Dynamic multilayer neural networks for nonlinear system on-line identification

This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.

Rotational Motion Control by Feedback with Minimum L1-Norm

This paper presents an online modeling and control strategy for a class of linearizable nonlinear systems with unknown parameters and nonlinearities. A dynamic neural network (DNN) is utilized for modeling of nonlinear systems in their equivalent feedback linearized form. Lyapunov techniques are used to derive the adaptation rules for training the DNN's weight matrices. An adaptive state feedback stabilizing control is developed based on the equivalent DNN model of the systems. An observer is also designed to estimate the states of the DNN model of the system. Subsequently, an adaptive output feedback stabilizing control law is derived for unknown nonlinear systems, using their equivalent DNN model, with guaranteed closed-loop stability. Simulation results show the effectiveness of the proposed technique.

Non-linear system identification based on Lie series solutions

An adaptive identification method of simple dynamic recurr ent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteris tics of system can reflect the dynamic ch aracteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNNe and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topologi cal structure in usual dynami c recurrent neural network. Keywords: non-linear system, system identification, dynamic recurrent neural network, RPE algorithm

Hamiltonian dynamics of neural networks

In this paper the design of a dynamic neural network (DNN) for modeling and control of a class of minimum-phase controllable nonlinear systems is considered. The dynamic neural network acts as a generic model of the system, which is then used in the derivation of the control signal. The training of the network is based oil a novel scheme that arranges the outputs of the hidden layer of the DNN into a set of orthogonal basis functions. This allows for the derivation of a stable rule for the training of the DNN's weights and does not require random initialization of the weights.

Iterative control framework with application to guidance and attitude control of rockets

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed network be stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.

Recurrent neural networks unlike feed-forward networks are able to process inputs with time context. The key role in this process is played by the dynamics of the network, which transforms input data to the recurrent layer states. Several authors have described and analyzed dynamics of small sized recurrent neural networks with two or three hidden units. In our work we introduce techniques that allow to visualize and analyze the dynamics of large recurrent neural networks with dozens units, reveal both stable and unstable points (attractors and saddle points), which are important to understand the principles of successful task processing. As a practical example of this approach, dynamics of the simple recurrent network trained by two different training algorithms on context-free language anbnwas studied.

In this study, differential stability is introduced for non-linear systems, and this concept is further exploited in reduced-order observer design for non-linear systems with non-linearities of unmeasured state variables, a more general class of non-linear systems than the systems with linear observer errors. It has been shown that if the dynamics of unmeasured state variables under a state transformation is differentially stable, a reduced-order observer can be designed to produce asymptotically convergent estimates of the unmeasured state variables. A systematic design method is then introduced for a class of multi-output non-linear systems. For such a system, a non-linear term of the unmeasured state variables enter the system through a coupling matrix. It is found that a reduced-order observer can be designed if the linear part with the coupling matrix as the input matrix has no unstable invariant zeros. A further exploitation is presented for a class of single-output non-linear systems with non-linearity of unmeasured state variables. In this case, the coupling vector is allowed to be a vector field which depends on the system output.

This paper aims at addressing the problem of finite time exact tracking control for a class of unknown nonlinear systems in pure feedback form while guaranteeing the closed-loop stability. The uncertain nonlinear system considered in this paper is not only in completely non-affine form but also explicitly dependent on the time and therefore, can cover a general class of nonlinear systems. By incorporating high order sliding mode controller into backstepping design procedure, a new robust control scheme with novel integrators is proposed, which is able to steer the tracking error to zero. Moreover, the closed loop stability can be proved with the help of the constructed integrators in every design step. Two simulation examples are presented to illustrate the correctness and effectiveness of the proposed control scheme.

Intelligent Multivariable Modeling of Blast Furnace Molten Iron Quality Based on Dynamic AGA-ANN and PCA

New results in the Lyapunov-type algorithm for algebraic MCV Riccati equations