Applications of recurrent neural networks in batch reactors. Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.Publicad

Contrôle du convertisseur monophasé par réseau de neurones

Control of the single-phase converter using neural networksThe single-phase converter (AC/DC) is a nonlinear multimodal system. In order to control the electrical power at the output of this system a direct nonlinear controller based on multilayer neural networks (MNN) is used

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches

DIRECT INVERSE CONTROL OF TWO-TANK SYSTEM USING NEURAL NETWORKS

Abstract and Figures In this paper, the implementation of the controller based on neural networks for controlling two-tank system is presented. A ready-made mathematical model of the Amira DTS200 system, which is a typical example of a slow nonlinear process, is used. Among the most important applications of artificial neural networks is their application in the control of nonlinear processes. The applied controlling structure represents Direct Inverse Control. Experimental results of the obtained process response for a given reference input using implemented inverse controller are given

Online Identification and Control of Aerospace Vehicles Using Recurrent Networks

Methods for estimating the aerospace system parameters and controlling them through two neural networks are presented in this study. We equate the energy function of Hopfield neural network to integral square of errors in the system dynamics and extract the parameters of a system. Parameter convergence is proved. For control, we equate the equilibrium status of a modified Hopfield neural network to the steady state Riccati solution with the system parameters as inputs. Through these two networks, we present the online identification and control of an aircraft using its nonlinear dynamics

Efficient Control of DC Servomotor Systems Using Backpropagation Neural Networks

DC motor systems have played an important role in the improvement and development of the industrial revolution, making them the heart of different applications beside AC motor systems. Therefore, the development of a more efficient control strategy that can be used for the control of a DC servomotor system, and a well defined mathematical model that can be used for off line simulation are essential for this type of systems Servomotor systems are known to have nonlinear parameters and dynamic factors, such as backlash, dead zone and Coulomb friction that make the systems hard to control using conventional control methods such as PID controllers. Also, the dynamics of the servomotor and outside factors add more complexity to the analysis of the system, for example when the load attached to the control system changes. Due to these parameters and factors new intelligent control techniques such as Neural Networks, genetic algorithms and Fuzzy logic methods are under research consideration in order to solve the complex problems related to the control of these nonlinear systems. In this research we are using a combination of two multilayer neural networks to implement the control system: a) The first network is used to build a model that mimics the function of DC servomotor system, and b) a second network is used to implement the controller that controls the operation of the model network using backpropagation learning technique. The proposed combination of the two neural networks will be able to deal with the nonlinear parameters and dynamic factors involved in the original servomotor system and hence generate the proper control of the output speed and position. Off line simulation using MATLAB Neural Network toolbox is used to show final results, and to compare them with a conventional PID controller results for the same model

Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming

There has been an increasing interest in using neural networks in closed-loop control systems to improve performance and reduce computational costs for on-line implementation. However, providing safety and stability guarantees for these systems is challenging due to the nonlinear and compositional structure of neural networks. In this paper, we propose a novel forward reachability analysis method for the safety verification of linear time-varying systems with neural networks in feedback interconnection. Our technical approach relies on abstracting the nonlinear activation functions by quadratic constraints, which leads to an outer-approximation of forward reachable sets of the closed-loop system. We show that we can compute these approximate reachable sets using semidefinite programming. We illustrate our method in a quadrotor example, in which we first approximate a nonlinear model predictive controller via a deep neural network and then apply our analysis tool to certify finite-time reachability and constraint satisfaction of the closed-loop system

Functional expansion representations of artificial neural networks

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network