Low-Load endoluminal navigation with a magnetically actuated medical soft microrobot

Error-constrained reliable tracking control for discrete time-varying systems subject to quantization effects

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

PurposeThis study aims to design a controller which can improve the end-effector low-frequency chattering resulting from the measurement noise and the time delay in the on-orbit tasks. The rendezvous point will move along the rendezvous ring owing to the error of the camera, and the manipulators’ collision need be avoided. In addition, owing to the dynamics coupling, the manipulators’ motion will disturb the spacecraft, and the low tracking accuracy of the end-effector needs to be improved.Design/methodology/approachThis paper proposes a minimum disturbance controller based on the synchronous and adaptive acceleration planning to improve the tracking error and the disturbance energy. The synchronous and adaptive acceleration planning method plans the optimal rendezvous point and designs synchronous approaching method and provides an estimation method of the rendezvous point acceleration. A minimum disturbance controller is designed based on the energy conservation to optimize the disturbance resulting from the manipulator’s motion.FindingsThe acceleration planning method avoids the collision of two end-effectors and reduces the error caused by the low-frequency chattering. The minimum disturbance controller minimizes the disturbance energy of the manipulators’ motion transferred to the spacecraft. Experiment results show that the proposed method improves the low-frequency chattering, and the average position tracking error reduces by 30%, and disturbance energy reduces by 30% at least. In addition, it has good performances in the synchronous motion and adaptive tracking.Originality/valueGiven the immeasurability of the target satellite acceleration in space, this paper proposes an estimation method of the acceleration. This paper proposes a synchronous and adaptive acceleration planning method. In addition, the rendezvous points are optimized to avoid the two end-effectors collisions. By the energy conservation, the minimum disturbance controller is designed to ensure a satisfying tracking error and reduce the disturbance energy resulting from the manipulators’ motion.

Risk budgeting is a process by which investors make asset allocation decisions subject to a portfolio-to-benchmark tracking error constraint. We use simulations of multi-asset class portfolio returns to compare risk budgeting outcomes in two contexts: a world in which the parameters of the distribution of asset returns are known (parameter certainty) and one in which those parameter values vary from evaluation period to evaluation period (parameter uncertainty). Even under parameter certainty, risk budgets are different from resource budgets in that high tracking error (active return volatility) does not imply a violation of the risk budget. Under the level of parameter uncertainty (variation in the parameters of asset return distributions) observed during the 1976–2004 period, tracking error volatility across simulations is approximately twice as large as is observed under risk certainty. Our results suggest that risk budgeting does not provide an appropriate analytical framework for measuring the realized performance of portfolios or portfolio managers relative to their benchmarks <b>TOPICS:</b>Portfolio theory, portfolio construction, equity portfolio management

An integrated direct/indirect adaptive robust controller (DIARC) is proposed to further improve the achievable posture trajectory tracking control performance of a parallel manipulator driven by pneumatic muscles. Due to the model errors of the static forces and friction forces of pneumatic muscles, the simplified average flow rate characteristics of valves, and the unknown disturbances of entire system, there exist large extent of parametric uncertainties and rather severe uncertain nonlinearities in the modeling of the parallel manipulator. To address these problems, in this paper, an indirect type parameter estimation is used to obtain reliable estimates of effective model parameters for reducing the parametric uncertainties while an integrated direct/indirect ARC with dynamic compensation type fast adaptation is utilized to further attenuate the influences of uncertain nonlinearities for better tracking performance. Considering that the conventional parameter estimation algorithm based on single error minimizing criterion normally fails to provide reliable parameter estimation for the parallel manipulator with symmetric structure due to the difficulty in satisfying the persistent exciting conditions all the time-the theoretical requirement for the convergence of online parameter estimation, additional practical constraints are imposed to further condition the parameter estimation process and a new parameter estimation algorithm based on composite error minimizing criterion in task-space is developed. Experimental results demonstrate that the parallel manipulator under the control of the proposed integrated DIARC has strong self-adaptability and robustness with the steady-state posture tracking error being less than 0.01deg, average tracking error less than 0.1deg, and maximum tracking error less than 0.3deg, which are significantly better than those of the direct ARC.

This study concerns the robust tracking control problem for an underactuated surface vessel with parameter uncertainties. In recent work by McNinch and collegeaues, a sliding mode control (SMC) law is presented and experimentally implemented for trajectory tracking of an underactuated autonomous surface vessel. The authors believe, however, their theory formulation involves essential flaws. Motivated by the obtained result by McNinch and collegeaues, a new revised SMC law is proposed under the same assumptions. The control law is similarly developed by introducing a first-order sliding surface in terms of surge tracking errors and a second-order surface in terms of lateral motion tracking errors; however, the desired surge and sway velocities are presented as a new form to guarantee the convergence of position tracking errors. Simulation results are provided to validate their method.

Trajectory tracking control of ROVs considering external disturbances and measurement noises using ESKF-based MPC

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

This paper develops a discrete constrained controller for electrohydraulic system (EHS) controlled by proportional directional control (PDC) valve while taking parametric uncertainties into account. The proposed controller solves the discrete barrier Lyapunov function via convex optimization while taking into consideration the system constraints such as state and actuator constraints. The proposed control law successfully tracks the reference trajectory while satisfying the state and actuator constraints. It is proven that the tracking error always converges to an arbitrary small neighborhood which is validated using Lyapunov stability analysis. The effectiveness of the proposed discrete barrier Lyapunov function based constrained controller was verified in a simulation environment and also through experimentation. From the simulation and experimental results, it is clear that the proposed controller drives the load to accurately track the reference trajectory in the presence of uncertainty and unknown disturbances.

This paper concerns the finite-time trajectory tracking control problem for an underactuated marine surface vessel (MSV) suffering from the external disturbance and parameter uncertainties. First, the virtual velocity command is proposed based on a novel piecewise function. It will be illustrated that the position tracking error can be stabilized to small regions in finite time once the desired velocity commands are tracked. Then, an adaptive tracking controller is developed such that sway and yaw velocities can converge to the desired ones in finite time. Utilizing the proposed control strategy, global finite-time stability can be ensured for the position and velocity tracking errors even in the presence of external disturbances and parameter uncertainties. Finally, the effectiveness of the proposed controller is illustrated by numerical simulation.

This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy.

Robust tracking of the heating value in an underground coal gasification process using dynamic integral sliding mode control and a gain-scheduled modified Utkin observer

Task-space cooperative tracking control of the networked multiple Euler–Lagrange systems is studied in this paper. On the basis of establishing kinematic and dynamic modeling of a Euler–Lagrange system, an innovative task-space coordination controller is designed to deal with the time-varying communicating delays and uncertainties. First, in order to weaken the influence of the uncertainty of kinematic and dynamic parameters on the control error of the system, the product of the Jacobian matrix and the generalized spatial velocity are linearly parameterized; thus, the unknown parameters are separated from known parameters. The online estimation of uncertain parameters is realized by designing parameters and by proposing new adaptive laws for the dynamic and kinematic parameters. Furthermore, to describe the transmission of time-varying delay errors among networked agents, a new error term is introduced, obtained by adding the observation error and tracking error, and the coefficient of the network mutual coupling term related to the time-varying delay rate is added with reference to the generalized space velocity and task-space velocity of the Lagrange systems. In the end, the influence of the time-varying delay on the cooperative tracking control error of the networked multiple Euler–Lagrange systems is eliminated. With the help of Lyapunov stability theory, the tracking errors and synchronization errors of this system are calculated by introducing the Lyapunov–Krasovskii functional; the asymptotic convergence results rigorously prove the stability of the adaptive cooperative control systems. The simulation results verify the excellent performance of the controller.

SummaryA new robust adaptive iterative learning control approach is proposed for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties. By virtue of a well‐designed dead‐zone function, the learning of the parametric and nonparametric uncertainties can be performed concurrently. Rigorous Lyapunov function‐based analysis ensures that the effect of system uncertainties can be fully compensated, and the tracking error will converge to zero asymptotically in the iteration domain, even under random initial conditions and iteration‐varying reference trajectories. The efficacy of the proposed controller is demonstrated by simulating a single‐link robot manipulator with unknown frictions. Copyright © 2015 John Wiley &amp; Sons, Ltd.

In this brief, a novel direct adaptive fuzzy tracking control (DAFTC) scheme for marine vehicles with fully unknown parametric dynamics and uncertainties is proposed. The significant contributions of the DAFTC approach are as follows. First, in the backstepping framework, fully unknown parametric dynamics and uncertainties are encapsulated into a lumped nonlinearity function encompassing system states and virtual control signals. Second, the integrated nonlinearity function is further identified online by an adaptive fuzzy approximator that synthesizes a model-free control scheme (termed DAFTC) without requiring any a priori knowledge of the model. Third, tracking errors are proven to be uniformly ultimately bounded (UUB) and can converge to an arbitrarily small neighborhood of zero in a finite time. Simulation studies and comprehensive comparisons demonstrate that the proposed DAFTC scheme has remarkable performance and is superior in both tracking accuracy and unknown parametric dynamics compensation.