Online Optimal Adaptive Control of Partially Uncertain Nonlinear Discrete-Time Systems using Multilayer Neural Networks

This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optimal control input and value function. Instantaneous control input error and temporal difference are used to tune the weights of the critic and actor networks, respectively. The selection of the basis functions and their derivatives are not required in the proposed approach. The state vector, critic, and actor NN weights are proven to be bounded using the Lyapunov method. Our approach can be extended to neural networks with an arbitrary number of hidden layers. We have demonstrated our approach via a simulation example

Optimal Adaptive Tracking Control Of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor–critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing gradient issue, the actor and critic MNN weights are tuned using control input and temporal difference errors (TDEs), respectively. In addition, a weight consolidation scheme is incorporated into the critic MNN update law to attain lifelong learning and overcome catastrophic forgetting, thus lowering the cumulative cost. The tracking error, and the actor and critic weight estimation errors are shown to be bounded using the Lyapunov analysis. Simulation results using the proposed approach on a two-link robot manipulator show a significant reduction in tracking error by $44\%$ and cumulative cost by $31\%$ in a multitask environment

Experimental plug&play quantum coin flipping

Performing complex cryptographic tasks will be an essential element in future quantum communication networks. These tasks are based on a handful of fundamental primitives, such as coin flipping, where two distrustful parties wish to agree on a randomly generated bit. Although it is known that quantum versions of these primitives can offer information-theoretic security advantages with respect to classical protocols, a demonstration of such an advantage in a practical communication scenario has remained elusive. Here, we experimentally implement a quantum coin flipping protocol that performs strictly better than classically possible over a distance suitable for communication over metropolitan area optical networks. The implementation is based on a practical plug&play system, designed for quantum key distribution. We also show how to combine our protocol with coin flipping protocols that are almost perfectly secure against bounded adversaries, hence enhancing them with a level of information-theoretic security. Our results offer a powerful toolbox for future secure quantum communications.Comment: Version 2, 19 pages including detailed security analysi

Online Deep Neural Network-Based Feedback Control of a Lutein Bioprocess

An online adaptive deep neural network (DNN) scheme has been introduced for the tracking control of a nonlinear bioprocess with uncertain internal dynamics. First, a detailed controllability analysis is conducted for the Lutein bioprocess to represent the bioprocess as a nonlinear system in affine form. Next, a controller consisting of a DNN-based function approximator is designed for the nonlinear Lutein production bioprocess. It is demonstrated that closed-loop tracking control of a bioprocess for a desired yield profile is possible only with two inputs. The set point trajectory to yield maximum Lutein production is shown by the proposed online adaptive deep NN controller. The proposed controller exhibits self-learning capability under closed loop condition, due to the online learning phase. In other words, no explicit offline learning phase is required and online learning is preferred due to lack of a priori training data for approximating complex nonlinear functions. Simulation results are provided to confirm the performance of the proposed approach

Online Optimal Adaptive Control of Partially Uncertain Nonlinear Discrete-Time Systems using Multilayer Neural Networks

This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optimal control input and value function. Instantaneous control input error and temporal difference are used to tune the weights of the critic and actor networks, respectively. The selection of the basis functions and their derivatives are not required in the proposed approach. The state vector, critic, and actor NN weights are proven to be bounded using the Lyapunov method. Our approach can be extended to neural networks with an arbitrary number of hidden layers. We have demonstrated our approach via a simulation example

Online deep neural network-based feedback control of a Lutein bioprocess

An online adaptive deep neural network (DNN) scheme has been introduced for the tracking control of a nonlinear bioprocess with uncertain internal dynamics. First, a detailed controllability analysis is conducted for the Lutein bioprocess to represent the bioprocess as a nonlinear system in affine form. Next, a controller consisting of a DNN-based function approximator is designed for the nonlinear Lutein production bioprocess. It is demonstrated that closed-loop tracking control of a bioprocess for a desired yield profile is possible only with two inputs. The set point trajectory to yield maximum Lutein production is shown by the proposed online adaptive deep NN controller. The proposed controller exhibits self-learning capability under closed loop condition, due to the online learning phase. In other words, no explicit offline learning phase is required and online learning is preferred due to lack of a priori training data for approximating complex nonlinear functions. Simulation results are provided to confirm the performance of the proposed approach. © 2020 Elsevier Lt

Multilayer Neural Network-based Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-time Systems

In this paper, online optimal adaptive tracking control of nonlinear discrete-time systems in affine form with uncertain internal dynamics is presented. The augmented system and the cost function over infinite horizon for the augmented state are defined. Two-layer neural network (NN) -based actor-critic framework is introduced to estimate the optimal control input and value function. The temporal difference (TD) error is derived as a function of the difference between actual and estimated value function. The NN weights of critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference errors and control policy errors, respectively. The proposed scheme ensures the closed-loop stability in the form of boundedness. Simulation results are provided to illustrate the effectiveness of the proposed approach. © 2020 IEEE

Fault Diagnosis of Batch Reactor Using Machine Learning Methods

Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM) is used to estimate the heat release (Qr) of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faulty Qr values, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter (Qr) shows that it is more efficient and fast for diagnosing the typical faults

Online Optimal Adaptive Control of a Class of Uncertain Nonlinear Discrete-time Systems

In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis function and its derivatives. The boundedness of the system state vector and actor and critic NN weights are shown through Lyapunov theory. Extension of the proposed approach to MNNs with more hidden layers is discussed. Simulation results are provided to illustrate the effectiveness of the proposed approach. © 2020 IEEE

Výuka a osvojení si prediktivního řízení

How to explain Model Predictive Control (MPC) to students? How to practise it? The paper deals with chain of actions involving teaching, practicing and laboratory application of MPC at University of Pardubice in Czech Republic and at Anna University in India. Individual steps are presented and discussed with examples from educational experience - e.g. modelling and identification, derivation of MPC controller, simulations and laboratory applications. Every phase has a key and weak point as well. Desired results is that students understand better the theoretical concepts and they are able to apply predictive controllers at least for laboratory processes. Derivations and MATLAB scripts are available online.Článek je věnován problematice jak vyučovat prediktivní řízení a jak znalosti procvičit a ověřit. Zabývá se celým řetězcem činností od modelování a identifikace, přes odvození regulátoru až po simulace a laboratorní ověření