This paper presents a convex optimization method for observer-based mixed $H_2/H_{\infty}$ control design of linear systems with time-varying state, input and output delays. Delay-dependent sufficient conditions for the design of a desired observer-based control are given in terms of linear matrix inequalities (LMIs). An observer-based controller which guarantees asymptotic stability and a mixed $H_2/H_{\infty}$ performance for the closed-loop system of the linear system with time-varying delays is then developed. A Lyapunov-Krasovskii method underlies the observer-based mixed $H_2/H_{\infty}$ control design. A numerical example with simulation results illustrates the effectiveness of the methodology.

This paper considers the problem of delay-dependent guaranteed cost controller design for uncertain neutral systems with distributed delays. The system under consideration is subject to norm-bounded time-varying parametric uncertainty appearing in all the matrices of the state-space model. By constructing appropriate Lyapunov functionals and using matrix inequality techniques, a state feedback controller is designed such that the resulting closed-loop system is not only robustly stable but also guarantees an adequate level of performance for all admissible uncertainties. Furthermore, a convex optimization problem is introduced to minimize a specified cost bound. By matrix transformation techniques, the corresponding optimal guaranteed controller can be obtained by solving a linear matrix inequality. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed approach.

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

The control of underactuated mechanical system is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural properties are analyzed in detail. A genetic algorithm (GA)based stable control approach is proposed for the class of under actuated mechanical systems. The Lyapunov stability theory and system properties are utilized to guarantee the system stability to its equilibrium. The real-valued GA is used to adjust the controller parameters to improve the system performance. This approach is applied to the underactuated double-pendulum-type overhead crane and the simulation results illustrate the complex system dynamics and the validity of the proposed control algorithm.

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

This paper presents a method for reducing modelling error in the linear motion system with voicecoil actuator (VCA). A model of linear motion system composed of a mechanism and control was prepared to verify the proposed method. In modeling of the system, the damping coefficient obtained experimentally is applied to the model in order to consider the effect of the viscous friction for the moving part in VCA. The response velocity of VCA for duty ratio of PWM signal was analyzed in the time domain. Consequently, the relation between velocity and duty ratio was obtained. The result from the experiment showed an error of 9% when compared with that of simulation. In order to reduce the modeling error, impedance variation according to input frequency was analyzed, and equivalent impedance with multi-frequency was applied to the control part. As a result, the modeling error decreased to 5%.

Vision-based simultaneous localization and map building using a single camera, while compelling in theory, have not until recently been considered extensive in the practical realm of the real world. In this paper, we propose a simple framework for the monocular SLAM of an indoor mobile robot using natural line features. Our focus in this paper is on presenting a novel approach for modeling the landmark before integration in monocular SLAM. We also discuss data association improvement in a particle filter approach by using the feature management scheme. In addition, we take constraints between features in the environment into account for reducing estimated errors and thereby improve performance. Our experimental results demonstrate the feasibility of the proposed SLAM algorithm in real-time.

Motivated by the fact that in many industrial robots the joint velocity is estimated from position measurements, the trajectory tracking of robot manipulators with output feedback is addressed in this paper. The fact that robot actuators have limited power is also taken into account. Let us notice that few solutions for the torque-bounded output feedback tracking control problem have been proposed. In this paper we contribute to this subject by presenting a theoretical reexamination of a known controller, by using the theory of singularly perturbed systems. Motivated by this analysis, a redesign of that controller is introduced. As another contribution, we present an experimental evaluation in a two degrees-of-freedom revolute-joint direct-drive robot, confirming the practical feasibility of the proposed approach.

Most of the previously employed dynamic modeling approaches, including Natural Orthogonal Complement Algorithm, have limitations on their application to the mobile robot, specifically at singular configurations. Also, in their dynamic modeling of mobile robots, wheel dynamics is usually ignored assuming that its dynamic effect is negligibly small. As a remedy for this, a singularity-free operational space dynamic modeling approach based on Lagrange's form of the D' Alembert principle is proposed, and the singularity-free characteristic of the proposed dynamic modeling is discussed in the process of analytical derivation of the proposed dynamic model. Then an accurate dynamic model taking into account the wheel dynamics of the omni-directional mobile robot is derived, and through simulation it is manifested that the effect of the wheel dynamics on the whole dynamic model of the mobile robot may not be negligible, but rather in some cases it is significantly large, possibly affecting the operational performances of dynamic model-based control algorithms. Lastly, the importance of its accurate dynamic model is further illustrated through impulse analysis and its simulation for the mobile robot.

The transposed Jacobian is proposed to transform the control space from task space to joint space, in this paper. Instead of inverse Jacobian, the transposed Jacobian is preferred to avoid singularity problem, short real time calculations and its generality to apply for rectangular Jacobian. On-line Jacobian identification is proposed to cancel parametric errors produced by D-H parameters of manipulator. To identify Jacobian, the joint angles and the end-effector position are measured when tracking a desired trajectory in task space. Stability of control system is analyzed. The control system is simulated for position control of a two-link manipulator driven by permanent magnet dc motors. Simulation results are shown to compare the roles of inverse Jacobian and transposed Jacobian for transforming the control space.

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

This paper examines the use of a wireless Fieldbus based on IEEE 802.15.4 MAC protocol. The superframe of IEEE 802.15.4 is applied to a transmission scheme of real-time mixed data. The transmission and bandwidth allocation scheme are proposed for real-time communication using a superframe. The proposed wireless Fieldbus protocol is able to transmit three types of data (periodic data, sporadic data, and non real-time messages), and guarantee realtime transmission simultaneously within a limited timeframe.

As many systems depend on electronics, concern for fault tolerance is growing rapidly. For example, a car with its steering controlled by electronics and no mechanical linkage from steering wheel to front tires (steer-by-wire) should be fault tolerant because a failure can come without any warning and its effect is devastating. In order to make system fault tolerant, there has been a body of research mainly from aerospace field. This paper presents the structure of predictive hybrid redundancy that can remove most erroneous values. In addition, several numerical simulation results are given where the predictive hybrid redundancy outperforms wellknown average and median voters.

A new approach to $H_{\infty}$ multi-step prediction is developed by applying the innovation analysis theory. Although the predictor is derived by resorting to state augmentation, nevertheless, it is completely different from the previous works with state augmentation. The augmented state here is considered just as a theoretical mathematic tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. By using the reorganized innovation analysis, calculation of the estimator does not require any augmentation. A numerical example demonstrates the effect in reducing computing burden.

In this paper, we deal with the problem of reducing the order of the detection filter for the linear time-invariant system. Even if the detection filter is generally designed in the form of full order linear observer, we show that it is possible to reduce its order when the response of fault signals is limited to a subspace of the estimation state space. We propose a method to extract the subspace using the observer canonical form considering the dynamics related to the remaining subspace acts as a disturbance. We designed a reduced order detection filter to reject the disturbance as well as to guarantee fault detection and isolation. A simulation result for a 5th order system is presented as an illustrative example of the proposed design method.