The problem of powered descent guidance and control for autonomous precision landing for next-generation planetary missions is addressed. The precision landing algorithm aims to trace a fuel-optimal trajectory while keeping geometrical constraints such as the line of sight to the target site. The design of an autonomous control algorithm managing such mission scenarios is challenging due to fact that critical geometrical constraints are coupled with the translational and rotational motions of the lander spacecraft, leading to a complex motion-planning problem. This problem is approached within the model predictive control framework by representing the dynamics of the rigid body in a uniform gravity field via a piecewise affine system taking advantage of the unit dual-quaternion parameterization. Such a parameterization in turn enables a six-degree-of-freedom motion planning in a unified framework while also admitting a quadratic cost on the required control commands to minimize propellant consumption. A n...

Constrained Autonomous Precision Landing via Dual Quaternions and Model Predictive Control

State recovery and disturbance estimation of unmanned surface vehicles based on nonlinear extended state observers

Design ideas and experiences gained during the development of an adaptive high precision track controller for ships are summarised. The controller is installed on a number of different ships, using a variety of position measurement systems. The design follows the spirit of the LQG paradigm combined with a model following feedforward strategy. The ship is kept to a manoeuvring trajectory through a combination of feedforward and LQG feedback control. The variances and weighting coefficients for the LQG controller are chosen systematically. The large number of design parameters is reduced by appropriate model scaling. In addition, a decomposition structure of the Kalman filter is exploited to reveal the important tuning parameters.

LQG approach for the high-precision track control of ships : Special section on marine control

Robust model predictive control for path-following of underactuated surface vessels with roll constraints

Two-time scale path following of underactuated marine surface vessels: Design and stability analysis using singular perturbation methods

A robust control method, based on a model predictive control, is developed for the trajectory tracking of a surface vessel with sensor failures. The kinematic model of a surface vessel is first obtained. The Lyapunov stability theory and linear matrix inequalities are then adopted to produce a closed-loop system that can have a fault-tolerant capability against sensor failures and to subsequently obtain the simulated results of a fault-tolerant controller through solving the linear matrix inequalities. The asymptotic tracking position, heading angle, and the velocities of the surface vessel can be obtained using the inputs of incomplete state information in the simulation. The feasibility and effectiveness of the proposed control method have been verified through simulation results.

Robust MPC-Based Fault-Tolerant Control for Trajectory Tracking of Surface Vessel

As one of the most important density-based clustering algorithms, DBSCAN has been widely used in many application fields due to its simplicity and good clustering effect. However, the original DBSCAN method exists two main problems, which include difficulty selecting the suitable external parameters and time consuming. In this paper, an improved version of the DBSCAN, namely DG-DBSCAN (dual grid-based DBSCAN) clustering algorithm, has been proposed. It adopts two types of grid (i.e. inner grid and outer gird) to solve the aforementioned problems respectively. Experimental results show that the new algorithm can not only determine the appropriate external input parameters, but also greatly reduce the running time. Therefore, the proposed DG-DBSCAN is successfully implemented as a novel clustering method.

Revised DBSCAN Clustering Algorithm Based on Dual Grid

An experimental study of the vertical stabilization control of a trimaran using an actively controlled T-foil and flap

A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained inputs. Aimed at the special structure of USV, a state-dependent coefficient (SDC) under the given USV is constructed in terms of diffeomorphism and state-dependent Riccati equation (SDRE) theory. Based on linear matrix inequalities (LMIs), the states of the USV are steered into an operating region around zero. When the states reach the region, the control law is switched to stabilize the system. And the constrained control input of the considered system is solved by convex optimization based on MPC involving LMIs. The simulation results verified the effectiveness of the proposed method. It is shown that, based on LMIs, it is easy to get the MPC for the USV with input constraints.

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Zhilin Liu

Papers

Robust MPC-Based Fault-Tolerant Control for Trajectory Tracking of Surface Vessel

Revised DBSCAN Clustering Algorithm Based on Dual Grid

An experimental study of the vertical stabilization control of a trimaran using an actively controlled T-foil and flap

LMI-Based Model Predictive Control for Underactuated Surface Vessels with Input Constraints

Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel