Cerebellar Model Articulation Controller Research Articles

Self-Structured Organizing Single-Input CMAC Control for De-icing Robot Manipulator

This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.

Efficient Recursive Least Squares Methods forthe CMAC Neural Network

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.

ADRC and CMAC combined optimization and control for a class of discrete-time uncertain chaotic systems

Immune dynamic particle swarm optimization (IDPSO) strategy integrated with active disturbance rejection control (ADRC) and cerebellar model articulation controller (CMAC) combined control is designed for uncertain nonlinear discrete-time chaotic systems. The ADRC-CMAC is comprised of a cerebellar model articulation controller (CMAC) and an ADRC controller. The ADRC controller is designed to guarantee the stability of the system and restrict the disturbance. The CMAC is used to guarantee the control precision and response speed. Immune binary-state particle swarm algorithm is used to tune online the parameters of the ADRC-CMAC. Simulation results of uncertain nonlinear discrete-time systems demonstrate that performance with favorable response speed and restrained disturbance can be achieved by using the proposed control system.

Neural Network Control for the Linear Motion of a Spherical Mobile Robot

This paper discusses the stabilization and position tracking control of the linear motion of an underactuated spherical robot. Including the actuator dynamics, the complete dynamic model of the robot is deduced, which is a third order, two-variable nonlinear differential system that holds underactuation, strong coupling characteristics brought by the mechanism structure of the robot. Different from traditional treatments no linearization is applied, whereas a single-input multiple-output PID (SIMO_PID) controller is proposed with a neural network controller to compensate the actuator nonlinearity. A six-input single-output CMAC_GBF (Cerebellar Model Articulation Controller with General Basis Function) neural network is employed, while the Credit Assignment (CA) learning method is adopted to obtain faster convergence rate than the classical backpropagation (BP) learning method. The proposed controller can be generalizable to other single-input multiple-output system with good real-time capability. MATLAB simulations are used to validate the control effects.

Development of robust intelligent tracking control system for uncertain nonlinear systems using H∞ control technique

In this paper, a robust intelligent tracking control (RITC) system employs an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is developed for uncertain nonlinear system to achieve H ∞ tracking performance. The proposed dynamic structure of ORCMAC has superior capability in efficient learning mechanism and dynamic response. Temporal relations are embedded in conventional cerebellar model articulation controller (CMAC) by adding feedback connections between the output space and input space, so that the ORCMAC captures the system dynamic. In the RITC design, the Taylor linearization technique is employed to increase the learning ability of ORCMAC and the on-line adaptive laws are derived based on the Lyapunov stability analysis, the sliding mode control methodology and the H ∞ control technique so that the stability of the closed-loop system and H ∞ tracking performance can be guaranteed. Finally, the proposed control system is applied to control an inverted pendulum system, a Van der Pol oscillator and a Genesio chaotic system. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performances for the uncertain nonlinear systems with unknown dynamic functions and under the occurrence of external disturbance.

Robust intelligent backstepping control system using RCMAC for tracking periodic trajectories

Since the backstepping control technique is a systematic and recursive design methodology for nonlinear systems, a robust intelligent backstepping control (RIBC) system is proposed in this study to control a linear ultrasonic motor (LUSM) for the tracking of periodic reference trajectories. In the proposed control system, an adaptive recurrent cerebellar model articulation controller (RCMAC) is used to mimic an ideal backstepping control and a robust controller is designed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. The proposed adaptive RCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The online adaptation laws of the RIBC system are derived in the sense of the Lyapunov function, the Taylor linearization technique and the H ∞ control approach, so that the stability of the system and robust tracking performance can be guaranteed. Finally, the effectiveness of the proposed control system is indicated in comparison with an integral–proportional (IP) position control system. Experimental results show that high-precision tracking performance can be achieved by using the proposed RIBC system.

Rotor Speed Identification of PMSM on DTC System Based on PSO and CMAC Neural Network Algorithms

Both the PSO (particle swarm optimization) for global search algorithm and the CMAC (cerebella model articulation controller) algorithm are used in the speed loop of direct torque control system in the permanent magnet synchronous motor. Firstly, the PSO algorithm is applied to search the optimal PID parameters in the domain space, and then the CMAC neural network is adopted to learn and train of the results which derive from the output of the PSO algorithm to furthermore optimize the PID parameters which can improve stability of the system. The DTC control system based on PSO with CMAC algorithmic and the traditional DTC (direct torque control) control system are established and simulated in the MATLAB circumstance whose results were all compared. It revels that the DTC control system based on PSO with CMAC algorithmic is fast response, ideal flux, and good anti-jamming.

ART-type CMAC network classifier

In this paper, the learning process of ART 2 (adaptive resonant theory) network is applied to construct the structure of cerebellar model articulation controller (CMAC) to form an ART-type CMAC network. The proposed updating rule is in an unsupervised manner as the ART 2 network or the self-organizing map (SOM), and could equally distribute the learning information into the association memory locations as the CMAC network. If the winner fails a vigilance test, a new state is created; otherwise, the memory contents corresponding to the winner are updated according to the learning information. Like SOM, the proposed network also has a neighborhood region, but the neighborhood region is implicit in the network structure and need not be defined in advance. This paper also analyzes the convergence properties of the ART-type CMAC network. The proposed network is applied to solve data classification problems for illustration. Experiment results demonstrate the effectiveness and feasibility of the ART-type CMAC network in solving five benchmark datasets selected from the UCI repository.

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H ∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.

P-CMAC-Based Control Method and its Application in Filament Tension Control

Cerebella Model Articulation Controller (CMAC) is considered as local association and generalization neural network. Parametric CMAC (P-CMAC) is a modification to the original CMAC. The introduction of continuous activation function applied in the input space can overcome the binary behavior of the original CMAC. Takagi-Sugeno(TS) type fuzzy inference is embedded in the internal mapping to improve the approximation accuracy. Hybrid control scheme, which combines P-CMAC neural network and traditional PID controller, is proposed in the paper. The output of P-CMAC network dominates the overall control signal applied to the plant, while the traditional PID controller serves as compensator for reducing tracking error. The application of hybrid control scheme to filament tension control is illustrated. The experimental results have shown the effectiveness and accuracy improvement of the control scheme.

Intelligent backstepping control for wheeled inverted pendulum

The adaptive output recurrent cerebellar model articulation control (AORCMAC) is an adaptive system with simple computation, good generalization capability and fast learning property. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. In this study, an intelligent backstepping tracking control system is proposed for wheeled inverted pendulums (WIPs) with unknown system dynamics and external disturbance. In this control system, an ABORCMAC is used to copy an ideal backstepping control (IBC), and a compensated controller is designed to compensate for difference between the IBC law and AORCMAC. Moreover, all adaptation laws of the proposed system are derived based on the Lyapunov stability analysis, the Taylor linearization technique, so that the stability of the closed-loop system can be guaranteed.

MIMO CMAC neural network classifier for solving classification problems

Developing an efficient classification method is a challenge task in many research domains, such as neural network (NN) classifiers, statistical classifiers and machine learning. This study focuses on NN classifiers, which are data-driven analytical techniques. This study presents a cerebellar model articulation controller NN (CMAC NN) classifier, which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. To increase the accuracies of training and generalization, the CMAC NN classifier is designed with multiple-input and multiple-output (MIMO) network topology. The performance of the proposed MIMO CMAC NN classifier is evaluated using PROBEN1 benchmark datasets (such as for diabetes, cancer and glass) taken from the UCI Machine Learning Repository. Numerical results indicate that the proposed CMAC NN classifier is efficient for tested datasets. Moreover, this study compares the experimental results of the CMAC NN classifier with those in the published literature, indicating that the CMAC NN classifier is superior to some published classifiers. Therefore, the CMAC NN classifier can be considered as an analytical tool for solving classification tasks, such as medical decision making.

The Resistance-Heated Furnace Temperature Control Based on CMAC-Fuzzy Immune PID Control

The control demand can not be satified by general PID algorithm because of time-change, nonlinear, uncertainty of controlled parameters in control process of the resistance-heated furnace. Based on regulative fuction of bilogic immune feedback response and characteristic of overhang nonlinear fuction with fuzzy discursion, and combining the advantages of CMAC(Cerebellar Model Articulation Controller), the CMAC-Fuzzy Immune-PID control strategy is applied to temperature control system of the resistance-heated furnace. Simulation study results show that control systems based on this method can attain high quality, is capable of adapting itself to variations of the control object’s parameters, and is featured by strong robustness and self-adaptability.

Adaptive Filter Design Using Recurrent Cerebellar Model Articulation Controller

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

SYNCHRONIZATION OF CHAOTIC GYRO SYSTEMS USING RECURRENT WAVELET CMAC

This study addresses synchronization of two chaotic gyros by using an adaptive recurrent wavelet cerebellar model articulation controller (RWCMAC). The proposed adaptive RWCMAC system contains an RWCMAC and a robust controller. Based on Lyapunov stability theory, the parameters of RWCMAC are on-line tuned and the robust controller is designed for achieving H ∞ robust performance. Finally, the proposed adaptive RWCMAC system is applied to synchronize two chaotic gyros. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

Intelligent robust tracking control for multi-arm mobile manipulators using a fuzzy cerebellar model articulation controller neural network

This article originally analyses intelligent robust tracking for multi-arm fruit-harvesting mobile manipulators (MAFHMMs) with delayed angle-velocity uncertainties. The MAFHMMs are composed of two parts: a crawler-type mobile platform and a four-arm harvesting manipulator. The method proposed here does not require a matching condition for the non-linear uncertainties. A fuzzy cerebellar model articulation controller (CMAC) neural network system is used to approximate an unknown controlled system from the strategic manipulation of the model following the tracking errors. In addition, an adaptive robust compensator is presented to compensate for the uncertainties. Based on the Lyapunov stability theory and neural network approximation capability, several sufficient conditions are derived, which guarantee the convergence of the closed-loop error system. Both simulation and experimental results show the superior control performance of the proposed intelligent control method.

Forecasting ATM cash demands using a local learning model of cerebellar associative memory network

Abstract Forecasting cash demands at automatic teller machines (ATMs) is challenging, due to the heteroskedastic nature of such time series. Conventional global learning computational intelligence (CI) models, with their generalized learning behaviors, may not capture the complex dynamics and time-varying characteristics of such real-life time series data efficiently. In this paper, we propose to use a novel local learning model of the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative memory network to produce accurate forecasts of ATM cash demands. As a computational model of the human cerebellum, our model can incorporate local learning to effectively model the complex dynamics of heteroskedastic time series. We evaluated the forecasting performance of our PSECMAC model against the performances of current established CI and regression models using the NN5 competition dataset of 111 empirical daily ATM cash withdrawal series. The evaluation results show that the forecasting capability of our PSECMAC model exceeds that of the benchmark local and global-learning based models.

Study of Neuron Sliding Mode Position Control Based on CMAC

Combining cerebella model articulation controller with sliding mode control, a novel control algorithm is designed. It adopts neuron PID controller as the teacher signal of CMAC. The control error rapidly tends to zero by adjusting weight of each corresponding unit in CMAC storage. The control algorithm eliminates the chattering in the conventional sliding mode control. It also has high real-time, strong robustness and better convergence. The control algorithm is applied on position servo system of NC machining tool. The experimental results show that it improves dynamic performance and static performance of position servo system. It can realize rapid and exact position servo control of NC machine tool.

The Design and Implementation of a Wheeled Inverted Pendulum Using an Adaptive Output Recurrent Cerebellar Model Articulation Controller

A novel adaptive output recurrent cerebellar model articulation controller (AORCMAC) is utilized to control wheeled inverted pendulums (WIPs) that have a pendulum mounted on two coaxial wheels. This paper focuses mainly on adopting a self-dynamic balancing control strategy for such WIPs. Since the AORCMAC captures system dynamics, it is superior to conventional CMACs in terms of efficient learning and dynamic response. The AORCMAC parameters are adjusted online using the dynamic gradient descent method. The learning rates of the AORCMAC are determined using an analytical method based on a Lyapunov function, such that system convergence is achieved. The variable and optimal learning rates are derived to achieve rapid tracking-error convergence. A WIP standing control is utilized to experimentally verify the effectiveness of the proposed control system. Experimental results indicate that WIPs can stand upright stably with external disturbances via the proposed AORCMAC.

Embedded support vector regression on Cerebellar Model Articulation Controller with Gaussian noise

In this study, an approach utilizing support vector regression (SVR) as the learning scheme of a Cerebellar Model Articulation Controller (CMAC) to handle noisy data is proposed. This approach is referred to as SVR-CMAC. Firstly, the memory-associated vector is transformed via the SVR model. Then, the output is computed from the SVR model as a given input of a CMAC. That is, the memory size of the proposed SVR-CMAC depends on the number of support vectors. It is difference from the conventional CMAC and the kernel CMAC that mainly depends on the number of input variables. Secondly, in order to measure the distance between two memory-associated vectors (i.e. unipolar binary input data), the modified Hamming distance is used in the proposed SVR-CMAC. That is, the modified Hamming distance measure is incorporated into the kernel function in the SVR model. Furthermore, the existed SVR software is easily modified to implement the SVR approach with these new Gaussian kernel functions. Besides, some easy approaches to determine the hyperparameters of the proposed SVR-CMAC are also proposed. Consequently, the proposed SVR-CMAC solves once a linearly constrained quadratic programming problem to obtain the final results. However, the final results of the conventional CMAC and the kernel CMAC need to update the weights with iteration. Finally, from the simulation results, the performance of the proposed SVR-CMAC is better than the conventional CMAC and the kernel CMAC for noisy data.