Large Initial Errors Research Articles

Newton-ILC Contouring Error Estimation and Coordinated Motion Control for Precision Multiaxis Systems With Comparative Experiments

This paper proposes a Newton extremum seeking algorithm based iterative learning coordinated control (Newton-ILC) strategy for contouring motion accuracy of precision multiaxial systems. Specifically, as the contouring error estimation is critically important for coordinated contouring motion control, a cost function is constructed based on the reference contour, as well as the current position, and the minimal value of the function is searched to determine the contouring error point through an offline Newton algorithm. Consequently, high precision estimation of the contouring error can be achieved, even under extreme contouring tasks with high speed, large curvature, and sharp corners. The calculated contouring error is then projected to each axis, and the axial contouring errors are controlled by iterative learning method, while the learning results will be used to adjust the axial position reference commands for contouring accuracy improvement. Comparative experiments are conducted on a biaxial linear motor stage to validate the practical effectiveness of the proposed Newton-ILC strategy. The experimental results illustrate that the proposed Newton-ILC achieves not only nearly perfect contouring error estimation but obvious improvement of contouring accuracy as well after a few iterations. Particularly, in some extreme cases such as large initial tracking error, high speed, large curvature, and sharp corners, the proposed Newton-ILC strategy can achieve rather excellent contouring performance when compared with individual axis control, conditional cross-coupled control, and cross-coupled iterative learning coordinated control (CCILC) methods.

An approach and landing guidance design for reusable launch vehicle based on adaptive predictor–corrector technique

In this study, a novel predictor–corrector guidance law based on the all-coefficient adaptive control theory is proposed for the approach and landing phase of an unpowered reusable launch vehicle (RLV). The flight phase includes two portions: the initial gliding flight phase and the final exponential flare one. The equilibrium glide condition and an exponential function of altitude are used to parameterize the guidance commands of two portions and generate the guidance sequence. Based on the first order characteristic model which has the advantages of less characteristic parameters and easy analysis, the all-coefficient adaptive predictor–corrector guidance method is presented. The guidance law has the ability of generating new trajectories online according to the current states and the final conditions of the landing point. Then, the stability and finite-time convergence of the guidance law are analyzed. Considering the process constraints, the fusion guidance law is obtained. Finally, simulation results demonstrate the effectiveness and robustness of the proposed guidance law, with respect to the large initial states errors and parameter uncertainties. The simulations also show that the system under the proposed guidance law converges to a small neighborhood of zero after limited steps.

Fast Slew Maneuvers for the High-Torque-Wheels BIROS Satellite

The satellite platform BIROS (Bi-spectral InfraRed Optical System) is the second technology demonstrator of the DLR R&D `FireBIRD' space mission aiming to provide infrared remote sensing for early fire detection. Among several mission goals and scientific experiments, to demonstrate a high-agility attitude control system, the platform is actuated with an extra array of three orthogonal `High-Torque-Wheels' (HTW). For agile reorientation, however, a challenge arises from the fact that time-optimal slew maneuvers are in general not of the Euler-axis rotation type, specially whenever the actuators are constrained independently. Moreover, BIROS' On-Board-Computer (OBC) can only accommodate rotational acceleration commands twice per second. Our objective is therefore to find a methodology to design fast slew maneuvers while considering a highly dynamic plant commanded by piecewise-constant sampled-time control inputs. We do this by considering a comprehensive analytical nonlinear model for spacecraft equipped with reaction wheels and transcribing a time-optimal control problem formulation into a multi-criteria optimization problem which is then solved with a direct approach in a sequential procedure using the trajectory optimization package `trajOpt' of DLR-SR's optimization tool MOPS `Multi-Objective Parameter Synthesis'. Our approach for efficient design of rest-to-rest fast slew maneuvers considers an attitude error whose magnitude is proportional to Euler-axis rotations between current and desired attitudes even for large initial attitude errors. Results based on numerical simulations are presented to illustrate our method.

Multipath Parallel ICCP Underwater Terrain Matching Algorithm Based on Multibeam Bathymetric Data

Using the underwater terrain for assisted navigation is a popular research direction for autonomous underwater vehicle navigation technology. The iterated closest contour point (ICCP) algorithm is one of the classic matching algorithms in underwater terrain assisted navigation. However, the traditional ICCP algorithm is not suitable for the case of large initial error of inertial navigation system (INS) because it easily leads to a total match failure. This failure brings a great challenge to an underwater terrain matching system. In this paper, the improved ICCP underwater terrain matching algorithm, a multipath parallel ICCP algorithm, is proposed. The improved algorithm is based on multibeam bathymetric data acquired by the multibeam echosounder that is one of the main instruments of water depth measurement. The data points at the center and both sides of the edge of the swath sounding data are chosen to comprise three paths that are fully utilized by the improved algorithm. Computer simulation is used to evaluate the proposed algorithm in two terrain differences areas. Simulation results verify the effectiveness of multipath parallel ICCP algorithm. The proposed algorithm can effectively solve the problem of matching divergence caused by large initial errors of INS and obtain higher location accuracy.

Velocity-Free Attitude Stabilization of a Nadir-Pointing Underactuated Rigid Spacecraft

This paper examines the velocity-free attitude stabilization of an underactuated nadir-pointing rigid spacecraft in a circular orbit in the presence of time-varying disturbances. A nonlinear observer is used to estimate the angular velocity of the spacecraft to complete an output feedback control law. A sliding-mode control strategy is proposed for this stabilization problem in conjunction with a nonlinear observer. The local ultimate boundedness of the closed-loop system is rigorously proven. Numerical examples demonstrate the performance of the controller in the presence of time-varying disturbances, nonzero orbital eccentricity, inertia matrix uncertainties, and measurement noise in a sampled data implementation. In particular, even though the theoretical ultimate boundedness guarantees are local, the numerical examples demonstrate ultimate boundedness with large initial attitude errors.

Computational geometric system identification for the attitude dynamics on SO(3)

A computational approach for system identification for the attitude dynamics of a rigid body is proposed. The estimation and system identification for the rigid body are particularly challenging as they evolve on the compact nonlinear manifold, referred to as the special orthogonal group. Current methods based on local parameterizations or quaternions suffer from inherent singularities or ambiguities associated with them. The proposed method addresses these issues by formulating the system identification problem as an optimization problem on the special orthogonal group. It is solved by a geometric numerical integrator that yields numerical trajectories that are consistent with the geometric structures of Hamiltonian systems on a Lie group. As a result, the proposed method is particularly useful to handle large initial estimation errors that may cause substantial discrepancies between the target attitude trajectories and the initial estimate of them. These are illustrated by numerical examples.

Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS.

The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H filter (SIHF) for improving both the accuracy and robustness of RTA. In this new nonlinear H filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H filter for the first time, and the resulting SIHF simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIHF and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H filter. Moreover, the SIHF inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.

A Spatial Approach to Control of Platooning Vehicles: Separating Path-Following from Tracking

We introduce a new way to look at the combined lateral and longitudinal control problem for platooning vehicles by studying these problems separately. The lateral control problem is approached as a path following problem in the spatial domain: based on the path of the preceding vehicle we determine a path for the following vehicle which converges to the given path of its predecessor. In particular if the following vehicle happens to be on the path of its predecessor, the generated path of the follower equals the path of its predecessor. This approach not only overcomes the problem of corner cutting, but also achieves appropriate following behavior in case of large initial errors. As a by-product of solving the lateral control problem, we obtain a mapping from the path of the follower to the path of its predecessor. Using this mapping we can consider the longitudinal control problem as controlling two points on the same path towards a required inter-vehicle distance, which is comparable to CACC, i.e., the problem of controlling two points on a straight line towards a required inter-vehicle distance. We illustrate our approach by means of simulation.

State-estimation for a class of tubular reactors using a pointwise innovation scheme

The problem of estimating the spatio-temporal temperature and concentration distribution in a tubular reactor with first order exothermic reaction and in-domain pointwise temperature measurements is considered. The problem is addressed using a simple pointwise innovation (PWI) structure, which imposes the measurement information at the sensor location in the simulator. Rigorous convergence conditions are presented in terms of the sensor location and reactor parameters. The approach is tested in numerical simulation studies and put in perspective with the (continuous time) extended Kalman filter (EKF) for a benchmark reactor with up to five steady-state profiles. In the simulations the proposed PWI scheme shows a good convergence behavior even in the presence of large initial errors, unmeasured feed perturbations, measurement noise and parameter mismatch.

Approach and landing guidance design for reusable launch vehicle using multiple sliding surfaces technique

An autonomous approach and landing (A&L) guidance law is presented in this paper for landing an unpowered reusable launch vehicle (RLV) at the designated runway touchdown. Considering the full nonlinear point-mass dynamics, a guidance scheme is developed in three-dimensional space. In order to guarantee a successful A&L movement, the multiple sliding surfaces guidance (MSSG) technique is applied to derive the closed-loop guidance law, which stems from higher order sliding mode control theory and has advantage in the finite time reaching property. The global stability of the proposed guidance approach is proved by the Lyapunov-based method. The designed guidance law can generate new trajectories on-line without any specific requirement on off-line analysis except for the information on the boundary conditions of the A&L phase and instantaneous states of the RLV. Therefore, the designed guidance law is flexible enough to target different touchdown points on the runway and is capable of dealing with large initial condition errors resulted from the previous flight phase. Finally, simulation results show the effectiveness of the proposed guidance law in different scenarios.

Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

This paper addresses a vision-based terrain referenced navigation of an aircraft. A digital terrain map, in the surroundings of the aircraft, is compared with the camera measurements to estimate the aircraft position. Generally, the measurement equation in the terrain referenced navigation is highly nonlinear due to the sharp changes of terrain. Thus, the conventional extended Kalman filter could lead to unstable navigation solutions. In this paper, a new approach using an adaptive extended Kalman filter is proposed to cope up with the nonlinearity problem. A least squares method is utilized to derive the linearized measurement equations. The Jacobian matrix and sensor noise covariance are modified as a means of smoothing the sharp changes of terrain. Monte Carlo simulations verify that the proposed filter gives the stable navigation solutions, even when there is a large initial error, which is the primary reason for the filter divergence. Moreover, the proposed adaptation barely requires additional computational burden, whereas the high-order filters such as particle filter generally needs higher computational power.

Time-varying sliding mode guidance scheme for maneuvering target interception with impact angle constraint

In this paper, a guidance scheme for impact angle control against maneuvering targets with unknown target acceleration is proposed. In this scheme, the unknown target acceleration is estimated via a linear extended state observer; a novel time-varying global slide mode control technique is presented to eliminate the reaching phase and enforce a desired impact angle exactly at the time of interception with finite-time convergence, good robustness, high precision and smooth guidance command. Moreover, feasible guidance logics are developed to achieve all-aspect interception with the tolerance of large initial heading errors. Numerical simulations in various scenarios are performed to verify the performance of the proposed guidance scheme.

Adaptive nonlinear gain based composite nonlinear feedback controller with input saturation

This article proposes for the first time a novel adaptive nonlinear gain based composite feedback controller. In the proposed method an adaptive nonlinear gain is introduced in the composite feedback controller to improve the system dynamic response. Using the Lyapunov analysis and the adaptive law for the nonlinear gain, it is shown that all the closed loop signals of the system are bounded. Finally, we use a double integrator single-input single-output system and twin rotor multi-input multi-output (MIMO) system to demonstrate the application of the proposed scheme. In both the applications, the improvement in settling time and root mean square error is scalable by the adaptive nonlinear gain based composite feedback control. Further the controller is successfully tested for a set of desired set points for MIMO system example which will create large initial errors.

Autolanding a Power-off UAV Using On-line Optimization and Slip Maneuvers

This paper tackles the final stages of autolanding a fixed-wing Unmanned Air Vehicle (UAV) in a power-fail emergency scenario. We applied the principle of Nonlinear Model Predictive Control (NMPC) to plan trajectories that respect the aircraft's limits, and react on disturbances and environmental changes in real-time. However, thanks to judicious problem formulations, our algorithm optimizes--in each time step--the whole horizon of the landing trajectory, and settles a dynamic compromise between the different touchdown requirements. Furthermore, the algorithm is powered by a novel method that utilizes slip maneuvers to achieve adequate energy control notwithstanding the absence of specialized drag control devices. Thus, precise touchdown point is attained even in the presence of strong wind shear and large initial errors. All design parameters are optimized off-line over a wide spectrum of conditions using Genetic Algorithm (GA). Monte-Carlo simulation of the optimized design shows excellent landing performance and high success rate on both flat and sloping terrains.

Multi-cameras visual servoing for dual-arm coordinated manipulation

SUMMARYAlthough image-based visual servoing (IBVS) provides good performance in many dual-arm manipulation applications, it reveals some fatal limitations when dealing with a large position and orientation uncertainty. The object features may leave the camera's field of view, and the dual-arm robot may not converge to their goal configurations. In this paper, a novel vision-based control strategy is presented to resolve these limitations. A visual path planning method for dual-arm end-effector features is proposed to regulate the large initial poses to the pre-alignment poses. Then, the visual constraints between the position and orientation of two objects are established, and the sequenced subtasks are performed to attain the pose alignment of two objects by using a multi-tasks IBVS method. The proposed strategy has been implemented on a MOTOMAN robot to perform the alignment tasks of plug–socket and cup–lid, and results indicate that the plug and socket with the large initial pose errors 145.4 mm, 43.8○ (the average errors of three axes) are successfully aligned with the allowed pose alignment errors 3.1 mm, 1.1○, and the cup and lid with the large initial pose errors 131.7 mm, 20.4○ are aligned with the allowed pose alignment errors −2.7 mm, −0.8○.

Time varying backstepping control for trajectory tracking of mobile robot

This paper presents a time varying backstepping tracking controller TVBTC for the kinematic model of the non-holonomic wheeled mobile robot WMR that can improves the transient performances of the standard backstepping control scheme. The proposed TVBTC is derived based on the analysis of the error dynamic, as well as the stability conditions of the controlled system. The asymptotic stability of the system and the convergence of the posture errors to zero are guaranteed using the Lyapunov stability theory. In comparison with the other tracking control methods for the mobile robot, the simulation results demonstrate that the transient performance can be improved significantly using the proposed TVBTC, which is capable to track a circular path with faster settling time and minimal overshoot. In addition, the TVBTC can efficiently handle a situation with arbitrarily large initial tracking errors and it is capable to produce smooth and bounded velocity output in a finite time interval. The simulation results show the effectiveness of the proposed tracking controller at the starting time.

An autonomous underwater vehicle positioning matching method based on iterative closest contour point algorithm and affine transformation

An improved autonomous underwater vehicle federal integrated navigation method based on two-stage position matching technology is proposed in this study to solve the problem of positioning divergence caused by the defects of error accumulation in an inertial navigation system. The improved two-stage position matching method includes the acquisition and preprocessing of the strap-down inertial navigation system and indicates track sequences and elevation measurement sequences. The first-stage position matching is based on iterative closest contour point, while the second-stage position matching is based on an affine algorithm. Simulation results indicate that the proposed algorithm solves the problem of the limitation of the rigid transformation of traditional iterative closest contour point and effectively resolves the matching failure caused by the large initial position error of long-time navigation.

Neural network based adaptive fuzzy PID-type sliding mode attitude control for a reentry vehicle

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

In-flight initial alignment for small UAV MEMS-based navigation via adaptive unscented Kalman filtering approach

In this paper, an adaptive unscented Kalman filter for UAV's MEMS-based navigation is derived to realize in-flight initial alignment aided by GNSS (global navigation satellite system) and fulfill data fusion. In the filter, unscented transformation is used to handle strong INS (Inertial Navigation System) model nonlinearity under large misalignment condition due to large and sudden maneuvers, and the technique of optimal adaptive factor is used to resist the influence of noise uncertainty of MIMU (MEMS-based Inertial Measurement Unit) and kinematic model errors. The flight test results indicate the proposed alignment algorithm can complete the initial alignment more quickly and accurately compared with the conventional EKF/UKF-based in-motion alignment approaches, especially when the initial attitude errors are large. As a unified in-flight alignment, it can guarantee the accurate and reliable alignment in situations of either large or small initial attitude errors without model changes for small UAV applications.

A Tandem Active Disturbance Rejection Control for a Laboratory Helicopter With Variable-Speed Rotors

This paper introduces a laboratory helicopter with a variable-speed rotor system based on decentralized active disturbance rejection control. The new control scheme is composed of two independent stages that are utilized to achieve the precise trajectory tracking of the generalized coordinates of the system. A simplified model is proposed at each stage, which highly simplifies the controller design task. High-gain generalized proportional integral observers are considered in order to locally estimate all the disturbances affecting each subsystem online. These estimated disturbances are used in the control law of each stage to eliminate the effects of these disturbances on the system performance. Some of the advantages of the proposed control are: 1) it only requires the knowledge of the input gain to the subsystems (minimum information required from the dynamical models to control them); 2) the robust controller design procedure is simplified; 3) easier and safer implementation with regard to standard controllers; and 4) high robustness to large initial errors, unmodeled unmatched perturbations, noisy measurements, and parametric uncertainties in the model. The effectiveness of the proposed approach has been verified with real experiments conducted on a laboratory platform.