An intelligent hybrid control scheme, using an adaptive recurrent cerebellar model articulation controller (CMAC), is developed for a class of nonlinear dynamical systems. In this control system, an adaptive recurrent CMAC is used as the main tracking controller which mimics a perfect control law, and a compensated controller is designed to compensate for the difference between the perfect cont...

This work proposes a new method of dynamical control systems implementation using the Cerebellar Model Articulation Controller (CMAC) neural network as part of a variable structure system. CMAC is a neural network easily implemented in hardware, quite efficient in the reproduction of multivariable functions and extremely fast in its training process. These features yield CMAC applications in ro...

In this paper, several possible algorithms and training methods for the CMAC network are analyzed thoroughly. Improvements are then examined and a Hybrid approach has been developed for the Maximum Error Algorithm by using the Neighborhood Training for the initial training period. The employment of the technique yielded faster initial convergence which is very important for many control applica...

This paper presents the design of a biped robot using the Cerebellar Model Articulation Controller (CMAC). An inertial navigation system (INS) including gyroscopes and accelerometers is used to measure the robot’s attitude and acceleration for modifying the dynamic attitude of the robot. Moreover, a zero moment point (ZMP) compensator is used to on-line adjust the gait trajectories to improve t...

The inverse kinematics problems of redundant manipulators have been investigated for many years. The conventional method of solving this problem analytically is by applying the Jacobian Pseudoinverse Algorithm. It is effective and able to resolve the redundancy for additional constraints. However, its demand for computational effort makes it not suitable for real-time control. Recently, neural ...

In this paper, a new adaptive fuzzy controller with CMAC-based adaptive scaling factors is proposed. By using the proposed method, scaling factors can be tuned on-line, and control performance can be improved. In addition, CMAC is used only for tuning scaling factors, so that it is easy to implement and other fuzzy parameters can be designed by conventional method. Furthermore, robustness for m...

The cerebellar model articulation controller (CMAC) neural network has the advantages of fast convergence speed and low computation complexity. However, it suffers from a low storage space utilization rate on weight memory. In this paper, we propose a direct weight address mapping approach, which can reduce the required weight memory size with a utilization rate near 100%. Based on such an addr...

The cerebellar model articulation controller (CMAC) is a simple and fast neural-network based on local approximations. However, its rigid structure reduces its accuracy of approximation and speed of convergence with heterogeneous inputs. In this paper, we propose a generalized CMAC (GCMAC) network that considers different degrees of generalization for each input. Its representation abilities ar...

This paper presents a fast highly regular digit-serial complex-number multiplier-accumulator (CMAC) architecture which is well suited for V LSI implementations. This paper makes two contributions. First, several complex-number representation schemes are discussed. It is shown that the real-imaginary alternate (RIA) scheme is the best among all representation schemes and the prior designs of CMA...