Indirect Adaptive Neural Control Research Articles

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

A New Indirect Adaptive Neural Control for Nonlinear Systems: A Real Validation on a Chemical Process

In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure a quick adaptation of neural networks parameters guaranteeing a faster output convergence and reducing the tracking error. The effectiveness of the proposed TDVLRs is illustrated by simulation with a nonlinear dynamic system. In order to validate simulation results, an application on a transesterification reactors is, also, presented.

An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target

This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target.

Multimodel and neural emulators for non-linear systems: application to an indirect adaptive neural control

This work investigates an uncoupled multimodel emulator for non-linear system control design. Efficiency of the proposed multimodel emulator is illustrated by comparison with the neural one by their application to SISO indirect adaptive neural control. Neither an initialisation parameter and nor online adaptation is required for multimodel emulator unlike the neural one. The effectiveness of the proposed multimodel emulator is shown through numerical simulations. The obtained results using multimodel emulator are far better than neural emulator.

Stable indirect adaptive neural controller for a class of nonlinear system

The paper presents an indirect adaptive neural control scheme for a general high-order nonlinear continuous system. In the proposed scheme a neural controller is constructed based on the single-hidden layer feedforward network (SLFN) for approximating the unknown nonlinearities of dynamic systems. A sliding mode controller is also incorporated to compensate for the modelling errors of SLFN. The parameters of the SLFN are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where the parameters of the hidden nodes are assigned randomly. However different from the original ELM algorithm, the output weights are updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system, even in the presence of modelling errors which are offset using the sliding mode controller. Finally the proposed adaptive neural controller is applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate that good tracking performance is achieved by the proposed control scheme.

Avoiding singularities in recurrent neural control for induction motors

In this paper, a master–slave synchronization scheme based on parameter identification is proposed to overcome the controller singularity problem that appears when linearization-like techniques are applied in indirect adaptive neural control, like Neural Block Control (NBC). Such a synchronization strategy requires an identifier-like recurrent neural network and an adaptive law to update the neural weights. The proposed adaptive law prevents both, specific adaptive weights zero-crossing and the ‘parameter drift’ phenomenon. NBC consists of two tasks; synchronizing an identifier-like recurrent neural network (slave) with the plant (master) and controlling the system based on the slave model. The effectiveness of the synchronization law is tested using NBC for controlling the angular speed and magnetic flux magnitude of an induction motor. Usingit a priori knowledge about the real plant, a high-order recurrent neural network is proposed as the slave system. Based on the slave neural model, a discontinuous control law is derived, which combines Block Control and Sliding Modes. NBC with the proposed synchronization strategy is tested via simulations, comparing results with a standard parameters adaptive law. Copyright © 2011 John Wiley & Sons, Ltd.

Nonlinear Adaptive Neural Controller for Unstable Aircraft

A model reference indirect adaptive neural control scheme that uses both off-line and online learning strategies is proposed for an,unstable nonlinear aircraft controller design. The bounded-input-bounded-output stability requirement for the controller design is circumvented using an off-line, finite interval of time training scheme. The aircraft model is first identified using a neural network with linear filter (also known as time-delayed neural network) with the available input-output data for a finite time interval. The finite time interval is selected such that this time interval is less than the critical time interval for the aircraft from its stability point of view (similar to the time to double). A procedure to select this critical time interval is also presented. For a given reference model and the identified model, the controller neural-network weights are adapted off-line for the same time interval. The off-line trained neural controller ensures the stability and provides the necessary tracking performance for the unstable aircraft. If there is a change in the aircraft dynamics or characteristics, the trained neural identifier and controller are also adapted online. The theoretical results are validated using the simulation studies based on a locally nonlinear longitudinal high-performance fighter aircraft similar to the F-16. The neural controller design proposed is also compared with the feedback error learning neural control strategy in terms of the tracking ability and control efforts for various level flight conditions and fault conditions such as modeling uncertainties and partial control surface loss. We also present the robustness of the aircraft under extreme wind and noise conditions.

Indirect adaptive neural control for precalcination in cement plants

Control of the precalcination degree in the precalciner of cement plants is a problem of great importance due to its effect to the quality of the clinker, the consumed energy and the byproducts of the whole cement pyroprocess. Divergence of the desired precalcination degree of the raw mix may cause increased carbon monoxide production which is a significant pollution factor that under certain circumstances may lead to explosive gas mixtures. The basic inputs of the precalciner are the temperature and pressure of the tertiary air, temperature and mass flow rate of the raw mix, and the mass flow rate of the fuel, namely pulverized coal. From these inputs, the coal mass flow rate is the only actuatable one. The outputs of the precalciner are the calcinated raw mix, characterized by its mass flow rate, temperature and degree of precalcination, and the abgasses consisting of oxygen, carbon monoxide, carbon dioxide, water and nitrogen, characterized by their temperature and the concentrations of the above elements. Some of the variables of the process, namely the raw mix and coal mass flow rates, the raw mix temperature, the temperature and pressure of the tertiary air and abgasses, and the oxygen concentration in them, can be measured by appropriate sensors. The rest variables can be computed from the measurable variables after appropriate approximations. In this paper, the precalcination degree is controlled via a dynamic controller that regulates the abgasses temperature. The parameters of the controller are computed on the basis of a polynomial neural network that identifies off-line the variations (around nominal values) of the real industrial measurements of the variables of the process. The results are illustrated via simulations of the closed loop system where it is shown that the abgasses temperature is set to a desired level while a desirable precalcination degree is derived.