An implementation of novel CMAC algorithm for very short term load forecasting

This paper describes the application of cerebellar model articulation controller (CMAC) and B-spline neural networks to switched reluctance motor (SRM) torque estimation and control. Non-linear adaptive systems such as neural networks are well suited to learning the highly non-linear electromagnetic characteristics of the SRM for the purposes of linearisation and simplification of their control and a number of researchers have investigated their use in this context. CMAC and B-spline neural networks are particularly suited to this application area due to their potential for low-cost, high-speed implementation including the capability for real-time, on-line adaptation. CMAC and B-spline neural networks have successfully been applied both to torque ripple minimisation and to torque estimation in simulation and, implemented using FPGA technology, experimentally. This paper describes those applications with particular emphasis on the suitability of the CMAC and B-spline neural networks and gives details of their FPGA implementation.

The cerebellar model articulation controller (CMAC) neural network (NN) has advantages over fully connected NNs due to its increased structure. This paper attempts to provide a comprehensive treatment of CMAC NNs in closed-loop control applications. The function approximation capabilities of the CMAC NN are first rigorously established, and novel weight-update laws derived that guarantee the stability of the closed-loop system. The passivity properties of the CMAC under the specified tuning laws are examined and the relationship between passivity and closed-loop stability is derived. The utility of the CMAC NN in controlling a nonlinear system with unknown dynamics is demonstrated through numerical examples.

The Cerebellar Model Articulation Controller (CMAC) neural network is an associative memory that is biologically inspired by the cerebellum, which is found in the brains of animals. The standard CMAC uses the least mean squares algorithm to train the weights. Recently, the recursive least squares (RLS) algorithm was proposed as a superior algorithm for training the CMAC online as it can converge in just one epoch, and does not require tuning of a learning rate. However, the RLS algorithm was found to be very computationally demanding as its computational complexity is dependent on the square of the number of weights required which can be huge for the CMAC. Here, we show a more efficient RLS algorithm that uses inverse QR decomposition and additionally provides a regularized solution, improving generalization. However, while the inverse QR decomposition based RLS algorithm reduces computation time significantly; it is still not fast enough for use in CMACs greater than two dimensions. To further improve efficiency we show that by using kernel methods the CMAC computational complexity can be transformed to become dependent on the number of unique training data. Additionally, it is shown how modeling error can be improved through use of higher order basis functions.

In this paper, a CMAC (cerebellar model articulation controller) neural network application on fault diagnosis of automobile automatic transmission system is proposed. Firstly, we build a CMAC neural network based diagnosis system with different coding scheme depending on the fault types. Secondly, the fault patterns, obtained from the China scholar's technical data, would be used to train the CMAC neural network off-line using new coding scheme. Thirdly, the learning algorithm was developed to guarantee the learning convergence. Finally, combining the MATLAB program the trained neural network can be used to diagnose the possible fault. Comparing with the traditional schemes, lower weights interference between different fault type patterns, higher noise rejection ability, do not require expert's expertise, fewer memory size and fast training speed are obtained.

This paper discusses adaptive control method used in high performance servo system based on Cerebellar Model Articulation Controller(CMAC). It analyzes the uncertain and nonlinear factors of the servo system, studies the modeling method using the frequency property of the system, and then uses an additional item to instead of the non-modeling factor. In the proposed control scheme, the CMAC controller is able to learn the unknown model dynamics, parameter variation and disturbance of the system on line. The model reference adaptive control (MRAC) scheme is used to give better solutions with online adaptation. It contemporarily improved the conception mapping algorithm of the Cerebellar Model Articulation Controller Neural Network(CMACNN), which gave a determined expression of the physical memory size and designed a physical memory address function. Experiment results verify that the proposed strategy can achieve high tracking precision for real-time position close-loop servo system.

The cerebellar model articulation controller (CMAC) neural network is an associative memory that is biologically inspired by the cerebellum, which is found in the brains of animals. In recent works, the kernel recursive least squares CMAC (KRLS---CMAC) was proposed as a superior alternative to the standard CMAC as it converges faster and does not require tuning of a learning rate parameter. One improvement to the standard CMAC that has been discussed in the literature is eligibility, and vector eligibility. With vector eligibility the CMAC is able to control online motion control problems that it could not previously, stabilize the system much faster, and converge to a more intelligent solution. This paper integrates vector eligibility with the KRLS---CMAC and shows how the combination is advantageous through two simulated control experiments.

This work presents a novel integral variable structure control (IVSC) that combines a cerebellar model articulation controller (CMAC) neural network and a soft supervisor controller for use in designing single-input single-output (SISO) nonlinear system. Based on the Lyapunov theorem, the soft supervisor controller is designed to guarantee the global stability of the system. The CMAC neural network is used to perform the equivalent control on IVSC, using a real-time learning algorithm. The proposed IVSC control scheme alleviates the dependency on system parameters and eliminates the chattering of the control signal through an efficient learning scheme. The CMAC-based IVSC (CIVSC) scheme is proven to be globally stable inasmuch all signals involved are bounded and the tracking error converges to zero. A numerical simulation demonstrates the effectiveness and robustness of the proposed controller.

The present American Institute of Steel Construction specifications use the alignment charts and approximate formulas conveniently to determine some coefficients in design, such as moment gradient coefficient Cb for beams of I-shaped section and effective length factor K of columns. In these methods, the coefficients are unconservative when the boundary conditions are different from the development of specifications. The governing equations, numerical approaches, on the K and Cb coefficients provide more accurate results. The approaches, however, are not readily available for structural engineers to use in design. Applying neural network computing toward structural engineering problems has received increasing interest, with particular emphasis placed on supervised neural networks. The cerebellar model articulation controller (CMAC), one of the supervised neural network learning models, is mostly used in the domain of control. In this work, we use a newly developed Macro Structure CMAC (MS_CMAC) neural network learning model to aid steel structure design. The topology of the novel learning model is constructed by a number of time inversion CMACs as a tree structure. The learning performance of the MS_CMAC is first compared with a stand-alone time inversion CMAC using one structural engineering example. That comparison indicates not only superior prediction but also fast learning propriety for the MS_CMAC neural network learning model. In addition, the MS_CMAC neural network learning model is applied to two steel design problems. It is shown that the MS_CMAC not only can learn structural design problems within a reasonable central processing unit time but also can estimate more accurate coefficients than that estimated through alignment charts and approximate formulas in American Institute of Steel Construction specifications.

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L 2 tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.

Self-balancing two-wheel robot is a high order, multi-variable, nonlinear, strong-coupling and absolutely unstable system. A reinforcement leaming algorithm based on many parallel Cerebellar Model Articulation Controller (CMAC) neural networks is proposed for the balance-control problem of self-balancing two-wheel robot. In the method, the outputs of CMAC are used to approximate the Q-functions of the input state variables. The input state variables are divided to decrease the grades of quantization. Therefore, the storage spaces of CMAC are reduced effectively, and the learning rate and control precision of Q-algorithm are improved. At the same time, the generalization of continuous state variables is realized too. The method is applied to solve the balance control problem of self-balancing two-wheel robot, and the simulation results show its correctness and efficiency.

A control scheme of ANN-based PID controller is developed here to reach high precision tracking control for an electro-hydraulic servo system. The PID controller is used as a feedback controller to guarantee the system stability. The cerebellar model articulation controller (CMAC) neural network is used as a feed-forward compensator to identify the inverse system dynamics model. The CMAC and the PID controller are connected in parallel. The outputs of this paralleled controller are summed up as the total control action. A nonlinear tracking differentiator (NTD) is presented to yield high quality differential signals for the PID controller. The main task of this control algorithm is to make the error between the total control action and the output of the CMAC minimize by the CMAC learning algorithm. Thus the control action is formed by the CMAC. Numerical simulation results show comparing with conventional PID control strategy this proposed control scheme has an excellent system performance including high precision trajectory tracking ability and rejection of disturbance.

This paper describes an image-based visual servo tracking control scheme using CMAC neural network as object recognition feedback methodology. A web camera based image capture system is mounted on the slider robot to capture the desired object and a CMAC (Cerebellar Model Articulation Controller) based object recognition scheme is developed to recognize the captured object image. Comparing the relative location of the recognized object and the web camera, then the position feedback signal can be obtained. Using the feedback signal, a PID (Proportional-Integral-Derivative) controller is designed to track the desired object by moving a single-axis slider robot, such that the captured object image located on the center of the captured frame always.

Aiming at the nonlinear ness and high performance requirements of high-voltage power supply of ECRH, a new intelligent control strategy is presented based on the concept of inverse model control using Cerebellar Model Articulation Controller (CMAC) neuron network. The experiments results show that this control method can control the power with faster response, much lower overshoot and much shorter settling time than usual way and thus the superiority is proved. This paper may be helpful to solve the difficulty in control of complicated power system with strict requirements and the promotion the application of intelligent control.

Cerebellar Model Articulation Controller (CMAC) is type of neural network inspired from part of the brain named cerebellum. It has long history in control applications, in simulated or in real-time implementations. MATLAB/Simulink is used for rapid building of CMAC neural network controller prototypes than can run in real-time. To run the controller in the same personal computer, QuaRC is used as real-time operating system that will execute controller task along Windows operating system. Hardware-in-the-loop architecture was used to test CMAC controller in real ball-and-beam physical plant from Quanser. Library of developed Simulink functional blocks enable easy exploration of different structural aspects of CMAC neural network controller, including selection of receptive field types, receptive field widths, Albus overlays, and fully interconnected multi-dimensional receptive fields. Same CMAC controller can be used also for controlling model of the plant, which can serve as means for controller to acquire knowledge before operating with real plant. CMAC controller learned to control the plant, and progressively became main control signal generator, while control signal from primary proportional-derivative controller almost vanished. Developed library can serve as easy startup for working with CMAC networks also for other types of applications.

The interpolation capabilities of the binary CMAC