activity into control signals to drive a prosthesis or communication device, such as a robotic arm or computer cursor. To be clinically viable, BMI decoders must achieve high accuracy and robustness. Optimizing these decoders is expensive, traditionally requiring animal or human experiments spanning months to years. This is because BMIs are closed-loop systems, where the user updates his or her motor commands in response to an imperfectly decoded output. Decoder optimization using previously collected "offline" data will therefore not capture this closed-loop response. An alternative approach to significantly accelerate decoder optimization is to use a closed-loop experimental simulator. A key component of this simulator is the neural encoder, which synthetically generates neural population activity from kinematics. Prior neural encoders do not model important features of neural population activity. To overcome these limitations, we use deep learning neural encoders. We find these models significantly outperform prior neural encoders in reproducing peri-stimulus time histograms (PSTHs) and neural population dynamics. We also find that deep learning neural encoders better match neural decoding results in offline data and closed-loop experimental data. We anticipate these deep-learning neural encoders will substantially improve simulators for BMIs, enabling faster evaluation, optimization, and characterization of BMI decoder algorithms.

Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing

Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

Modeling and Robust Low Level Control of an Omnidirectional Mobile Robot

This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. iSRob is a multivariable nonlinear system subject to parameter uncertainties mainly due to friction forces. A multilinear model is proposed to approximate the behavior of the system, and the parameters of these models are estimated from closed-loop experimental data applying Gauss–Newton techniques. A robust control technique, quantitative feedback theory (QFT), is applied to design a proportional–integral (PI) controller for robust low-level control of the iSRob system, being this the main contribution of the paper. The designed controller is implemented, tested, and compared with a gain-scheduling PI-controller based on pole assignment. The experimental results show that robust stability and control effort margins against system uncertainties are satisfied and demonstrate better performance than the other controllers used for comparison.

Design and experimental evaluation of robust controllers for a two-wheeled robot

ABSTRACTThe paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.

Experimental determination of some equilibrium parameter of Damavand tokamak by magnetic probe measurements for representing a physical model for plasma vertical movement.

This investigation is about plasma modeling for the control of vertical instabilities in Damavand tokamak. This model is based on online magnetic measurement. The algebraic equation defining the vertical position in this model is based on instantaneous force-balance. Two parameters in this equation, including decay index, n, and lambda, Λ, have been considered as functions of time-varying poloidal field coil currents and plasma current. Then these functions have been used in a code generated for modeling the open loop response of plasma. The main restriction of the suitability analysis of the model is that the experiments always have to be performed in the presence of a control loop for stabilizing vertical position. As a result, open loop response of the system has been identified from closed loop experimental data by nonlinear neural network identification method. The results of comparison of physical model with identified open loop response from closed loop experiments show root mean square error percentage less than 10%. The results are satisfying that the physical model is useful as a Damavand tokamak vertical movement simulator.

The Design of Frequency Filters of Iterative Feedback Tuning Using Particle Swarm Optimization

Iterative feedback tuning (IFT) is a data-based tuning approach that minimizes a quadratic performance index using some closed-loop experimental data. A control weighting coefficient, known as lambda, and two frequency filters are the most important parameters which can significantly improve the performance of the method. One of the major problems in IFT is tuning these parameters. This paper presents a new approach to tune frequency filters using particle swarm optimization (PSO). At the end, the performance of the proposed method is evaluated by two case study simulations.

State-dependent control of a hydraulically actuated nuclear decommissioning robot

This paper develops and evaluates state-dependent parameter (SDP) control systems for the hydraulically actuated dual-manipulators of a mobile nuclear decommissioning robot. A unified framework for calibration and SDP model identification is proposed, in which the state-dependent variable is a delayed voltage input associated with the time-varying system gain. Such nonlinearities can cause undesirable joint movements under automatic control. Hence, the present paper develops a nonlinear pole assignment algorithm for the SDP model. Closed-loop experimental data shows that the SDP design more closely follows the joint angle commands than an equivalent linear algorithm, offering improved resolved motion.

Simultaneous attainment of model and controller for linear time delay systems with the data driven Smith compensator

AbstractIn this paper, we propose a data-driven approach to the Smith compensator for the simultaneous attainment of a controller and a mathematical model of linear time-delay SISO systems. Under the situation in which we do not know a mathematical model of a plant, the proposed method here enables us to obtain the optimal Smith compensator for the desired tracking property based on the direct utilization of a one-shot closed loop experimental data. In addition, by introducing the special structure to the feedback controller used in the Smith compensator, it is possible to obtain not only the desired controller but also the mathematical model of a plant with a time-delay. Finally, we also give an experimental result in order to show the validity of the proposed method.

Frequency Response Estimation for NCFs of an MIMO Plant From Closed-Loop Time-Domain Experimental Data

Frequency response estimation from closed-loop time-domain experimental data is investigated in this paper for normalized coprime factors (NCF) of a multiple-input-multiple-output (MIMO) plant. Based on a linear fractional transformation (LFT) representation for all the NCFs of plants internally stabilizable by a known controller, this estimation problem is converted into the open-loop nonparametric estimation of an inner transfer function matrix (TFM). An estimate is derived through constrained data-matching. It is proved that when the probing signals are periodic and the NCFs of the auxiliary plant are appropriately selected, the estimate is asymptotically unbiased and with a normal/complex normal distribution. It has been made clear that the estimation bias and the estimation variance are always finite. Computationally tractable procedures are suggested for choosing the desirable auxiliary TFMs.

Time-Domain System Identification Methods for Aeromechanical and Aircraft Structural Modeling

Time-domain model structures and algorithms are described that are suitable for the identification of aeromechanical and aircraft structural models from input-output data. An efficient batch subspace identification algorithm and its online version are presented. The batch algorithm was implemented and evaluated for structural mode identification of the V-22 tilt rotor and for the identification of models of aeromechanical instability. The online algorithm is demonstrated with a numerical example in which structural modes are identified and tracked as they appear. Our results and comparisons with current aircraft industry practice show several advantages of time-domain subspace methods over Prony and frequency-domain methods. Specific advantages include the ability to identify multiple structural modes simultaneously from a single experiment, the ability to use online system identification, and the ability to identify open-loop systems from closed-loop experimental data. These advantages have a significant effect on the number of ground and flight tests required and on how to perform testing in unstable flight regimes.

Performance-Based Switching for Fault Tolerant Control

This paper presents an approach to active fault-tolerant control (FTC) based on online closed-loop performance monitoring and experimental data without an explicit fault detection and isolation algorithm. The proposed approach sets forth the theory of unfalsified control to achieve a real-time control reconfiguration which is consistent with the performance objectives for a class of system faults. The obtained FTC scheme is an alternative or might be a complement to FTC methods based on explicit fault detectors and provides a precise and direct characterization of suitable real-time controllers for reconfiguration.

On the reproduction of closed-loop experimental data by an LFT perturbed model set

This paper investigates the consistency between frequency domain closed-loop experimental data and a model set with linear fractional unstructured uncertainties. When the number of unstable poles of the transfer function matrices (TFM) in the model set are identical, a necessary and sufficient condition is derived for the reproduction of experimental data. This condition is expressed as the non-positiveness of a quadratic form of the external disturbances and measurement errors, together with a linear constraint. In this derivation, it is not required that all the TFMs in the model set are internally stabilizable by the controller. The condition is also not necessary for the closed-loop system consisting of the nominal model and the controller to be internally stable. It is believed that this condition is theoretically and computationally attractive in model set validation, no matter whether a deterministic or stochastic framework is adopted.

Fitting nonlinear low-order models for combustion instability control

Abstract Tools for fitting low-complexity nonlinear models based on experimental data are examined through the example problem of finding a reduced-order model suitable for control of a combustion instability operating in a limit cycle. This proceeds in four parts; physical modeling, linear system identification, nonlinear analysis, and validation test design. It is shown how the nonlinear tools of describing functions, bifurcation methods and manifold analysis assist in developing a simple nonlinear model capable of describing the data and consistent with physical understanding. The system being modeled is a lean gas turbine combustor which exhibits a sustained mid-range (100– 1000 Hz ) limit cycle instability. The closed-loop experimental data does not contain a sufficiently rich spectrum for confident modeling in the first linear system identification phase. Despite the paucity of information quality, a grey-box nonlinear model is created and parametrized which provides an explanation both of the limit-cycle fundamental oscillation and of a high frequency nonharmonic signal also present. The model structure is explored and various operating conditions simulated to understand the model better. The validation and/or refinement of this model is then considered. The model validation problem is important because of the poor information content of the periodic limit cycle data. The challenge is to provide a practically feasible, small excitation to the loop to improve identifiability and to provide qualitative tests of model performance. We examine this problem by considering the nonlinear dynamics of the model class and feasible excitation mechanisms.

Closed loop identification and control loop reconfiguration: an industrial case study

Abstract The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.

Efficient tuning of linear multivariable controllers using iterative feedback tuning

Iterative feedback tuning is a direct tuning method using closed-loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. Due to these unbiased gradient estimates, the method converges to a stationary point of the control criterion provided the closed loop signals remain bounded throughout the iterations. In this contribution, it is shown how such unbiased estimates can be obtained for multivariable linear time-invariant systems. Particular attention is given to the issue of keeping the experiment time to a minimum and several efficient algorithms are presented. It is shown that, for tuning an arbitrary linear time-invariant multivariable controller with nw inputs and nu outputs, 1+nuA—nw experiments are sufficient in each iteration of the algorithm. For disturbance rejection, an alternative algorithm is proposed which requires nu+nw experiments. As an illustration, the method is applied to a simulation model of a gas turbine engine.

A primal-dual interior-point method for iterative feedback tuning

Abstract Iterative Feedback Tuning (IFT) is a method to tune controllers using closed loop experimental data and estimation of gradients. In this paper is shown how IFT can be extended to the case when there are constraints on some of the performance measures. This is accomplished by solving the Karush-Kuhn-Tucker conditions for the optimization problem using a primal-dual interior-point method. It is shown that only a few extra experiments have to be performed as compared to traditional IFT.

Process Control-Relevant and Closed-Loop Identification

The identification of dynamical systems on the basis of data, measured under closed-loop experimental conditions, is a problem which is highly relevant in many (industrial) applications. When using models as a basis for model-based robust control design both nominal models and model uncertainty bounds are required. In this paper it is shown how -in particular- model uncertainty bounds can be obtained from closed-loop experimental data in the classical prediction error identification framework. The considered uncertainty structure is adjusted so as to allow direct evaluation of the performance robustness of both the actual and a to-be-designed controller.

4SID Identification for Control

Many applications of control system design based on input-output measurements allow an iterative scheme of repeated identification and control with progressive refinements of the designed controller. In dealing with the interplay between identification and robust control design, a new alternative is given to aspects of the Subspace-based State Space System Identification (4SID) method under closed-loop experimental data for numerical robust control design.

A New PID tuning method by applying an iterative concept

Abstract In this paper, we propose a new PID controller design method based on the iterative identification and controller design scheme. At the identification part, we use a frequency weighted least square method for closed loop experimental data. The controller design is made two parts. (1) to design a frequency weighted LQG controller. (2) to design a PID controller by LQG controller reduction. In the reduction part, we use a frequency domain model matching method that is focused on the frequency response of the loop transfer function. The efficiency of this algorithm is shown through some simulation results.

Unfalsified plant model parameterization from closed-loop experimental data

The problem of parameterizing all unfalsified plant models from closed-loop time-domain identification experimental data is investigated in this paper. The assumed a priori plant information is an upper bound of the L ∞ norm and the number of the unstable poles of its transfer function. Moreover, it is assumed that magnitude bounds of the disturbances that perturb the closed-loop system have been supplied. Under the condition that the stabilizing controller is known, it is shown that all the unfalsified plant transfer functions can be parameterized by a linear fractional transformation of a fixed transfer-function matrix and an L ∞-norm-bounded, structure-fixed but uncertain transfer-function matrix. These results are very similar to those when plant open-loop time-domain identification experimental data are supplied. However, further efforts are still needed in order to apply these results to robust controller design. This results mainly from the computational complexity and the high dimensions of the transfer-function matrices involved in the parameterization.