1,721,144 research outputs found
Simulation error minimization methods for NARX model identification
No full textIn non-linear model identification the problem of model structure selection is critical for the success of the identification process. This paper discusses this problem with reference to the class of polynomial NARX models. First it is shown that classical identification approaches based on (one-step-ahead) Prediction Error Minimisation (PEM) may lead to an incorrect or redundant model structure selection, especially in non-ideal identification conditions where the identification data are not adequately exciting or over-sampled. Then a more effective approach is introduced, based on the minimisation of the simulation (or model prediction) error. Finally, to reduce the computational load required for the evaluation of the simulation error, a two-stage identification algorithm, that exploits the effect of the choice of the sampling time on structure selection is proposed. A coarse identification of the model structure is initially performed using over-sampled input-output data, and then the structure is refined considering a decimated version of the data. Some simulation and experimental examples are also discussed
NARX model identification using a forward-regression orthogonal estimator with error filtering
No full textFront-tracking centralized motor control in a paper-making process
No full textIn the present work, the problem of controlling recipe transitions in a paper-making process is addressed, in order to avoid paper breakage and reduce paper waste. A decoupling control scheme is proposed, based on the control of speed differences between contiguous motors. This guarantees smooth and fast transitions of all the motor speeds along the production line. A front-tracking scheme is also employed, in order to adapt the recipe change to the actual plant conditions. A simulation model has been constructed and validated for a real industrial paper-making plant, and the effectiveness of the proposed control algorithm has been tested by means of simulation
An identification algorithm for polynomial NARX models based on simulation error minimization
No full textClassical prediction error approaches for the identification of non-linear polynomial NARX/NARMAX models often yield unsatisfactory results for long-range prediction or simulation purposes, mainly due to incorrect or redundant model structure selection. The paper discusses some limitations of the standard approach and suggests two modifications: namely, a new index, based on the simulation error, is employed as the regressor selection criterion and a pruning mechanism is introduced in the model selection algorithm. The resulting algorithm is shown to be effective in the identification of compact and robust models, generally yielding model structures closer to the correct ones. Computational issues are also discussed. Finally, the identification algorithm is tested on a long-range prediction benchmark application
A GMV Technique for Nonlinear Control with Neural Networks
No full textA nonlinear extension of minimum variance and generalised minimum variance control strategies is developed. The plant is modelled with a linear autoregressive part and a nonlinear dependency on the input. A neural network based implementation of the control law is discussed. This results in a nonlinear controller constituted by a few linear blocks complemented with not more than two neural networks. The weights of the networks are estimated off-line and the learning is carried out with input-output data provided by suitable open loop identification experiments. The performance of the time-invariant neuro-control system is compared with the one achievable by adaptive controllers based on linear models of the plant
Nonlinear identification and control of a heat exchanger: a neural network approach
No full textIn this paper the potentials of neural network based control techniques are explored by applying a nonlinear generalized minimum variance control methodology to a simulated application example. In particular, reference is made to the control problem of regulating the output temperature of a liquid-satured steam heat exchanger by acting on the liquid flow rate. Due to the non minimum phase characteristic of the dynamics of the process, a simple inverting minimum variance controller is unsuitable. On the other hand, an effective solution is provided by a detuned model reference approach, which introduces a penalization factor in the control variable. A steady state off set error problem, caused by the neural network approximations, is tackled by means of an hybrid control structure, which combines a nonlinear integral action block with a neural controller. A comparison analysis is made to show the effectiveness of the proposed neural control schemes with respect to classical linear controllers
Neural implementation of GMV control schemes based on affine input-output models
No full textMinimum variance (MV) and generalised minimum variance (GMV) control methods are studied with respect to a particular class of nonlinear input/output recursive models. The plant model is affine control variable. The prediction introduced and nonlinear MV/GMV control design techniques are developed based on these. Suitable neural networks are employed for the estimation of the nonlinearities of the system. The weights of the networks are estimated off-line and the learning is carried out with input/output data provided by suitable open-loop identification experiments. The MV/GMV controllers obtained are composed of linear and nonlinear blocks. The latter being implemented with neural networks. The linear blocks can be tuned online to improve the controlled system performance. A satisfactory performance is generally obtained in a wide operation range, with convenient dynamical behaviour and low control effort. A discussion on the presence of offset errors is presented and some possible rejection methods are propose
Hybrid neural control systems: some stability properties
No full textNonlinear control with feedforward neural networks is usually designed by means of model based control strategies, which make explicit use of (direct or inverse) models of the controlled system. In this framework, a typical control problem consists in reducing the effects of the inevitable errors introduced by neural network approximation. In a non-adaptive setting, modeling errors can be compensated by hybrid control schemes, where the approximate neural controller is complemented with an integral type regulator connected in parallel. However, in this way, the model based control paradigm is partially lost and stability properties of the control system may be degradated. In this paper a stability analysis of such hybrid schemes is performed, which shows that control system stability can be achieved provided each of the two control blocks obeys a specific condition. Furthermore, a modified hybrid scheme is proposed to enhance the cooperation between the two control blocks: a nonlinear static filter is employed to modulate the integral action so that it becomes significant only when the neural controller has approached the equilibrium. Stability analysis is extended to this case. The hybrid scheme where the two control blocks are connected hierarchically in cascade is finally discussed
Modeling and control of fluid transportation operations in production plants with Petri nets
No full textIn industrial plants various material flows take place, connecting processing and storing units to accomplish specific product routings. For example, chemical and batch plants are characterized by fluid exchange between tanks, reactors, filters, distillation columns, etc. Such fluid transportation operations may involve the use of many elementary transporting resources (e.g., pipes and valves), which can be extremely complicated to manage, especially in the case of concurrent material transfer and possibly conflicting commands. A systematic modeling and control approach for such operations is proposed based on Petri nets, which employs aggregate transportation resources denoted lines, for which suitable definitions of dependency and compatibility are provided. The actual management of valves is demanded to a specific control module, which computes valve commands with simple logical rules on the basis of the marking of resource places in the supervisor Petri net. The hierarchical and modular control architecture separates the resource allocation and deadlock avoidance problems from the physical device management, thus limiting system complexity. The general ideas discussed in this brief have been exemplified with a simple batch process example drawn from the literature
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