Considering human visual system and characteristics of images, a novel image fusion strategy is presented for panchro matic high resolution image and multispectral image in nonsubsampled contourlet transform (NSCT) domain. The NSCT can give an asymptotic optimal representation of edges and contours in image by virtue of its characteristics of good multiresolution, shift-invariance, and high directionality. An intensity component addition strategy based on LHS transform is introduced into NSCT domain to preserve spatial resolution and color content. Experiments show that the fusion method proposed can improve spatial resolution and keep spectral information simultaneously, and that there are improvements both in visual effects and quantitative anal ysis compared with the traditional principle component analysis (PCA) method, intensity-hue-saturation (IHS) transform technique, wavelet transform weighted fusion method, corresponding wavelet transform-based fusion method, and contourlet transform-based fusion method.
For improving the estimation accuracy and the convergence speed of the unscented Kalman filter (UKF), a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. On the basis of the MIT rule, an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function. The updated covariance is fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations. The asymptotic properties of this adaptive UKF are discussed. Simulations are conducted using an omni-directional mobile robot, and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.
A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path, and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.
This paper proposes a practical generalized predictive control (GPC) algorithm based on online least squares support vector machines (LS-SVM) which can deal with nonlinear systems effectively. At each sampling period the algorithm recursively modifies the model by adding a new data pair and deleting the least important one out of the consideration on realtime property. The data pair deleted is determined by the absolute value of lagrange multiplier from last sampling period. The paper gives the recursive algorithm of model parameters when adding a new data pair and deleting an existent one, respectively, and thus the inversion of a large matrix is avoided and the memory can be controlled by the algorithm entirely. The nonlinear LS-SVM model is applied in GPC algorithm at each sampling period. The experiments of generalized predictive control on pH neutralizing process show the effectiveness and practicality of the proposed algorithm.
This paper considers the problem of delay-dependent robust stabilization for uncertain singular delay systems. In terms of linear matrix inequality (LMI) approach, a delay-dependent stability criterion is given to ensure that the nominal system is regular, impulse free, and stable. Based on the criterion, the problem is solved state feedback controller, which guarantees that the resultant closed-loop system is regular, impulse free, and stable for all admissible uncertainties. An explicit expression for the desired controller is also given. Some numerical examples are provided to illustrate the validity of the proposed methods.
This paper considers the cooperative tracking of linear multi-agent systems with a dynamic leader whose input information is unavailable to any followers. Cooperative iterative learning controllers, based on the relative state information of neighboring agents, are proposed for tracking the dynamic leader over directed communication topologies. Stability and convergence of the proposed controllers are established using Lyapunov-Krasovskii functionals. Furthermore, this result is extended to the output feedback case where only the output information of each agent can be obtained. A local observer is constructed to estimate the unmeasurable states. Then, cooperative iterative learning controllers, based on the relative observed states of neighboring agents, are devised. For both cases, it is shown that the multi-agent systems whose communication topologies contain a spanning tree can reach synchronization with the dynamic leader, and meanwhile identify the unknown input of the dynamic leader using distributed iterative learning laws. An illustrative example is provided to verify the proposed control schemes.
The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.
In this paper, the finite-time consensus problems of heterogeneous multi-agent systems composed of both linear and nonlinear dynamics agents are investigated. Nonlinear consensus protocols are proposed for the heterogeneous multi-agent systems. Some sufficient conditions for the finite-time consensus are established in the leaderless and leader-following cases. The results are also extended to the case where the communication topology is directed and satisfies a detailed balance condition on coupling weights. At last, some simulation results are given to illustrate the effectiveness of the obtained theoretical results.
A nonlinear control is proposed for trajectory tracking of a 6-DOF model-scaled helicopter with constraints on main rotor thrust and fuselage attitude. In the procedure of control design, the mathematical model of helicopter is simplified into three subsystems: altitude subsystem, longitudinal-lateral subsystem and attitude subsystem. The proposed control is developed by combining the sub-controls for the corresponding subsystems. The sub-controls for altitude subsystem and longitudinal-lateral subsystem are designed with hyperbolic tangent functions to satisfy the constraints; the sub-control for attitude subsystem is based on backstepping technique such that fuselage attitude tracks the virtual control for longitudinal-lateral subsystem. It is proved theoretically that tracking errors are ultimately bounded, and control constraints are satisfied. Performances of the proposed controller are demonstrated by simulation results.
In this paper, we present a system for real-time performance-driven facial animation. With the system, the user can control the facial expression of a digital character by acting out the desired facial action in front of an ordinary camera. First, we create a muscle-based 3D face model. The muscle actuation parameters are used to animate the face model. To increase the reality of facial animation, the orbicularis oris in our face model is divided into the inner part and outer part. We also establish the relationship between jaw rotation and facial surface deformation. Second, a real-time facial tracking method is employed to track the facial features of a performer in the video. Finally, the tracked facial feature points are used to estimate muscle actuation parameters to drive the face model. Experimental results show that our system runs in real time and outputs realistic facial animations. Compared with most existing performance-based facial animation systems, ours does not require facial markers, intrusive lighting, or special scanning equipment, thus it is inexpensive and easy to use.
This paper proposes a sensor fault diagnosis method for a class of discrete-time linear time-varying (LTV) systems. In this paper, the considered system is firstly formulated as a descriptor system representation by considering the sensor faults as auxiliary state variables. Based on the descriptor system model, a fault estimation filter which can simultaneously estimate the state and the sensor fault magnitudes is designed via a minimum-variance principle. Then, a fault diagnosis scheme is presented by using a bank of the proposed fault estimation filters. The novelty of this paper lies in developing a sensor fault diagnosis method for discrete LTV systems without any assumption on the dynamic of fault. Another advantage of the proposed method is its ability to detect, isolate and estimate sensor faults in the presence of process noise and measurement noise. Simulation results are given to illustrate the effectiveness of the proposed method.
The box constraints in image restoration have been arousing great attention, since the pixels of a digital image can attain only a finite number of values in a given dynamic range. This paper studies the box-constrained total-variation (TV) image restoration problem with automatic regularization parameter estimation. By adopting the variable splitting technique and introducing some auxiliary variables, the box-constrained TV minimization problem is decomposed into a sequence of subproblems which are easier to solve. Then the alternating direction method (ADM) is adopted to solve the related subproblems. By means of Morozov's discrepancy principle, the regularization parameter can be updated adaptively in a closed form in each iteration. Image restoration experiments indicate that with our strategies, more accurate solutions are achieved, especially for image with high percentage of pixel values lying on the boundary of the given dynamic range.
This paper concerns the delay-dependent robust stability problem of uncertain neutral systems with mixed neutral and discrete delays. Nonlinear time-varying parameter perturbations are considered. Based on the newly established integral inequalities, the neutral-delay-dependent and discrete-delay-dependent stability criterion is derived without using a fixed model transformation. The condition is presented in terms of linear matrix inequality and can be easily solved by existing convex optimization techniques. A numerical example is given to demonstrate the less conservatism of the proposed results.
On the basis of a new dynamic linearization technology along the iteration axis, a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems. Dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively. The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system. In other words, we can easily select the control parameters without knowing any other knowledge of the system. Simulation study illustrates the geometrical convergence of the presented method along the iteration axis, in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance.
Fractional order proportional-integral-derivative (FOPID) controller generalizes the standard PID controller. Compared to PID controller, FOPID controller has more parameters and the tuning of parameters is more complex. In this paper, an improved artificial bee colony algorithm, which combines cyclic exchange neighborhood with chaos (CNC-ABC), is proposed for the sake of tuning the parameters of FOPID controller. The characteristic of the proposed CNC-ABC exists in two folds: one is that it enlarges the search scope of the solution by utilizing cyclic exchange neighborhood techniques, speeds up the convergence of artificial bee colony algorithm (ABC). The other is that it has potential to get out of local optima by exploiting the ergodicity of chaos. The proposed CNC-ABC algorithm is used to optimize the parameters of the FOPID controller for an automatic voltage regulator (AVR) system. Numerical simulations show that the CNC-ABC FOPID controller has better performance than other FOPID and PID controllers.
Using the analogy between the discrete time axis and the iterative learning axis, a new discrete-time adaptive iterative learning control (AILC) approach is developed to address a class of nonlinear systems with time-varying parametric uncertainties. Analogous to adaptive control, the new AILC can incorporate a projection algorithm, thus the learning gain can be tuned iteratively along the learning axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis.
For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, estimators of the noise variances are obtained, and under the linear minimum variance optimal information fusion criterion weighted by diagonal matrices, a self-tuning information fusion Kalman predictor is presented, which realizes the self-tuning decoupled fusion Kalman predictors for the state components. Based on the dynamic error system, a new convergence analysis method is presented for self-tuning fuser. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is strictly proved that if the parameter estimation of the MA innovation models is consistent, then the self-tuning fusion Kalman predictor will converge to the optimal fusion Kalman predictor in a realization, or with probability one, so that it has asymptotic optimality. It can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.
In this paper, a method of fault estimation and fault tolerant control for networked control system (NCS) with transfer delay and process noise is presented. First, the networked control system is modeled as a multiple-input-multiple-output (MIMO) discrete-time system with transfer delays, process noise, and model uncertainties. Under this model and under some conditions, a fault estimation method is proposed to estimate the system faults. On the basis of the information on fault estimation and the sliding mode control theory, a fault tolerant controller is designed to recover the system performance. Finally, simulation results are used to verify the efficiency of the method.
In this paper, output-feedback adaptive stabilization is investigated for a class of nonlinear systems with unknown control directions. First, through a linear state transformation, the unknown control coefficients are lumped together and the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of an observer and an estimator for state and parameter estimates, respectively, a constructive design procedure is given for the output-feedback adaptive stabilizing controller using integrator backstepping and tuning function techniques. It is shown that the controller designed ensures the original system state converges to the origin whereas all the other closed-loop system states are bounded. Simulation results are illustrated to show the effectiveness of the proposed approach.
This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle (UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or off-line computational power, while the mapping is done off-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.