Robust Adaptive Control of Linear Induction Motors With Unknown End-Effect and Secondary Resistance

This paper proposes a novel robust adaptive speed/ position tracking control for a linear induction motor (LIM) with both end-effect and secondary resistance unknown. The practical current-fed controlled LIM, with residual current error, is considered, i.e., the traditional ideal current-loop assumption is relaxed. More practical conditions, such as bounded primary voltage and a finite absolute-integral of current tracking error is considered. To overcome the high nonlinearity and nonzero current error, a backstepping method, combining virtual desired variable synthesis, is developed for the speed and position tracking. Then, the controller achieves asymptotic speed and position tracking even with unknown parameters and immeasurable secondary flux. Furthermore, the effect of the residual current error is attenuated in an L₂ -gain sense. The experiments for several scenarios are carried out to verify the theoretical result.     

Semi-decentralized adaptive fuzzy control for cooperativemultirobot systems with H∞ motion/internal forcetracking performance

We present a semi-decentralized adaptive fuzzy control scheme for cooperative multirobot systems to achieve H(infinity) performance in motion and internal force tracking. First, we reformulate the overall system dynamics into a fully actuated system with constraints. To cope with both parametric and nonparametric uncertainties, the controller for each robot consists of two parts: 1) model-based adaptive controller; and 2) adaptive fuzzy logic controller (FLC). The model-based adaptive controller handles the nominal dynamics which results in both zero motion and internal force errors for a pure parametric uncertain system. The FLC part handles the unstructured dynamics and external disturbances. An H(infinity) tracking problem defined by a novel performance criterion is given and solved in the sequel. Hence, a robust controller satisfying the disturbance attenuation is derived being simple and singularity-free. Asymptotic convergence is obtained when the fuzzy approximation error is bounded with finite energy. Maintaining the same results, the proposed controller is further simplified for easier implementation. Finally, the numerical simulation results for two cooperative planar robots transporting an object illustrate the expected performance.     

Intelligent multi-sensor control system based on innovative technology integration via ZigBee and Wi-Fi networks

Data transmission network integration is one of the most difficult problems to solve in wireless sensor network systems. ZigBee and Wi-Fi belong to different network protocols. If a network system must use both ZigBee and Wi-Fi at the same time to transmit data a considerable challenge is presented. This paper introduces a novel hardware method that integrates ZigBee and Wi-Fi. The proposed method is based on the Arduino wearable module ZigBee and Ethernet concept. This study builds an intelligent home appliance control system using the ZigBee network. This intelligent control system uses an integrated ZigBee and Wi-Fi network architecture in the house. Our study sends the ZigBee sensor messages to a cloud database through the TCP/IP protocol network containing the physical network and wireless network device lines. Control management access is achieved using smart phones. The proposed method is very simple and easy to implement using Arduino circuits. The effectiveness of the proposed method is verified by the simulation and experimental results. The hardware components include the Arduino controller, XBee Series 2 wireless communication module and end device sensors. The Android and Java programming languages are used to write the smart phone and Server recognition programs.     

Induction motor control with friction compensation: an approach of virtual-desired-variable synthesis

In this paper, the speed control problem of induction motors suffering from substantial friction force is considered. Here, a semi-current-fed model for induction motors and LuGre's dynamic model for friction force are used. To reflect practical situations, rotor resistance, torque load, and friction parameters are assumed to be unknown. In the design methodology, a double-observer structure is applied to estimate the immeasurable friction states. On the other hand, in light of the principles of vector control and field orientation, a set of virtual desired variables (VDVs) are introduced to synthesize the control law. Therefore, using only measurable signals of rotor speed, stator voltage and current, an asymptotic adaptive tracking controller is designed. Numerical simulations and experiments are carried out to verify the theoretical results and show satisfactory performance.     

Sliding-mode motion/force control of constrained robots

A sliding-mode controller is proposed for the simultaneous position and force control of constrained robot manipulators with parametric uncertainties. Based on this controller, the trajectories of the closed-loop system can reach a stable sliding surface in finite time. Under this condition, the asymptotic convergence of the motion error and force error can be successfully ensured with improved results compared to previous studies     

LMI-based Integral fuzzy control of DC-DC converters

In this paper, we propose a T-S fuzzy controller which combines the merits of: i) the capability for dealing with nonlinear systems; ii) the powerful LMI approach to obtain control gains; iii) the high performance of integral controllers; iv) the workable rigorous proof for exponential convergence of error signals; and v) the flexibility on tuning decay rate. The output regulation problems of a basic buck converter and a zero-voltage-transition (ZVT) buck converter are used as application examples to illustrate the control performance of the proposed methodology. First, we consider a general nonlinear system which can represent the large-signal models of the converters. After introducing an added integral state of output regulation error and taking coordinate translation on an equilibrium point, the resulting augmented system is represented into a Takagi-Sugeno (T-S) fuzzy model. Then, the concept of parallel distributed compensation is applied to design the control law whereby the control gains are obtained by solving linear matrix inequalities (LMIs). An interesting result is that the obtained control law is formed only by the linear state feedback signals weighted by grade functions. In addition, the robustness analysis is carried out when uncertainty and disturbance are taken into consideration. The performance of numerical simulations and practical experiments results is satisfactory.     

Hybrid Fuzzy Model-Based Control of Nonholonomic Systems: A Unified Viewpoint

This paper proposes a unified hybrid fuzzy model-based control scheme for uncertain nonholonomic systems. Compared with typical hybrid fuzzy control, the stability analysis is performed based on a new concept of constructing a semicommon Lyapunov function and a new definition called as exponential-like model following. This advancement provides a strict stability analysis but results in relaxed gain conditions. In detail, a unified hybrid Takagi-Sugeno fuzzy model is first introduced for representing well-known nonholonomic systems with a momentum conservation constraint or a no-slip constraint. Then, the hybrid fuzzy controller is derived to ensure robust nonlinear model following control, i.e., an asymptotic convergence with adjustable ultimate bound and arbitrary disturbance attenuation in an -gain sense. Furthermore, an iterative linear matrix inequality technique is proposed to guarantee the stability and avoid the need of a common positive-definite matrix. Finally, the applications are carried out on a hopping robot and a car-like mobile robot. Numerical simulations and experiment results show the expected performances.     

A skew-symmetric property of rigid-body systems

The configuration space for the attitude of a vehicle can be modeled as SO(3), namely the rotation matrix group. This work investigates the globally valid dynamics of natural Lagrangian systems and gyroscopic systems with configuration space including SO(3). The dynamics is derived by using the global representations of jet bundles of SO(3). A skew-symmetric property associated with the systems can be then established. Such property can be used in many applications such as the adaptive controller design.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Smart home safety handwriting pattern recognition with innovative technology

Intelligent home security control equipment has become indispensable in daily life. This study uses an innovative handwriting recognition technology to confirm user identity and to manage door security. Smart phones are used for security system management and control actions, and identity confirmation is performed in two stages. In the first stage, the user enters a user name and password into a smart phone and waits for successful identity recognition. In the second stage, the user must write a text or symbol pattern previously set into the smart phone. After successful identification in the second stage, the user is given permission to control the electronic lock and enter the door. The proposed home security system is implemented using a RNN (Recurrent Neural Network) with associative memory for training and recognition. The RNN used in the handwriting recognition step performs some recognition iterative processes.