The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A new type of estimator that incorporates optimal control and outputs a control policy is developed and analyzed in this study. The estimator is developed in a similar manner to a Kalman Algorithm with an almost identical form, but has additional properties for more accurate tracking, maneuver detection, and maneuver reconstruction. Unlike the Kalman Algorithm, this estimator frees up the initial...
A high-dimensional Simultaneous Localization and Mapping (SLAM) algorithm is presented that replaces the particles in FastSLAM with individual Gaussians. In addition, the high-dimensional vehicle state is partitioned into linear and nonlinear parts and the nonlinear part is approximated by a mixture of Gaussians of which the means and covariances are propagated and updated using sparse grid quadrature...
The problem of dynamic output-feedback control is solved for linear retarded discrete-time state multiplicative stochastic systems. The stochastic white noise sequences multiply both the delayed and the non-delayed states and the input signal of the systems. The problem is solved, for the stationary case, for both nominal and uncertain polytopic systems, via the input-output approach by which the...
Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization [2, 3, 12, 13 and 17]. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EAbased optimization. The presence of noise interferes with the evaluation...
This paper proposes a compensated robust Kalman filter to reduce estimation error of a practical system. The practical system is assumed to have system uncertainty and affected by external disturbance and noise. Proposed Kalman filter has a compensation input, which reduces discrepancy between error dynamics of the reference system model and that of the practical system. The compensation term is calculated...
Continuous phase estimation is known to be superior in accuracy as compared to static estimation. The estimation process is, however, desired to be made robust to uncertainties in the underlying parameters. Here, homodyne phase estimation of coherent and squeezed states of light, evolving continuously under the influence of a second-order resonant noise process, are made robust to parameter uncertainties...
In this paper the command governor-based model reference control architecture is developed and analyzed for uncertain dynamical systems in the presence measurement noise and actuator dynamics. Specifically, the command governor is a dynamical system that adjusts the trajectory of a given command in order to enable an uncertain system to be able to follow an ideal reference system capturing a desired...
This work considers the problem of control of nonlinear systems subject to uncertainty. To this end, a Lyapunov-based robust model predictive controller (MPC) design is integrated with a moving horizon based offset-free mechanism to enable improved closed-loop performance while still retaining the guarantees provided by the Lypunov-based robust MPC. Simulation results are presented to illustrate the...
In this work, the problem of pole identification of discrete-time single-input single-output (SISO) linear time-invariant (LTI) systems directly from input-output data is considered. The solution to this nonlinear estimation problem is derived in form of the general Bayesian estimation framework, as well as a practical approximate solution by application of statistical linearization. The derived direct...
The main contribution of this paper is a novel planning algorithm that, starting from a probabilistic roadmap, efficiently constructs an expanded graph used to search for the optimal solution of a multi-objective problem. The primary cost is the shortest path from start to goal and the secondary cost is related to the state estimation error covariance. This needs to be optimized as we assume the navigation...
In this paper, a controller tuning methodology for unknown linear systems is proposed. The approach requires a set of experimental data generated by the plant for a single experiment. For a controller structure parametrized as a linear combination of basis functions, the procedure allows to find a feasible set of parameters compatible with a given performance criterion expressed as a desired complementary...
The paper deals with the state feedback quadratic mean square stabilization problem for multiple-input discrete-time networked control systems with quantization errors and multiplicative random noises. An analytic solvability condition is first derived for the single-input case in terms of the Mahler measure of the system and the effective worst signal-to-noise ratio (EWSNR) of the channel. Moreover,...
This paper is concerned with the verification of convexity for a class of stochastic control problems. In our previous work we proposed a hybrid method for solving the stochastic control problem with uncertainty in both the system and the constraint parameters. Under certain conditions, the optimization program is convex resulting in a drastic reduction in computational complexity over other methods...
This paper presents a novel sampling-based planner which guarantees robustness for linear systems subject to bounded process noise, localization error, and/or uncertain environmental constraints. The proposed algorithm extends RRT*, efficiently generating and optimizing robust, dynamically feasible trajectories. During planning, state constraints are individually tightened for robustness against future...
Successive interference cancellation (SIC) has been extensively applied to estimate transmit signals in communication systems. When the channel state information (CSI) and noise statistics are imperfectly estimated, the standard SIC estimators that ignore the model mismatch may perform poorly. This paper introduces regularized SIC estimation to provide robustness against the model mismatch. Suboptimal,...
In this paper, we consider the distributed consensus of high-dimensional first-order agents with relative-state-dependent measurement noises. Each agent can measure or receive its neighbors' state information with random noises, whose intensity is a nonlinear vector function of agents' relative states. For this kind of multi-agent networks, it is a prominent feature that the dynamics associated with...
This paper considers the problem of planning a path for an autonomous vehicle from a start to a goal location in presence of sensor intermittency modeled as a stochastic process, in addition to process and measurement noise. The aim is to plan a path that minimizes the localizational uncertainty for the vehicle upon arriving at the goal location. The main contribution of this paper is two-fold. We...
This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT∗-D. The proposed CC-RRT∗-D employs the chance-constraint approximation and leverages the asymptotically optimal property of RRT∗ framework to compute risk-aware and asymptotically optimal trajectories. By explicitly considering the state dependence...
This paper proposes a novel method to estimate relative poses for a calibrated stereo camera. Three corresponding points in 3D space are theoretically required to recover unconstraint motion which has six degrees of freedom. The proposed method solves this problem with only two 3D points by exploiting a common reference direction between poses. Two points are selected in accordance with the distance...
Underwater acoustic measurements are made to provide validation and qualification for a wide range of applications and developments in the off-shore industry, oceanography, defense, fisheries, and geophysics. However, measurements are only meaningful if they are performed in a technically sound manner and if they can be related to common standards of measurement. In this paper, a summary is given...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.