Cerebellar Model Articulation Controller Research Articles

Function-Link Fuzzy Cerebellar Model Articulation Controller Design for Nonlinear Chaotic Systems Using TOPSIS Multiple Attribute Decision-Making Method

This paper aims to propose a more efficient control algorithm to select suitable firing nodes, improve the computational efficiency, reduce the number of firing rules and achieve good performance for nonlinear chaotic systems. A novel function-link fuzzy cerebellar model articulation controller (FLFCMAC) is designed by using a multiple attribute decision-making method named as technique for order of preference by similarity to ideal solution (TOPSIS). The TOPSIS is used to determine the optimal threshold values for receptive-field basis function in association memory space such that the firing fuzzy rules can be effectively reduced. In the TOPSIS design, the Shannon entropy index is used to derive the objective weights of the evaluation attribute. The proposed control system is composed of a TOPSIS-based FLCMAC (TFLFCMAC) and a fuzzy compensator. The TFLFCMAC is the main tracking controller employed to mimic an ideal controller, and the fuzzy compensator can eliminate the approximation error between the TFLFCMAC and the ideal controller. The parameters of the proposed TFLFCMAC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so that the system’s stability is guaranteed. Finally, the proposed control system is applied to a Duffing–Holmes chaotic system and a gyro chaotic system to illustrate its favorable control performance and to show its superiority to the other control techniques.

Diesel Engine Combustion Control Based on Cerebellar Model Articulation Controller (CMAC) in Feedback Error Learning

The trend started in 1997 with the introduction of common rail injection and after some protocols set new targets for overall CO2 emissions. As the diesel engine emits less CO2 than its gasoline counterpart, it kept conquering more and more market shares. Conventional diesel engine control design is mainly based on the maps techniques which required too much time, money and human resources under the number of experiments under various environmental conditions. This makes increasing system complexity. In our authors group, we have proposed that the control structure has the feedback error learning, two-degree-of-freedom controller configuration, with advanced neural networks (NNs) as the feedforward controller along the model-based control method. On the other hand, a cerebellar model articulation controller (CMAC) is a non-fully connected perceptron like associative memory network with overlapping receptive fields, which is used to resolve problems that involve rapid growth and the learning difficulty. Then CMACs have the advantages of good generalization capability, fast learning ability, and simple computation. To our best knowledge, this is new introduce the cerebellar model articulation controller (CMAC) for the control diesel engine combustion control. The effectiveness of the proposed method will be confirmed through numerical simulations based on the Tokyo University diesel engine model with triple fuel injections.

A Novel Constant Output Powers Compound Control Strategy for Variable-Speed Variable-Pitch Wind Turbines

The output power of wind turbine should be maintained at rated value when wind speed exceeds rated speed.However, the wind turbine is a system with large nonlinearity and time delay. Therefore, it is difficult for conventional linear controllers to realize effective control. In order to solve this problem, a combination of feedforward control with feedback control is developed. In feedback loop, feedback linearization theory is adopted to overcome the nonlinear features, and sliding mode algorithm is employed to improve the robustness of the system. On the other hand, in feedforward loop, a cerebellar model articulation controller is implemented to approximate the system. Furthermore, an improved credit assignment method is used to adjust the weight of the network. Together these procedures improve the effectiveness of the algorithm. Through this method, not only can the stability of the nonlinear model be ensured, but also the control lag caused by the time-delayed system can be compensated. Simulation results show that the feedforward-feedback compound control algorithm can both keep the output power at rated value and improve dynamic response speed, reduce adjustment time, has small steady-state error.

Design and adaptive control of omni-directional mobile platform with four driving wheels

In this paper, a general kinematic model of the four drive wheel omnidirectional mobile platform is established, and the CMAC (Cerebellar model articulation controller) +PID joint control strategy is used to design the embedded adaptive control of the omnidirectional mobile platform in view of the problem that the conventional control can not be self-tuning online and the real-time response of the response needs to be improved. The MATLAB simulation and experimental analysis of DC motor speed regulation were carried out, and the motion performance of the prototype was tested by a series of typical experiments. The results show that the kinematic model of the mecanum wheel omnidirectional moving platform is reasonable. The dynamic response of the CMAC+PID adaptive controller is fast, the control precision is high, and the robustness is good. The prototype can achieve the horizontal / vertical translation, the original rotation and the omni-directional motion in the plane. The overall performance can meet the requirements of the engineering application.

Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC provides enhanced modeling capability based on channel characteristics.

Robust Adaptive Tracking Control for Manipulators Based on a TSK Fuzzy Cerebellar Model Articulation Controller

The robot manipulator system is a complicated system with multiple-input and multiple-output, high nonlinearity, strong coupling, and uncertainties, such as parameter disturbances, external interference, and unmodeled dynamics. A robust adaptive Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller (RATFC) is proposed and applied to a robot manipulator to achieve high-precision position and speed control. A Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller is adopted, and the parameters are regulated by the derived adaptable rules according to a Lyapunov function. The robust compensation controller mitigates approximation-based errors. Finally, simulation results show that the proposed RATFC can achieve improved tracking performance compared with other neural network controllers.

A Neuro-Fuzzy Visual Servoing Controller for an Articulated Manipulator

The challenges of selecting appropriate image features, optimizing complex nonlinear computations, and minimizing the approximation errors always exist in visual servoing. A fuzzy neural network controller is developed for a six-degrees-of-freedom robot manipulator to perform visual servoing is proposed to tackle these problems. To increase the accuracy of the image preprocesses, a synthetic image process performs feature extraction for the controller. The method combines a support vector machine contour recognition algorithm and a color-based feature recognition algorithm. For visual servoing, a control method based on the fuzzy cerebellar model articulation controller with the Takagi–Sugeno framework is proposed to directly map an image feature error vector to a desired robot end-effector velocity. This approach achieves visual servoing control without the need of computing the inverse interaction matrix. The control variables are learned and updated by the T–S fuzzy inference. This simplifies the implementation of visual servoing in real-time applications. The proposed control method is used to control an articulated manipulator with an eye-in-hand configuration. The results of simulations and experiments demonstrate that the proposed visual servoing controller has good performance, in terms of stability and convergence.

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Abstract Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds.

Load-aware multicast routing in multi-radio wireless mesh networks using FCA-CMAC neural network

Multicasting is a useful network service in wireless mesh networks (WMNs) for delivering same data from a source to multiple destinations. An effective multicast routing protocol in multi-channel multi-radio WMNs (MCMR-WMNs) is required to satisfy the following criteria together: high network throughput, low end-to-end delay, low tree cost, low computational time, and load-aware routing. Furthermore, how to fully exploit channel diversity in MCMR-WMNs to accomplish low channel interference criteria is a critical issue in designing multicast routing protocol. In spite of its significance, multicast routing which satisfies all of the mentioned criteria, has not drawn much attention so far. Besides, major multicast routing protocols proposed in MCMR-WMNs are centralized or solve two problems of multicast tree construction and channel assignment sequentially. These protocols are time-consuming in addition to suffering from a single-point-of-failure. In this paper, we propose a distributed cross-layer algorithm for joint multicast routing and channel assignment in MCMR-WMNs. For the first time, we apply fuzzy credit assigned cerebellum model articulation controller (FCA-CMAC) neural network model to construct multicast routing tree considering load on the mesh nodes and the delay between neighboring mesh nodes. Moreover, we present a heuristic channel assignment algorithm aiming to reduce interference among the links of the multicast tree. FCA-CMAC converges quickly and creates minimal delay and load-aware multicast tree. Therefore, proposed method can optimize the network throughput, end-to-end delay, tree cost, and computational time. Additionally, channel assignment algorithm is subject to produce the minimal interference multicast tree. Simulation results show that in terms of aforementioned criteria, the proposed FCA-CMAC based multicast algorithm achieves better performance than those comparative references.

Fuzzy cerebellar model articulation controller network optimization via self-adaptive global best harmony search algorithm

Fuzzy cerebellar model articulation controller (FCMAC) networks with excellent nonlinear appropriation ability and simple implementation are used to solve complex uncertainties problems in engineering applications. Both online and off-line learning algorithm of FCMAC networks usually applies the gradient-descent-type methods. However, such gradient-descent methods lead to the high possibility to converging into local minima. To cope with the local minimum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parameters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC network’s weight variables, but also optimizes network receptive field’s center position and standard deviation parameters. In order to obtain an optimal network, the weight values, center positions, and standard deviations are transformed to three data strings that can be processed by harmony search algorithm. In particular, the self-adaptive global best harmony search algorithm (SGHS) is used to search optimal parameter combinations of FCMAC within solution domains. The network’s performances are verified by approximating six nonlinear formulae. In order to compare the performances of the FCMAC networks optimized by the SGHS algorithm, a back-propagation trained network and another harmony search variant optimized networks are also tested in this work. The experimental results show that the networks optimized by SGHS perform the faster convergence speed and better accuracy.

Position and angle control for a two-wheel robot

In this paper, a two-wheel robot (TWR) system is implemented based on a position and angle decoupled intelligent backstepping control system. The decoupled method provides a simple method to achieve asymptotic stability control for a fourth-order nonlinear TWR system using a second-order dynamics model. Moreover, adaptive output recurrent cerebellar model articulation control (AORCMAC) is designed in this study to imitate an ideal backstepping controller (IBC). Furthermore, to compensate for the difference between the two controllers mentioned, a compensator is used. Based on the Lyapunov stability method, the stability of the closed-loop system can be guaranteed. To train the AORCMAC effectively, the variable learning rates that guarantee the convergence of the system are proposed. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method for TWR control.

Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting

This paper proposes a method to prevent overlearning (weight drift) and bursting in adaptive control using the cerebellar model arithmetic computer (CMAC). Traditional robust adaptive methods such as deadzone, projection, e-modification are not necessarily suitable for CMAC control. The proposed method relies on the idea of weight smoothing, where the difference between adjacent weights in the CMAC is penalized in the adaptation. Previously proposed weight smoothing schemes are only suitable for one or two-input CMACs and do not have stability guarantees. This work extends the method for use with n-input CMACs and develops the adaptive scheme within a Lyapunov framework to guarantee uniformly ultimately bounded signals. Simulations with a two-link, flexible-joint robot subject to sinusoidal disturbance demonstrate the performance and stability of the new method.

WCMAC-based control system design for nonlinear systems using PSO

This paper presents a wavelet cerebellar model articulation controller (WCMAC) based adaptive control design for nonlinear systems using a particle swarm optimization (PSO). The WCMAC is the main tracking controller and a robust compensation controller is used to compensate for the residual error. In the WCMAC, the adaptive laws of controller parameters are derived using the gradient descent method. However, the initial values of learning rates of these adaptive laws are very important and they affect much to the performance of control systems. In this paper, the particle swarm optimization algorithm is applied to find the optimal learning rates of the parameter adaptation laws. To show the effectiveness of the proposed approach, numerical simulations of magnetic levitation system and inverted pendulum are provided to confirm the applications of the proposed PSO-WCMAC-based control system. The superiority of the proposed control scheme is also evaluated by quantitative comparison with other control schemes.

Design of cross-coupled CMAC for contour-following – a reinforcement-based ILC approach

ABSTRACTOne of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logic-based RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC.

Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization

In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed group-based strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wall-following control than other methods in unknown environments.

A KCMAC-PD controller with reduced memory and optimized mapping for the torque control of electric load simulator

An electric load simulator requires high demand on control precision and dynamic performance due to its inherent non-linearity and external interference of surplus torque. The cerebellar model articulation controller (CMAC) is a simple, fast and promising neural network with good performance. However, there still exist some problems in the CMAC network, such as large memory, over-learning and complex mapping. This paper introduces the kernel method in CMAC to form KCMAC, by using a third-order B-spline as a kernel function, so that the mapping of CMAC is transferred from the feature space to the kernel space. This method may effectively reduce the storage space as well as the computational complexity. A compound controller with KCMAC and PD (proportional–derivative) is designed with improvement on learning speed for the torque control of an electric load simulator. Compared with the conventional CMAC-PD control strategy, the KCMAC-PD has improved the control precision by 40.4%, 40.8%, 14.1% and 30.5% at a loading frequency of 0.5 Hz, 1 Hz, 1.5 Hz and 2 Hz in the experiments, respectively. The dynamic simulation and experimental results of KCMAC-PD show that this control strategy may ensure loading precision and avoid over-learning of CMAC. They also demonstrate that KCMAC has ability to smooth control output and restrain external disturbances.

Optimal synchronization of fractional-order chaotic systems subject to unknown fractional order, input nonlinearities and uncertain dynamic using type-2 fuzzy CMAC

In this paper, a new robust optimal control strategy is presented to synchronize a class of fractional-order chaotic systems with unknown fractional orders, uncertain dynamics and input nonlinearities. The dynamics of the system are estimated by an integer-order model, using proposed non-singleton type-2 fuzzy cerebellar model articulation controller (NST2-CMAC). The free parameters of NST2-CMAC are adjusted based on the adaptation laws which are derived from Lyapunov stability analysis. To show the effectiveness of the proposed method, two simulation examples are provided and the results are compared with other methods.

Control of an innovative super-capacitor-powered shape-memory-alloy actuated accumulator for blowout preventer

The design of a super-capacitor-powered shape-memory-alloy (SMA) actuated accumulator for blowout preventer (BOP) presented in this paper featured several advantages over conventional hydraulic accumulators including instant large current drive, quick system response and elimination of need for the pressure conduits. However, the mechanical design introduced two challenges, the nonlinear nature of SMA actuators and the varying voltage provided by a super capacitor, for control system design. A cerebellar model articulation controller (CMAC) feedforward plus PID controller was developed with the aim of compensation for these adverse effects. Experiments were conducted on a scaled down model and experimental results show that precision control can be achieved with the proposed configurations and algorithms.

Diesel Engine Combustion Control with Triple Fuel Injections based on Cerebellar Model Articulation Controller (CMAC) in Feedback Error Learning

Diesel Engine Combustion Control with Triple Fuel Injections based on Cerebellar Model Articulation Controller (CMAC) in Feedback Error Learning

Preventing Overlearning in CMAC by Using a Short-Term Memory

This paper proposes improvements to the cerebellar model articulation controller (CMAC). Often, the CMAC plays the role of a fuzzy nonlinear approximator in direct adaptive control. However, in applications where the inputs oscillate the CMAC exhibits more drift in the adaptive parameters (overlearning) compared to other types of fuzzy approximators and neural networks and is thus more likely to cause bursting. This is due to the local nature of CMAC fuzzy membership functions: even a small oscillation of the trajectory across the origin and through different membership functions results in repeated positive or negative updates for each adaptive parameter. The solution proposed in this paper uses a short-term memory: a membership function remains activated during the indexing of subsequent CMAC cells. This ensures a small oscillation will occur entirely within membership functions rather than between them. The method uses Gaussian radial basis functions. Simulation results with an underdamped flexible joint demonstrate the improved performance and stability characteristics.