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

Infinite Time Optimal Neuro Control for Distributed Parameter Systems

The conventional dynamic programming methodology for the solution of optimal control, despite having many desirable features, is severely restricted by its computational requirements. However, in recent times, an alternate formulation, known as the adaptive-critic synthesis, has given it a new perspective. In this paper, we have attempted to use the philosophy of adaptive-critic design to the optimal control of distributed parameter systems. An important contribution of this study is the derivation of the necessary conditions of optimality for distributed parameter systems, described in discrete domain, following the principle of approximate dynamic programming. Then the derived necessary conditions of optimality are used to synthesize infinite time optimal neuro-controllers in the framework of adaptive-critic design. A motivating example that follows clearly shows the potential of the adaptive critic procedure

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

Neuro Control of Nonlinear Discrete Time Systems with Deadzone and Input Constraints

A neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of uncertain nonlinear systems with unknown deadzones and magnitude constraints on the input. The NN controller consists of two NNs: the first NN for compensating the unknown deadzones; and the second NN for compensating the uncertain nonlinear system dynamics. The magnitude constraints on the input are modeled as saturation nonlinearities and they are dealt with in the Lyapunov-based controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking errors and the neural network weights estimation errors is demonstrated via Lyapunov stability analysis

Stability Analysis and Neuro-control of Nonlinear Systems: a Dynamic Pole Motion Approach

In a linear time-invariant system, the parameters are constant thereby poles are static. However, in a linear time-varying system since the parameters are a function of time, therefore, the poles are not static rather dynamic. Similarly, the parameters of a nonlinear system are a function of system states, and that makes nonlinear system poles dynamic in the complex plane. The location of nonlinear system poles are a function of system states explicitly and time implicitly. Performance characteristics of a dynamic system, e.g., stability conditions and the quality of response depend on the location of dynamic poles in the complex plane. In this thesis, a dynamic pole motion in the complex g-plane based approach is established to enhance the performance characteristics of a nonlinear dynamic system. g-plane is a three-dimensional complex plane. The stability approach, initiated by Sahu et al. (2013), was an exertion of the dynamic Routh's stability criterion by constructing a dynamic Routh's array to examine the absolute stability of a nonlinear system in time domain. This thesis extends the work to investigate the relative stability as well as stability in the frequency domain with the introduction of the dynamic Nyquist and Bode plots. A dynamic Nyquist criterion together with the concept of the dynamic pole motion is developed. The locations of the dynamic poles are executed by drawing a dynamic root locus from the dynamic characteristic equation of a nonlinear system. The quality of the response of a nonlinear dynamic system is enhanced by using a dynamic pole motion based neuro-controller, introduced by Song et al. (2011). In this thesis, we give a more comprehensive descriptions of the neuro-controller design techniques and illustrate the neuro-controller design approach with the help of several nonlinear dynamic system examples. The controller parameters are a function of the error, and continually relocate the dynamic poles in the complex g-plane to assure a higher bandwidth and lower damping for larger errors and lower bandwidth and larger damping for smaller errors. Finally, the theoretical concepts are further corroborated by simulation results

Online identification and nonlinear control of the electrically stimulated quadriceps muscle

A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure

Tahap kepuasan bekerja dan motivasi kerja di kalangan pekerja industri pelancongan

Kajian ini berkaitan dengan tahap Kepuasan Bekeija dan Motivasi di kalangan pekeija di dalam Industri Pelancongan yang berbentuk kajian deskriptif. Teori Kepuasan Bekeija dan Teori Motivasi Herzberg telah digunakan sebagai asas kepada kajian ini. Dua agensi telah diambil sebagai tempat kajian. Sampel telah diambil dari keseluruhan populasi untuk pengumpulan data. Borang soal selidik digunakan sebagai instrumen untuk pengumpulan data. Kajian ini menggunakan perisian Microsoft Excel untuk analisis data. Dapatan kajian menunjukkan skor min kepuasan bekeija berada di tahap sederhana (min = 2.97). Melalui kajian yang dijalankan pengkaji mendapati keadaan penyelenggaraan memperolehi tahap kepuasan yang rendah. Oleh itu beberapa cadangan telah diusulkan untuk memperbaiki tahap yang lemah ini