A Robust Adaptive CMAC Neural Network-Based Multisliding Mode Control Method for Unmatched Uncertain Nonlinear Systems

This study entailed applying image-processing techniques and an intelligent control method to a wheeled mobile robot (WMR) for real-time trajectory recognition and tracking. The WMR was fitted with a charge-coupled device (CCD) camera to capture images of the environment, and the hue-saturation-intensity color space was used to classify the color features of the captured images. The WMR calculates the relative position of a target object by using image-processing and edge-detection algorithms. This paper proposes a cerebellar model articulation controller (CMAC) and adaptive CMAC to replace conventional proportional-integral-derivative (PID) controller for guiding the WMR to track a desired path. Moreover, using Lyapunov's stability theory to optimize the learning rate ensures that the proposed control system is stable. The tracking performance of several controllers used to track various path patterns were compared. Finally, according to the theoretical analysis, simulations, and experimental results of this study, the proposed control scheme is effective.

Robust intelligent backstepping tracking control for uncertain non-linear chaotic systems using H∞ control technique

Vanadium Redox Battery (VRB) has the advantages of large capacity, long life, safety and environmentally friendly. This paper proposes a double closed-loop charge-discharge control strategy of VRB based on cerebellar model arithmetic computer (CMAC) to ensure safe charge-discharge and shorten the charge-discharge time. The proposed control strategy consists of two main control loops: an outer voltage loop and an inner current loop. The outer control loop is designed as a voltage loop with CMAC controller which uses kernel voltage for feedback, and the inner current loop adopts PI regulator with amplitude limiting. In this paper, we first build a mathematical model of VRB of 5 kW/30kW· h. Then, the proposed control strategy and conventional PID control strategy are respectively applied to VRB model and comparisons are made. Results show that under the proposed control strategy, charging time of VRB shortened by 3.03%, discharging time of VRB shortened by 9.37% compared with conventional PID control strategy, and the terminal voltage can be guaranteed in a safe range, thus realizing fast and safe charge-discharge of VRB.

CMAC-based adaptive backstepping synchronization of uncertain chaotic systems

An adaptive cerebellar model articulation controller (CMAC) is proposed for command to line-of-sight (CLOS) missile guidance law design. In this design, the three-dimensional (3-D) CLOS guidance problem is formulated as a tracking problem of a time-varying nonlinear system. The adaptive CMAC control system is comprised of a CMAC and a compensation controller. The CMAC control is used to imitate a feedback linearization control law and the compensation controller is utilized to compensate the difference between the feedback linearization control law and the CMAC control. The online adaptive law is derived based on the Lyapunov stability theorem to learn the weights of receptive-field basis functions in CMAC control. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Then the adaptive CMAC control system is designed to achieve satisfactory tracking performance. Simulation results for different engagement scenarios illustrate the validity of the proposed adaptive CMAC-based guidance law.

There is an increasing trend in using exoskeleton to walk or carry payload. In the process of human-exoskeleton interaction, since the user's intention is required accurately, an important problem is human-robot collaborative control. A precise position control strategy will significantly enhance collaboration between the exoskeleton and the user. This paper proposes a novel method combining sliding mode control (SMC) with Cerebellar model articulation controller (CMAC). The proposed method is applied in a lower extremity exoskeleton to track a desired trajectory. The switching surface in sliding mode control was determined by tracking error. A CMAC was designed as a compensator which considers uncertainty of the dynamical model. The dynamic of the lower extremity is derived by using Euler-Lagrange Equation. The proposed control strategy was proved stable with Lyapunov analysis and used for trajectory tracking in comparison with a conventional sliding mode control. Results with Matlab simulation shows that the proposed controller can achieve better tracking capability and robust performance.

This paper presents a novel control strategy employing the artificial neural network-based direct torque control (DTC) strategy and direct power control (DPC) strategy for permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). The Aeolos 3 kW wind system ratings are considered in this paper for real time system validation. The proposed control strategy combines artificial neural network (ANN)-based DTC as machine side control (MSC) strategy and ANN-based DPC as grid side control (GSC) strategy in order to track the maximum power and to regulate the active power, respectively. To generate the switching pulse for the converter in generator side and grid side, a neural network-based technique is employed. Furthermore, the DC link voltage is optimised using the GSC topology. The proposed controller provides a stable voltage and power which is an effective solution for grid integration and constant power flow from the generator system to the grid system. The proposed coordinate control strategy is validated using MATLAB/Simulink software to analysis the working condition. The experimental test bench is implemented using dSPACE to study the real time analysis of the proposed control strategy.

A mode-free airship altitude control strategy is proposed based on Q-learning algorithm and CMAC (Cerebellar Model Articulation Controller) network. First, we designed an adaptive method to establish MDP (Markov Decision Process) model of airship altitude control on the foundation of analyzing actual motions. Then, control strategy is learned online via Q-learning algorithm, and CMAC network is used to generalize the value function of action. Finally, typical control tasks are carried out using a simulated low altitude airship. Control strategy could be worked out without access to airship accurate dynamic model. Results show that the airship's MDP model parameters using the adaptive method are better matched with each other than using simple equally divided method, and CMAC networks could accelerate converge of algorithm. Control strategy's effectiveness and stability is demonstrated by comparing with a PID controller.

Ultrasonic motors (USMs) are widely used in many applications that precise and fast motions are required, such as precision machines and medical devices, etc. In this paper, a USM -driven ear surgical device is introduced. To address the control challenges of the USM, an intelligent controller consisting of a cerebellar model articulation controller (CMAC) and a sliding mode compensator (SMC) is designed. Therein, the CMAC serves as the main controller due to its fast learning ability while the SMC is used to eliminate the approximation error between the CMAC and the desired perfect controller. Several experiments are carried out to validate the effectiveness of the proposed control scheme, and the results show that the proposed control scheme is able to achieve good tracking performance and guaranteed robustness.

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.

This paper proposes a new and original course keeping control strategy for an unmanned surface vehicle in the presence of modeling error, external disturbance and input saturation. The trajectory linearization control method is used as the basic algorithm to design the course keeping strategy, and the radial basis function neural network and disturbance observer are used to compensate modeling error and external disturbance respectively to enhance the robustness of the control system. Moreover, a robust term is used to compensate various compensation errors to further improve the robustness of the system. In addition, hyperbolic tangent function and Nussbaum function are hired to deal with the potential input saturation problem, and the neural shunting model is adopted to avoid the computational explosion caused by the derivation of virtual control law. Taking the above facts into account will help to further realize engineering practice. Finally, the control strategy proposed in this paper is compared with the classical proportional–integral–derivative control strategy. The simulation results show that the course control results of the proposed control strategy are more robust than proportional–integral–derivative control, regardless of whether the external disturbance is weak or strong.

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 presents a notion of speed control of brushless DC (BLDC) motor through a compound control technique using CMAC (cerebellar model articulation controller) and PID controller. The proposed system provides effective starting and diminished cost and core loss. Compound controller realizes the dual-fed control which ensures stability and convergence of system. The initial rotor position is determined by estimating the voltage on sample resistor moreover the core loss are significantly diminished by using the ferrite material in stator core. The benefits like high control accuracy, increased efficiency, adaptability, decreased startup time and increased starting torque are attained in presented system.

This paper proposes a flexible enhanced power quality strategy for a multi-function distributed generation (DG) in an aggregated system. Being a multi-function DG system, the grid-interfaced inverter can provide very sinusoidal current and harmonic compensating current simultaneously. Different from the classic strategy where the compensating current is directly calculated by the measured harmonic content in the load current, the proposed power quality control strategy presents a more flexible way using a voltage-controlled approach based on the virtual impedance theory. As a result, each individual DG can provide harmonic compensation functionality independently and the power quality of grid current can be enhanced. This paper first develops an aggregated DGs model using continuous timedomain equivalent circuit. The classic and proposed power quality control strategies can then be derived from the developed models. The proposed control strategy is performed by the virtual harmonic impedance method, which can promote the output current of each individual DG itself and compensate harmonics caused by the other DGs. The proposed control strategy can be seamlessly incorporated into the conventional PR control scheme, and maintain both the stead-state and transient performance within the control bandwidth. Moreover, the proposed control strategy is less sensitive to the variable of the grid frequency. Experimental results are provided to validate the correctness of the proposed power quality strategy.

To meet the challenge of excellent tracking performance even under complicated nonlinear disturbance, a desired compensation neural network adaptive robust control (DCN-NARC) strategy is proposed for an industrial linear motor motion system. The proposed control scheme seriously concentrates on attenuating the effects of various model uncertainties and unknown nonlinearities, such as inertia load variation, force ripple, measurement noise, and external disturbances. Particularly, a three-layer radial basis function neural network (RBFNN) is employed to compensate the unknown nonlinearities. Online parameter adaptation is then utilized to reduce the effect of parametric uncertainties and to update the neural network weights. In addition, to facilitate the controller gain tuning process and attenuate the effect of measurement noise, the desired trajectory states are used in the adaptive model compensation following the desired compensation concept. The residual modeling error and unknown uncertainty are handled through certain robust control term. Theoretically, the proposed DCNNARC can achieve not only prescribed transient performance but also excellent tracking accuracy in general. Comparative experiments are conducted on an industrial linear motor system, and the results consistently demonstrate that the proposed scheme can achieve excellent tracking performance with remarkable disturbance rejection ability even under complicated disturbances. The proposed strategy also could be extended to other industrial control applications.