We consider a problem of trajectory planning for road-like path on the two-dimensional plane. As the basic tool for constructing trajectories, we employ smoothing splines using normalized uniform B-splines as the basis functions. The pass is assumed to possess piecewise linear boundaries, specified by a series of pairs of right and left corner points. We allow two-fold corner points in order to allow flexible description of the path. On constructing smoothing splines, we impose the boundary constraints as a collection of inequality pairs by right and left boundary lines, yielding a set of linear inequality constraints on the so-called control point vector. Unlike standard smoothing spline settings, a piecewise linear centerline of the given path is provided as the data for the trajectory to follow, where the given entire time interval is divided into subintervals according to the centripetal distribution rule. Other constraints, typically as the initial and final conditions, can be imposed on the trajectory easily, and we see that the problem reduces to strictly convex QP (quadratic programming) problem. Efficient QP solvers are available for numerical solution, and the effectiveness of the proposed method is confirmed by three examples: two with piecewise linear boundaries including an example of obstacle avoidance problem, and the third with piecewise linear approximation of circular boundaries.
Brain computer interfaces based on steady-state visually evoked potential (SSVEP-BCI) have been developed substantially in recent years, but these are not available to patients with severe paralysis or eye-impairment who lost the control ability of eye-movement. This paper proposes an eye-movement-independent SSVEP-BCI available in the eyes-closed state based on the modulation of SSVEP elicited by performing a mental task. Although performance of the proposed BCI depended on subjects, electrode locations, mental tasks employed, and flickering frequencies, the mean precision and recall, which were obtained from the confusion matrix, reached 72% to 95% using the support vector machine classifier across 18 normal subjects under the stimulus frequency of 10Hz or 14Hz. Results from simulated information transfer rate and its inter-individual difference suggest that it is adequate to set an inter-trial interval at 2s to 3s for better performance of the proposed BCI. It is consequently feasible to develop a practical eye-movement-control-independent BCI by optimizing the parameters such as the stimulation frequency and electrode sites each user.
This paper presents a novel unified approach of controller design and identification for unknown input affine nonlinear systems. An issue with obtaining the best performance of optimal control is that identification errors degrade control performance. One solution to overcome it is direct controller tuning without system identification, which is expected to give high quality control. However, many experiments with various control inputs are required in the design procedure. The proposed framework simultaneously implements system identification and controller design. This method adopts a weighted least squares method to cope with various identification criteria. An unknown plant system is identified with a weight selected appropriately for control. It is selected in a simple manner based on an analytical discussion of nonlinear optimal control theory, namely the Hamilton-Jacobi-Bellman equation. An iterative calculation procedure for a preliminarily given data set is derived. This iterative algorithm gives a (sub-)optimal pair of the weight and the model parameter that improves the control performance. Numerical results demonstrate that the proposed approach achieves better optimal control performance than the standard least squares method.
In this paper, we propose a parameter estimation method for nonlinear state-space models based on the variational Bayes. It is proved that the variational posterior distribution of the hidden states is equivalent to a posterior distribution of the states of an augmented nonlinear state-space model. This enables us to estimate the probability of the hidden states by implementing a variety of existing filtering and smoothing algorithms. Using this technique, a system identification algorithm for nonlinear systems based on variational Bayes and nonlinear smoothers is proposed. It is expected to be more accurate than the existing results since it does not employ any additional approximations in executing the variational Bayes inference. Furthermore, a numerical example demonstrates the effectiveness of the proposed method.
Since smartphones have been widely adopted by the general public and wireless fundamental infrastructure has been changed greatly, indoor positioning and location tracking methods have been applied to various kinds of service and applications. Pedestrian dead-reckoning (PDR) and a received signal strength indicator (RSSI)-based positioning method are widely used since they can be easily utilized by smartphone with build-in sensors such as accelerometer and gyroscope. However, estimation error would occur to PDR which caused by environmental interference and accumulation error in angular calculation. Moreover, RSSI measurements fluctuate according to external effects. In this paper, a novel indoor localization method based on access point (AP) selection is proposed. By assisting PDR with RSSI from stable APs, the heading angle can be updated periodically to reduce the error. Furthermore, in order to enhance the accuracy, the angle is calculated using quaternion. The experimental results show that the suitable APs selection can improve the accuracy effectively.
Nondestructive inspection of the sizes and positions of defect regions in a fuel cell is an indispensable task in order to ensure their practical use. In the present paper, a level set method for estimating the defects in a membrane electrode assembly (MEA) in fuel cells is proposed. Boundaries of the defects in the MEA are represented by a zero contour of a level set function. By updating the function, the shapes and positions of the defect regions can be identified. A cost function for updating the level set function consists of the mean squared error between the magnetic fields computed by a 2-D model and sensor data and the regularization terms for the estimated shapes. The magnetic fields were measured using magnetic impedance sensors. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.
A generic control method is proposed for the non-square systems where the number of system inputs is not equal to that of the states. The non-square system to be controlled is first restructured in form of the combination of a square system and the variation from the original non-square system. This variation term is treated as a time-varying uncertainty to the restructured square system. Thus the stabilization for a non-square system is reformulated as an adaptive control problem for a square system. In this paper we address this adaptive control problem by applying the function approximation technique. Specifically, we can parameterize the variation with a chosen basis function weighted by unknown constant parameters. Then we define an update law such that the parameters of the weighted basis function can be automatically determined and the variation between the auxiliary square system and the original non-square system can then be eliminated. The asymptotic stability is established for the closed loop system formulated by the non-square system and the constructed controller. The feasibility of the proposed control method is verified under simulations for linear system, nonlinear underactuated system, and nonholonomic system.
Multi-energy networks, which include electricity, heat, and fuel energies, have attracted attention in recent times. These energies should be traded in market dynamics to maximize total economic benefits. However, energy transmission constraints caused by heat and/or fuel network structure restrict trading with market transactions. Therefore, in this paper, a coordinated energy management system based on negotiated transaction mechanism is considered. Then, a transaction protocol based on marginal cost of energy accommodation is proposed. Some properties of a general multi-energy network of negotiated transactions are also discussed. Then, the spatiotemporal multi-energy network is proposed as a generalized energy transformation model, along with an investigation of its properties from the viewpoint of graph complexity with a simulation example.
In this paper, we address output synchronization for a network of heterogeneous agents with linear time invariant single-input-single-output dynamics in the presence of communication delays. To this end, we present a three-stage modularized design procedure: (i) a parallel feedforward compensator is designed to convert the dynamics to a minimum-phase system with relative degree one, (ii) the resulting system is then transformed into a feedback equivalent to a passive system, and (iii) the agents are interconnected based on a passivity-based output synchronization law. The benefit of the present control scheme is that not only implementation but also the design stage of the controller is distributed, namely the agents do not need to know the entire network structure. The present solution is then shown to achieve output synchronization. We finally demonstrate the present controller through simulation.
This paper presents gain scheduling (GS) control of a variable speed control moment gyroscope (VSCMG) based on sum of squares (SOS). Nonlinear motion equations of the VSCMG are complicated because they contain many trigonometric functions of angles of gimbals. The dynamics varies depending on the angles of the gimbals. In this study, the difficulty of control design of the VSCMG is solved by two methods. First, the complicated nonlinear model is transformed to the linear parameter varying model, such that the linear control method can be applied, to make control design easy by using a proposed approximation method. The sine function and the cosine function are generally approximated by the first-order Taylor series expansion in ordinary controller synthesis. The model obtained by the first-order Taylor series expansion ignores the nonlinear dynamics. But, in this study, those nonlinear functions are highly accurately approximated by using the proposed approximation method. The proposed approximation method is based on a high-order Taylor series expansion and a high-order Padé approximation. By using redundant representations, the synthesis condition can be reduced to polynomially parameter-dependent linear matrix inequalities (PDLMIs). Second, GS controller whose gains depend on the angles is applied. The polynomially PDLMIs can be relaxed to finite design conditions based on matrix SOS polynomials. The GS controller is designed by solving the finite SOS conditions. By using those methods, GS controller depending on the nonlinearities is designed. The effectiveness of the proposed controller is illustrated by simulations and experiments.
This study proposes a design method for unilateral control systems with communication rate constraints. In the case where it is necessary to control under a communication rate constraint, the effect of the quantization noise should be minimized using effective signal quantization methods. One of the effective methods for signal quantization is a feedback-type dynamic quantizer. We previously proposed a design method for a dynamic quantizer under communication rate constraints. In this paper, a unilateral control structure is proposed to minimize the effect of the quantization error. The design method for the quantizer is applied to the proposed structure. The effectiveness of the proposed system with the designed quantizer is assessed via numerical examples.