Van de Vusse reactor is known as a highly nonlinear chemical process and has been considered by a number of researchers as a benchmark problem for nonlinear chemical process. Various identification methods for nonlinear system are also verified by applying these methods to Van de Vusse reactor. From the point of view of identification, only the Volterra kernel of second order has been obtained until now. In this paper, the authors show that Volterra kernels of nonlinear Van de Vusse reactor of up to 3rd order are obtained by use of M-sequence correlation method. A pseudo-random M-sequence is applied to Van de Vusse reactor as an input and its output is measured. Taking the crosscorrelation function between the input and the output, we obtain up to 3rd order Volterra kernels, which is the highest order Volterra kernel obtained until now for Van de Vusse reactor. Computer simulations show that when Van de Vusse chemical process is identified by use of up to 3rd order Volterra kernels, a good agreement is observed between the calculated output and the actual output.

Called VISTA (Variable-stability In-flight Simulator Test Aircraft), the one-of-a-kind NF-l6D has a simulation system that can mimic several aircraft. Though housed in an F-l6 Fighting Falcon airframe, VISTA can also act like the F-15 Eagle or the Navy's F-14 Tomcat. More importantly, such flexibility allows for improved training and consolidation of some sorties. Consequently USAF Test Pilot School students will have an opportunity to learn how to test future integrated cockpits. In this paper we will use the multiple model adaptive estimation (MMAE) and the multiple model adaptive controller (MMAC) techniques to model the aircraft's flight control system containing the longitudinal and lateral-directional axes. Single and dual actuator and sensor failures will also be included in the simulation. White Gaussian noise will be included to simulate the effects of atmospheric disturbances.

This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.

In this paper, at first, we investigate existing algorithms for finding the minimum infinity-norm solution of consistent linear equations and then propose a new algorithm. The proposed algorithm is intended to includes the advantages of computational efficiency as well as geometric explicitness. As a practical application example, optimum trajectory planning for redundant robot manipulators is considered. Also, an efficient approach avoiding discontinuity in trajectory is proposed by resolving the non-uniqueness problem of minimum infinity-norm solution. To be specific, the proposed method for checking possible discontinuity does not need any other algorithms in checking the possibility of discontinuity while previous work needs specially designed checking courses. To show the usefulness of the proposed techniques, an example calculating minimum infinity-norm solution for comparing the computational efficiency as well as the trajectory planning for a redundant robot manipulator are included.

We consider a class of large-scale interconnected time delay systems and investigate a decentralized robust passive control problem. sufficient conditions for unforced interconnected uncertain systems with time delay to be robustly stable with extended strictly passivity is given in terms of algebraic Riccati inequality and linear matrix inequality. The decentralized robust passive control problem for norm-bounded and positive real uncertainty is shown to be converted to extended strictly positive real control problem for a modified system which contains neither time delay nor uncertainty.

In this paper, the regular rotational gaits of the quadruped crawling robot are studied. It is assumed that the proposed regular rotational gaits starts from one of six support patterns in a translational gaits and end up with one of six support patterns in a translational gaits. Noting that six support patterns in a regular translational gait belong to two different groups with respect to regular rotational gait, the static stability margin and the maximum rotational displacement during one rotational stride period for the two representative support patterns are investigated. It is expected that the proposed regular rotational gaits will enhance the omni-directional characteristics of the quadruped crawling robot.

This paper presents a new formulation to simplify the three resulting constraint equations of the direct kinematics of the 3-6 (Stewart-Gough) Platform. The conventional direct kinematics of the 3-6 Platform has been formulated through complicated steps with trigonometric functions in three angle variables and thus results in the computational burden. In order to reduce the formulation complexity, we replace an angle variable into a length one and express three connecting joints on the moving platform in the same frame. The proposed formulation yields considerable abbreviation of the number of the calculation terms involved in the direct kinematics. It is verified through a series of simulation results.

In this article, we present the novel approach of avoiding temporal insufficiency of data blocks, jitter, which occurs due to the commencement of new session. We propose to make the sufficient amount of data blocks available on memory such that the ongoing session can survive the cycle extension. This technique is called ″pre-buffering″. We examine two different approaches in pre-buffering: (i) loads all required data blocks prior to starting playback and (ii) incrementally accumulates the data blocks in each cycle. We develop an elaborate model to determine the appropriate amount of data blocks necessary to survive the cycle extension and to compute startup latency involved in loading these data blocks. The simulation result shows that limiting the disk bandwidth utilization to 60% can greatly improve the startup latency as well as the buffer requirement for individual streams.

A sliding mode control of spacecraft attitude tracking with actuator, especially reaction wheel, is presented. The sliding mode controller is derived based on quaternion parameterization for the kinematic equations of motion. The reaction wheel dynamic equations represented by wheel input voltage are presented. The input voltage to wheel is calculated from the sliding mode controller and reaction wheel dynamics. The global asymptotic stability is shown using a Lyapunov analysis. In addition the robustness analysis is performed for nonlinear system with parameter variations and disturbances. It is shown that the controller ensures control objectives for the spacecraft with reaction wheels.

This paper focuses on the study of simulation and evolution of Micro Air Vehicles. Micro Air Vehicles or MAVs are small flying robots that are used for surveillance, search and rescue, and other missions. The simulated robots are designed based on realistic characteristics and the brains (controllers) of the robots are generated using genetic algorithms, i .e., simulated evolution. The objective for the experiments is to investigate the effects of robot team size and topology (simulation environment) on the evolution of simulated robots. The testing of team sizes deals with finding an ideal number of robots to be deployed for a given mission. The goal of the topology experiments is to see if there is an ideal topology (environment) to evolve the robots in order to increase their utility in most environments. We compare the results of the various experiments by evaluating the fitness values of the robots i .e., performance measure. In addition, evolved robot teams are tested in different situation in order to determine if the results can be generalized, and statistical analysis is performed to evaluate the evolved results.