Adaptive Hybrid Control for Linear Piezoelectric Ceramic Motor Drive Using Diagonal Recurrent CMAC Network

Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter. Third, after descripting the chaotic signal, the embedded information message can be recovered. Besides, the stability problem is analyzed in detail based on the stability theory. Finally, two simulation examples, including audio signal and image, are introduced to illustrate the effectiveness and the advantages of the proposed method.

Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor

This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN's potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.

Microdispensing system via the contacting method

This paper presents the development of a microdispensing system based on a contacting method, with an aim to lowering production and maintenance cost. The liquid, to be dispensed, is brought into contact with the work piece, thus dispensing a droplet by making use of the adhesion force between the liquid and the work piece. A piezoelectric actuator is employed as the drive for the system to achieve high precision. The control of the system is accomplished with a PID controller; controlling the dispensing process and trajectory tracking.

Robust neural network motion tracking control of piezoelectric actuation systems for micro/nanomanipulation

This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.

Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) via sliding-mode approach for a class of nonlinear systems. The proposed SAFNC system is comprised of a computation controller and a supervisory controller. The computation controller including a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller. The SOFNN identifier is used to online estimate the controlled system dynamics with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure learning phase possesses the ability of online generation and elimination of fuzzy rules to achieve optimal neural structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The supervisory controller is used to achieve the L2-norm bound tracking performance with a desired attenuation level. Moreover, all the parameter learning algorithms are derived based on Lyapunov function candidate, thus the system stability can be guaranteed. Finally, simulation results show that the SAFNC can achieve favorable tracking performances.

RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology

A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat's lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system.