Recurrent Cerebellar Model Articulation Controller Research Articles

Neural longitudinal control of hypersonic vehicles with constrained aerodynamic surfaces

This paper presents a neural adaptive flight control for longitudinal dynamics of air-breathing hypersonic vehicles (AHVs) with constrained aerodynamic surfaces. Multiple actuator constraints including magnitude, rate, and first-order dynamic model in both the elevator and canard are transformed into a specific control allocation problem, which can be readily solved using the standard model predictive control (MPC) technique. Furthermore, an adaptive control scheme is developed combining with the above control allocation and the recurrent cerebellar model articulation controller (RCMAC), which well handles actuator constraints and uncertain factors including aerodynamic coefficients, external disturbances, and flexible dynamics. Numerous simulation results verify performance and robustness of the proposed neural adaptive control.

An Innovative Approach for Water Distribution Systems

Water Distribution System (WDS) is one of the important phases of the Water Treatment Plant (WTP) and plays a crucial role in plant, animal, and human life. The WDS aims not only to supply a continuous, stable water amount but also to reduce energy consumption as little as possible during operation. To keep the continuous, stable water amount, the water pressure in the pipe network of the WDS must be maintained at desired set points under the effecting of uncertainties, disturbances, and noises. For saving the energy requirement, a Variable Frequency Driver (VFD) was utilized to control the speed of the Water Boost Pump (WBP) as speed as the WDS requires and a Recurrent Cerebellar Model Articulation Control System (RCMACS) was proposed to keep the water pressure at desired reference under abnormal events. Furthermore, to adapt to the industrial environment, the RCMACS was implemented on the Allen-Bradly Programmable Logic Controller (PLC), and industrial networks were also proposed to connect and transfer data between control stations and field devices.

Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control

The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi–Sugeno–Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.

Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.

A Redundant Recurrent Cerebellar Model Articulation Control System for Industrial Applications

Because of increasing reliability requirements of the applications in industrial areas, the control systems must guarantee the stability and robustness during operation and continuously maintain control and supervisory process. In order to meet the above requirements, this study proposes a redundant recurrent cerebellar model articulation control system (RRCMACS) for controlling industrial applications, in particular the water level and the pressure in a tank. Therein, the stability and the robustness of the applications are guaranteed by the recurrent cerebellar model articulation controller (RCMAC) and the redundant solution maintains continuously control and supervisory process. To adapt industrial applications, a Programmable Logic Controller (PLC) was used to build the control system and industrial networks were used to transfer data between the control system and field devices.

Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO

A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for non-linear systems using the genetic particle swarm optimization (GPSO) algorithm is presented in this study. The RARC is used as the principal tracking controller and the robust compensation controller is designed to recover the residual of the approximation error. In the RARC neural network, the steepest descent gradient method and the Lyapunov function are used for deriving the adaptive law parameter of the system. Besides, the learning rates play an important role in these adaptive laws and they have a great effect on the functions of control systems. In this paper, the combination of the genetic algorithm with the mutation particle swarm optimization algorithm is applied to seek for the optimal learning rates of the RARC adaptation laws. The numerical simulations about the inverted pendulum system as well as the robot manipulator system are given to confirm the effectiveness and practicability of the GPSO-RARC-based control system. Compared with other control schemes, the proposed control scheme is testified to be reliable and can obtain the optimal parameter about the learning rates and the minimum root mean square error for non-linear systems.

Robust Recurrent Cerebellar Model Articulation Controller for Non-Linear MIMO Systems

This research proposes a robust recurrent cerebellar model articulation control system (RRCMACS) for MIMO non-linear systems to achieve the robustness of the system during operation. In this system, the superior properties of the recurrent cerebellar model articulation controller (RCMAC) are incorporated to imitate an ideal sliding mode controller. The robust controller efficiently attenuates the effects of uncertainties, external disturbances, and noises to maintain the robustness of the system. The parameters of the controller were updated in the sense of the Lyapunov-like Lemma theory. Therefore, the stability and robustness of the system were guaranteed. The simulation results for the micro-motion stage system are given to prove the effectiveness and applicability of the proposed control system for model-free non-linear systems.

Adaptive RCMAC Neural Network Dynamic Surface Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table

In this paper, a robust adaptive dynamic surface control (RADSC) is proposed with a recurrent cerebellar model articulation controller (RCMAC) based on the function link neural network (FLNN) for two-axis X-Y table derived by two permanent-magnet synchronous motors (PMSMs) servo drives. Initially, an optimal computed torque controller (OCTC) is used to ensure the stability of the two-axis X-Y table system. But there may be destroyed in the control performance due to the presence of parameter uncertainties. This might be due to the fact that linear optimal control possesses inherent robustness within a specified spectrum of model uncertainties. Due to the above reasons, the RADSC is developed according to the system requirements in order to increase the robustness of the control system. In the proposed control scheme, a dynamic surface controller (DSC) and an RCMAC uncertainty observer with a robust controller are combined to constitute the RADSC scheme. In the proposed RADSC, the DSC is utilized to get rid of the complexity of explosion present in the backstepping design. Furthermore, the RCMAC uncertainty observer is developed such that it can approximate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMAC. Based on the Lyapunov stability analysis, the online adaptive control laws are derived. The experimental results confirm that the x-axis and y-axis motions are controlled separately, whereas it can be concluded that the proposed RADSC's dynamic behaviors accomplish robust tracking performance though there were parameter uncertainties.

Fault diagnosis approach of rolling bearing based on NA‐MEMD and FRCMAC

This paper proposed a new method of fault diagnosis based on Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Fuzzy Recurrent Cerebellar Model Articulation Controller (FRCMAC) Neural Networks. Aiming at the problem that during the use of the NA-MEMD method, the white noise amplitude parameter needs to be selected by artificial experience, a method of using Genetic Algorithm (GA) to optimize its auxiliary white noise parameters is proposed, which facilitates the use of NA-MEMD. We proposed a novel FRCMAC structure which improved Learning efficiency and dynamic response speed than traditional CMAC structure. First, the GA-NA-MEMD method is applied to process the vibration signals of rolling bearings, and the signals are decomposed into a group of Intrinsic Mode Functions (IMFs). Then use energy moments of IMFs as fault feature vectors to train FRCMAC neural network, a neural network structure suitable for rolling bearing fault diagnosis is obtained. Finally, the data from bearing data center of Case Western Reserve University is used to prove that the fault diagnosis method proposed in this paper is superior to other methods in diagnosis time and precision, which can meet the training requirements more quickly with limited training samples and fault diagnosis results more accurate.

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.

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

In this study, an efficient navigation control method of mobile robot is proposed. The proposed navigation control method consists of behavior manager, toward goal behavior, and wall-following behavior. According to the relative position between the mobile robot and the environment, the behavior manager switches to determine toward goal behavior or wall-following behavior of mobile robot. A novel recurrent fuzzy cerebellar model articulation controller based on an improved dynamic artificial bee colony is proposed for performing wall-following control of mobile robot. The proposed improved dynamic artificial bee colony algorithm uses the sharing mechanism and the dynamic identity update to improve the performance of optimization. A reinforcement learning method is adopted to train the wall-following control of mobile robot. Experimental results show that the proposed method obtains a better navigation control than other methods in unknown environment.

Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.

Intelligent Robust Control for Uncertain Nonlinear Multivariable Systems using Recurrent Cerebellar Model Neural Networks

This paper develops an intelligent robust control algorithm for a class of uncertain nonlinear multivariable systems by using a recurrent-cerebellar-model- articulation-controller (RCMAC) and sliding mode technology. The proposed control algorithm consists of an adaptive RCMAC and a robust controller. The adaptive RCMAC is a main tracking controller utilized to mimic an ideal sliding mode controller, and the parameters of the adaptive RCMAC are on-line tuned by the derived adaptive laws from the Lyapunov function. Based on the

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

Friction and uncertainty compensation of robot manipulator using optimal recurrent cerebellar model articulation controller and elasto-plastic friction observer

A model-free control scheme with the elasto-plastic friction observer is presented for robust and high-precision positioning of a robot manipulator. The traditional model-based adaptive controller requires information on the robotic dynamics in advance and thus undergoes robustness problem because of complex dynamics and non-linear frictions of a robot system. This problem is overcome by an employed model-free recurrent cerebellar model articulation controller (RCMAC) system and friction estimator for friction and uncertainty compensation of a robot manipulator. The adaptive laws of the RCMAC networks to approximate an ideal equivalent sliding mode control law and adaptive friction estimation laws based on the elasto-plastic friction model are derived based on the Lyapunov stability analysis. To guarantee stability and increase convergence speed of the RCMAC network, the optimal learning rates are obtained by the fully informed particle swarm (FIPS) algorithm. The robust positioning performance of the proposed control scheme is verified by simulation and experiment for the Scorbot robot in the presence of the joint dynamic friction and uncertainty.

Hybrid recurrent cerebellar model articulation controller-based supervisory H ∞ motion control system for permanent-magnet synchronous motor servo drive

This study proposes a hybrid supervisory H∞ control system using a recurrent cerebellar model articulation controller (RCMAC) for achieving high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drive. The hybrid control system is composed of a RCMAC, a supervisory controller and a robust H∞ controller. The RCMAC is used as the main position tracking controller to mimic a perfect computed torque control law, and the robust H∞ controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RCMAC and the ideal controller. The supervisory controller is designed based on the uncertainty bound of the PMSM to stabilise the system states around a predefined bound region and achieve satisfactory tracking performance. The online adaptive control laws are derived based on the Lyapunov stability analysis, so that the stability of the system can be guaranteed. A computer simulation is developed and an experimental system is established to validate the effectiveness of the proposed hybrid control system. All control algorithms are implemented in a TMS320C31 digital signal processor-based control computer. The simulated and experimental results confirm that the proposed hybrid control system grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

Robust intelligent backstepping tracking control for wheeled inverted pendulum

In this study, a robust intelligent backstepping tracking control (RIBTC) system combined with adaptive output recurrent cerebellar model articulation controller (AORCMAC) and H ∞ control technique is proposed for wheeled inverted pendulums (WIPs) with unknown system dynamics and external disturbance. The AORCMAC is a nonlinear adaptive system with simple computation, good generalization capability and fast learning property. Therefore, the WIP can stand upright when it moves to a designed position stably. In the proposed control system, an AORCMAC is used to copy an ideal backstepping control, and a robust H ∞ controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level. Moreover, the all adaptation laws of the RIBTC system are derived based on the Lyapunov stability analysis, the Taylor linearization technique and H ∞ control theory, so that the stability of the closed-loop system and H ∞ tracking performance can be guaranteed. The proposed control scheme is practical and efficacious for WIPs by simulation results.

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.

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.