The Middeck Active Control Experiment (MACE)

The Middeck Active Control Experiment (MACE) is a NASA In-Step and Control Structure Interaction (CSI) Office funded Shuttle middeck experiment. The objective is to investigate the extent to which closed-loop behavior of flexible spacecraft in zero-gravity (0-g) can be predicted. This prediction becomes particularly difficult when dynamic behavior during ground testing exhibits extensive suspension and direct gravity coupling. On-orbit system identification and control reconfiguration is investigated to improve performance which would otherwise be limited due to errors in prediction. The program is presently in its preliminary design phase with launch expected in the summer of 1994. The MACE test article consists of three attitude control torque wheels, a two axis gimballing payload, inertial sensors and a flexible support structure. With the acquisition of a second payload, this will represent a multiple payload platform with significant structural flexibility. This paper presents on-going work in the areas of modelling and control of the MACE test article in the zero and one-gravity environments. Finite element models, which include suspension and gravity effects, and measurement models, derived from experimental data, are used as the basis for Linear Quadratic Gaussian controller designs. Finite element based controllers are analytically used to study the differences in closed-loop performance as the test article transitions between the 0-g and 1-g environments. Measurement based controllers are experimentally applied to the MACE test article in the 1-g environment and achieve over an order of magnitude improvement in payload pointing accuracy when disturbed by a broadband torque disturbance. The various aspects of the flight portion of the experiment are also discussed

Frequency Domain Identification of Multirate Systems:A Lifted Local Polynomial Modeling Approach

Frequency-domain representations of multirate systems are essential for controller design and performance evaluation of multirate systems and sampled-data control. The aim of this paper is to develop a time-efficient closed-loop identification approach for multirate systems in the frequency-domain. The developed method utilizes local polynomial modeling for lifted representations of LPTV systems, which enables direct identification of closed-loop multirate systems in a single identification experiment. Unlike LTI identification techniques, the developed method does not suffer from bias due to ignored LPTV dynamics. The developed approach is demonstrated on a multirate example, resulting in accurate and fast identification in the frequency domain

Robust Distributed Control Protocols for Large Vehicular Platoons with Prescribed Transient and Steady State Performance

In this paper, we study the longitudinal control problem for a platoon of vehicles with unknown nonlinear dynamics under both the predecessor-following and the bidirectional control architectures. The proposed control protocols are fully distributed in the sense that each vehicle utilizes feedback from its relative position with respect to its preceding and following vehicles as well as its own velocity, which can all be easily obtained by onboard sensors. Moreover, no previous knowledge of model nonlinearities/disturbances is incorporated in the control design, enhancing in that way the robustness of the overall closed loop system against model imperfections. Additionally, certain designer-specified performance functions determine the transient and steady-state response, thus preventing connectivity breaks due to sensor limitations as well as inter-vehicular collisions. Finally, extensive simulation studies and a real-time experiment conducted with mobile robots clarify the proposed control protocols and verify their effectiveness.Comment: IEEE Transactions on Control Systems Technology, accepte

Development of three phase back to back converter with current flow control using raspberry Pi microcontroller

A High-Voltage Direct Current (HVDC) electric power transmission system uses direct current form the bulk transmission of electrical power, in contrast with the common Alternating Current (AC) systems. For a long-distance transmission, HVDC systems may be less expensive and suffer lower electrical losses. The overall HVDC system is call back-to-back converter. Therefore, this project is to design and to develop a back-to-back converter with Proportional-Integrative-derivative (PID) control current that could be applied for the resistive load. The basic structure of the PID controller makes it easy to regulate the process output. The control technique is called a current control technique by comparing the output current with the reference current. Thus, the PID controller will force the output current to follow the reference current by creating and changing the pulse width modulation (PWM) signals. The PID controller is developed and simulated by using MATLAB/Simulink software and then implemented to the hardware by using Raspberry Pi Microcontroller. The result from the simulation shows that, the load current follows the reference current from 0 amperes until 1 amperes and the results from the experiment shows that the output current at the load follows the reference current from 0 amperes until 0.4 amperes. The high sensitivity of current sensor and also due to very low resolution of analogue to digital converter effect the result in this project. The results explanation of the project can be divided into three categories; simulation, open loop control and closed loop control

Space shuttle flying qualities and criteria assessment

Work accomplished under a series of study tasks for the Flying Qualities and Flight Control Systems Design Criteria Experiment (OFQ) of the Shuttle Orbiter Experiments Program (OEX) is summarized. The tasks involved review of applicability of existing flying quality and flight control system specification and criteria for the Shuttle; identification of potentially crucial flying quality deficiencies; dynamic modeling of the Shuttle Orbiter pilot/vehicle system in the terminal flight phases; devising a nonintrusive experimental program for extraction and identification of vehicle dynamics, pilot control strategy, and approach and landing performance metrics, and preparation of an OEX approach to produce a data archive and optimize use of the data to develop flying qualities for future space shuttle craft in general. Analytic modeling of the Orbiter's unconventional closed-loop dynamics in landing, modeling pilot control strategies, verification of vehicle dynamics and pilot control strategy from flight data, review of various existent or proposed aircraft flying quality parameters and criteria in comparison with the unique dynamic characteristics and control aspects of the Shuttle in landing; and finally a summary of conclusions and recommendations for developing flying quality criteria and design guides for future Shuttle craft

Direct data-driven control of linear time-varying systems

An identification-free control design strategy for discrete-time linear time-varying systems with unknown dynamics is introduced. The closed-loop system (under state feedback) is parametrised with data-dependent matrices obtained from an ensemble of input-state trajectories collected offline. Subsequently, controllers guaranteeing bounded closed-loop trajectories, optimal performance and robustness to process and measurement noise are designed via convex feasibility and optimisation problems involving purely data-dependent linear matrix inequalities. For the special case of periodically time-varying systems, performance guarantees are achieved over an infinite horizon, based on data collected over a single, finite duration experiment. The results are demonstrated by means of an illustrative academic example and a practically motivated example involving a voltage source converter.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Process Estimation with Relay Feedback Method

Manual tuning of PID controllers can be very time-consuming and that is why automatic tuning was developed. The conventional automatic tuners use a relay instead of controller in a closed loop system to obtain stabile oscillations. The information from a relay experiment is later used to calculate PID control parameters with different tuning rules and there also exist systems that, apart from the relay experiment, use a step response experiment to obtain even better control. The aim with this thesis is to investigate if there is enough information in a relay experiment to estimate an unknown process as a first order process with delay and then use AMIGO design to obtain satisfying PI/PID controller parameters. The calculation of the estimation parameters of the model is done with Gauss-Newton optimization algorithm. The algorithm minimizes the square of the output error between the unknown process output and estimation model output and calculates the optimal model parameters. The algorithm is dependant of good initial Svalues so a method for initializing good values is developed

On-line direct control design for nonlinear systems

An approach to design a feedback controller for nonlinear systems directly from experimental data is presented. Improving over a recently proposed technique, which employs exclusively a batch of experimental data collected in a preliminary experiment, here the control law is updated and rened during real-time operation, hence enabling an on-line learning capability. The theoretical properties of the described approach, in particular closed-loop stability and tracking accuracy, are discussed. Finally, the experimental results obtained with a water tank laboratory setup are presented

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces