The Augmented Synthetic Control Method

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package

Identification of Hidden Failures in Process Control Systems Based on the HMG Method

We will continue here the research work, the goal of which was to introduce the notion of nondeterministic aggregation operators and study their properties, even in relation to classification systems and the associated learning problem. Here we will concentrate mostly on the notion of the nondeterministic aggregation system and its relation with deterministic ones. We will also see how such a model extends a discretized version of a model of participatory learning with an arousal background mechanism

Fuzzy Method for in Control Acetaldehyde Generation in Resin Pet in the Process of Packaging Pre-Forms of Plastic Injection

In order to control the drying temperature of the PET resin in the silo of the plastic injection molding machine, during the plastic injection process in the industries producing preforms for the manufacture of beverage bottles, care is taken in the ideal temperature regulation for the better performance in controlling the generation of Acetaldehyde (AA), which alters the taste of carbonated or non-carbonated drinks, providing a citrus nuance to the palate and questioning the quality of the packaged products The objective of this work is to develop a tool based on Fuzzy logic to support the control of the drying temperature of PET resin, allowing specialists to make the ideal temperature control decisions necessary to control the generation of Acetaldehyde (AA). For the development of the proposed Fuzzy inference model, we used the Matlab Fuzzy toolbox tool, where the input variables, the fuzzyfication rules and the output variable were implemented based on the data collected from the preform injection process. From the inference model, we obtained a more precise management of the variables that influence the generation of AA, estimating a reduction of $ 240,044.00 in annual costs in the production of preforms

Optimal Control Prediction Method for Control Allocation

This paper proposes a novel prediction method for online optimal control allocation that extends the volume of moments achievable with the Moore-Penrose generalized inverse to the entire Attainable Moment Set. This method formulates the control allocation problem using selected basis vectors and associated gains which reduces the optimization problem dimensions and provides physical insight into the resulting optimal solutions. The proposed algorithm finds the entire family of unique optimal control solutions along the desired moment vector from the origin to the boundary of the Attainable Moment Set. Numerical results for the Moore-Penrose prediction method show that the unique minimal controls obtained yield the desired moment with near machine precision accuracy while maintaining control effectors within specified position limits. This method has been fully validated against the unique solution obtained on the boundary of the Attainable Moment Set using the Durham Direct Allocation method. Minimal control solutions obtained for moments in the interior of the Attainable Moment Set, similarly yield the desired moment to near machine precision while providing control solutions that are smaller (i.e. 2-norm) than solutions found with traditional control allocation algorithms (e.g. interior point methods) applied to the minimal control problem. Numerical simulations using a Matlab autocoded executable (MEX) for the representative real world problem of 3-moments with 20 individual control effectors and prescribed control position limits show a mean computation speed of approximately 125 Hz which is sufficient to enable real-time flight allocation

A finite control set model predictive control method for matrix converter with zero common-mode voltage

In this paper a finite control set model predictive control method is presented that eliminates the common-mode voltage at the output of a matrix converter. In the predictive control process only the rotating vectors are selected to generate the output voltage and the input current in order to remove the common mode voltage. In addition, a modified four-step commutation strategy is proposed to eliminate common-mode voltage spikes caused by the conventional four-step commutation strategy based on the current direction. The proposed method reduces the computational complexity greatly compared with the enhanced space vector modulation with rotating vectors. The feasibility and operation of the proposed method are verified using experimental results. The resulting common-mode voltage is near to zero with good quality input and output converter waveforms

A Modified Method for Tuning PID Controller for Buck-Boost Converter

This paper presents a design and simulation of simplified method for designing a proportional – integral–derivative(PID)controller operating in continuous conduction mode for the Buck-Boost converter ,this method provides good voltage regulation and is suitable for Buck-Boost Dc-Dc converter, it is exposed to significant variations which may take this system away from nominal conditions caused by the line change and parameters variation at the input .Simulation results shows that this PID controller provides good voltage regulation and is suitable for the Buck-Boost purposes. The obtained results prove the robustness of proposed Controller against variation of the input voltage ,load resistance and the referent voltage of the studied converter