A Wheeled Mobile Robot Path-Tracking System Based on Image Processing and Adaptive CMAC

CMAC-based adaptive backstepping synchronization of uncertain chaotic systems

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

This paper proposes a new robust adaptive cerebellar model articulation controller (CMAC) neural network-based multisliding mode control strategy for a class of unmatched uncertain nonlinear systems. Specifically, by employing a stepwise recursion-based multisliding mode method, such a proposed strategy is able to obtain the virtual variables and the actual control inputs of each order first, and then it reduces the conservativeness for controller parameter design by adopting the CMAC neural network to learn both system uncertainties and virtual control variable derivatives of each order online. Meanwhile, with the hyperbolic tangent function being chosen to replace the sign function in the variable structured control components, the proposed strategy is able to avoid the chattering effects caused by the discontinuous inputs. The stability analysis shows that the proposed control strategy ensures that both the system tracking errors and the sliding modes of each order could converge exponentially to any saturated layer being set. The control strategy was also applied onto a passive electrohydraulic servo loading system for verifications, and simulation results show that such a proposed control strategy is robust against all system nonlinearities and external disturbances with much higher control accuracy being achieved.

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.

Optimal and intelligent motion control scheme for an Ultrasonic-Motor-Driven X-Y stage

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.

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.

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.

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 describes an image-based visual servo tracking control scheme using CMAC neural network as object recognition feedback methodology. A web camera based image capture system is mounted on the slider robot to capture the desired object and a CMAC (Cerebellar Model Articulation Controller) based object recognition scheme is developed to recognize the captured object image. Comparing the relative location of the recognized object and the web camera, then the position feedback signal can be obtained. Using the feedback signal, a PID (Proportional-Integral-Derivative) controller is designed to track the desired object by moving a single-axis slider robot, such that the captured object image located on the center of the captured frame always.

This work deals with a design scheme of the speed control for a 3-mass system, which has a hybrid structure of a sliding mode controller and a cerebellar model articulation controller (CMAC). The nonlinear control part in the sliding mode controller is firstly designed, and then a real-coded genetic algorithm is utilized to optimize the output from the sliding mode controller. The CMAC compensates the sliding mode controller so that the control performance is improved well. The behavior of the newly proposed control scheme is experimentally examined in comparison with using only the sliding mode controller.

Versions of the proportional-integral-derivative (PID) controllers are most widespread type of controllers used to control plants, in achieving satisfactory performance. Versions of PID controller are also used for controlling robotic plants. With increased speed of operation and more stringent performance requirements PID controller alone will not suffice. Control performance improve significantly by adding second controller that computes necessary driving torque from current operating condition, based on robot model. Two types of cerebellum based controllers where tested in acquiring robot model. First one is standard Albus cerebellar model articulation controller (CMAC), and second one is new cerebellar model controller with added functionality for some of cerebellar neuron types, namely Golgi cell, basket cell, and stellate cell (CMACgbs). Physical modeling of robot joint with SimMechanics was used as testing plant for rapid prototyping. Cerebellar controller features addressed are size of the neural network, learning process, and generalization.

This paper proposes the design of fuzzy logic controllers to revolve a wheeled mobile robot (WMR) through digital images, and the design of WMR anti-collision system. After the image processing, the authors utilize the center of object image to design the fuzzy logic controllers that incorporate expert knowledge in terms of 9 linguistic rules to drive the WMR to revolve. By the way, the WMR can execute auto moving and anti-collision via infrared sensors. The program codes of fuzzy logic controllers are implemented on a PC which could send all the motion commands to the WMR by wireless communication.

Proportional-integral-derivative (PID) control is widely applied in industries, though it is sometimes difficult to apply to nonlinear systems. In contrast, database-driven PID (DD-PID) control has been proposed as a control method to adjust adaptive PID parameters. However, although this method can efficiently construct control systems with high control performance for nonlinear systems, its large computational cost in terms of computation time and memory consumption limits its application to relatively slow-response systems such as some process control systems. Therefore, in this paper, the cerebellar model articulation controller (CMAC) is introduced, and a new method to realize the database constructed by DD-PID with CMAC memory is proposed. Finally, numerical simulation examples are employed to quantitatively evaluate the reduction of computation time and memory usage, which verifies the effectiveness of the proposed method.