Energy-efficient control of pump units based on neural-network parameter observer

An observer based on an artificial neural network was designed. The observer determines the pumping unit performance depending on the operating point. Determination is based on the measured technological coordinates of the system and the pressure of the turbomechanism. Three neural networks were designed for three types of the productivity observer. The developed observer was investigated by the simulation method within different variations of disturbing actions, such as hydraulic resistance of the hydraulic system and geodetic pressure. A comparative analysis of three types of the productivity observer, built with using the pressure and different signals of the system with arbitrary change of hydraulic resistance was given. By the use of the pump unit efficiency observer, in addition to the results presented earlier, the efficiency of the productivity observer, which built with using different sensors, in water supply systems with two series-connected pump units, operating for filling the large tank, is researched. In the water supply system one pump speed is regulated, the other is unregulated. References 14, figures 5

Neural Architectures for Control

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Neural development and sensorimotor control

What is the relationship between development of the nervous system and the emergence of voluntary motor behavior? This is the central question of the nature-nurture discussion that has intrigued child psychologists and pediatric neurologists for decades. This paper attempts to revisit this issue. Recent empirical evidence on how infants acquire multi-joint coordination and how children learn to adapt to novel force environments will be discussed with reference to the underlying development of the nervous system. The claim will be made that the developing human nervous system by no means constitutes an ideal controller. However, its redundancy, its ability to integrate multi-modal sensory information and motor commands and its facility of time-critical neural plasticity are features that may prove to be useful for the design of adaptive robots

Adaptive neural network control of fes-induced cyclical lower leg movements

As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL) and by backpropagation through time (BTT). The performance of the controller with random initial weights was poor after training with BTT and fair after SL. BTT training of the neural controller with weights, which had been initialized by SL, resulted in good control. Training with BTT thus improved the performance of the controller that initially had been trained by SL. An adaptive neural control system based on BTT has been proposed and partially tested. The controller adapted relatively fast to the change of an important model parameter

Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example

Internal Model Control (IMC) - Neural Network (NN) Gain Scheduling Untuk Pengendalian Kolom Distilasi

This research is develop the alternative control algorithm using Internal Model Control - Neural Network Gain Scheduling (IMC-NNGS) to control mole fraction of methanol-water distillation column. Distillation column with L-V control strategy has pairing Xd-L and Xb-Qr. IMC performances depend on only ? tuning value or filter time constant. With ? tuning value manipulating IMC could be nonlinear control, where ? tuning value is outputs of NN that had been trained by using error variable, process variable, manipulated variable, and set point variable from plant. Gain scheduling using NN could be increase control system performance and product quality. The best IAE changing value shown at mole fraction feed increase. There are IAE equal with 0,234799 for IMC and IAE equal with 0, 00042 for IMC-NNGS. In other word IMCGS has IAE 559 times better than IMC. Beside that IMC-NNGS has faster response, offset free and robust to overcome set-point and disturbance changes

Controlling chaos in a chaotic neural network

The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern