A Nonlinear Optimal Control Approach for the Vertical Take-off and Landing Aircraft

The paper proposes a nonlinear optimal control approach for the model of the vertical take-off and landing (VTOL) aircraft. This aerial drone receives as control input a directed thrust, as well as forces acting on its wing tips. The latter forces are not perpendicular to the body axis of the drone but are tilted by a small angle. The dynamic model of the VTOL undergoes approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the VTOL aircraft. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the aerial drone, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains, an algebraic Riccati equation is solved at each time-step of the control method. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the VTOL aircraft, under moderate variations of the control inputs. The stability properties of the control scheme are proven through Lyapunov analysis.

Interlaced Attitude Estimation for Spacecraft Using Vector Observations

This paper proposes an interlaced attitude estimation method for spacecraft using vector observations, which can simultaneously estimate the constant attitude at the very start and the attitude of the body frame relative to its initial state. The arbitrary initial attitude, described by constant attitude at the very start, is determined using quaternion estimator which requires no prior information. The multiplicative extended Kalman filter (EKF) is competent for estimating the attitude of the body frame relative to its initial state since the initial value of this attitude is exactly known. The simulation results show that the proposed algorithms could achieve better performance compared with the state-of-the-art algorithms even with extreme large initial errors. Meanwhile, the computational burden is also much less than that of the advanced nonlinear attitude estimators.

Topological Similarity-Based Multi-Target Correlation Localization for Aerial-Ground Systems

In this paper, an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated. Aiming at the multi-target correlation problem, the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives. First, the visual axis was preprocessed by the threshold method, so that the sparse targets were initially associated. Then, the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets. The shortcoming of dense target similarity with small difference was optimized by the improved topological similarity method. For the problem of co-location, combined with the multi-target correlation algorithm in this paper, the triangulation positioning model was used to complete the co-location of multiple targets. In the experimental part, simulation experiments and flight experiments were designed to verify the effectiveness of the algorithm. Experimental results show that the proposed algorithm can effectively achieve multi-target correlation positioning, and that the positioning accuracy is obviously better than other positioning methods.

Autonomous Reconfiguration Strategy Based on Reconfigurability Evaluation

This paper aims to improve the autonomous operational capabilities of spacecraft. For a complex system with physical coupling and flexible attachments, an autonomous reconfiguration strategy based on reconfigurability evaluation is proposed. First, a quantitative evaluation index for reconfigurability is given based on operator theory to fully exploit the potential of system reconfigurability under resource constraints. Then, specific requirements and evaluation methods for autonomous reconfiguration are proposed in accordance with the results, thereby providing a theoretical reference for the design of reconfiguration strategy methods. Thus, reconfigurability is maximized from the design point of view. Finally, the validity and correctness of the method are verified by simulation.

Distributed Formation Control of UAVs for Circumnavigating a Moving Target in Three-Dimensional Space

In this paper, a novel formation control strategy is proposed to address the target tracking and circumnavigating problem of multi-UAV formation. First, two sets of definitions, space angle definition and space vector definition, are presented in order to describe the flight state and construct the desired relative velocity. Then, the relative kinematic model between the UAV and the moving target is established. The distributed control law is constructed by using dynamic feedback linearization so as to realize the tracking and circumnavigating control with the desired velocity, circling radius and relative angular spacing. Next, the exponential stability of the closed-loop system is further guaranteed by properly choosing some corresponding parameters based on the Lyapunov method. Finally, the numerical simulation is carried out to verify the effectiveness of the proposed control method.

Sliding Mode Control-Based Autonomous Control of a Tri-rotor Unmanned Aerial Vehicle

A nonlinear control technique for autonomous control of a tri-rotor unmanned aerial vehicle is presented in this paper. First, a comprehensive mathematical model is developed using the Newton–Euler approach for a tri-rotor, which is found to be highly nonlinear and coupled. Then, the equivalent input affine model is extracted by applying a suitable transformation. Finally, the sliding mode control for trajectory tracking is chosen which is immune to matched external disturbances, parametric uncertainties, and modeling errors. The proposed controller performance has been verified for appropriate inputs under wind disturbances using MATLAB, and the simulation results are presented.

Graphical Minimax Game and Off-Policy Reinforcement Learning for Heterogeneous MASs with Spanning Tree Condition

In this paper, the optimal consensus control problem is investigated for heterogeneous linear multi-agent systems (MASs) with spanning tree condition based on game theory and reinforcement learning. First, the graphical minimax game algebraic Riccati equation (ARE) is derived by converting the consensus problem into a zero-sum game problem between each agent and its neighbors. The asymptotic stability and minimax validation of the closed-loop systems are proved theoretically. Then, a data-driven off-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without the information of the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation.

System Characterization and Adaptive Tracking Control of Quadrotors under Multiple Operating Conditions

This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.

Symbolic Control of Hybrid Systems from Signal Temporal Logic Specifications

In safety-critical systems, it is desirable to automatically synthesize controllers for complex tasks with guaranteed safety and correctness. Although much progress has been made through controller synthesis from temporal logic specifications, existing approaches generally require conservative assumptions and do not scale well with system dimensionality. We propose a scalable, provably complete algorithm that synthesizes continuous trajectories for hybrid systems to satisfy temporal logic specifications. Specifically, we harness highly efficient Boolean satisfiability (SAT) and Linear Programming (LP) solvers to find trajectories that satisfy non-convex Signal Temporal Logic (STL) specifications for a class of high dimensional hybrid systems. The proposed design algorithms are proven sound and complete, and are validated in simulation experiments.

Ascent Trajectory Optimization for Air-Breathing Hypersonic Vehicles Based on IGS-MPSP

This paper proposes an improved Generalized Quasi-Spectral Model Predictive Static Programming (GS-MPSP) algorithm for the ascent trajectory optimization for hypersonic vehicles in a complex flight environment. The proposed method guarantees the satisfaction of constraints related to the state and control vector while retaining its high computational efficiency. The spectral representation technique is used to describe the control variables, which reduces the number of decision variables and makes the control input smooth enough. Through Taylor expansion, the constraints are transformed into an inequality containing only decision variables, such that it can be added into GS-MPSP framework. By Gauss quadrature collocation method, only a few collocation points are needed to solve the sensitivity matrix, which greatly accelerates the calculation. Subsequently, the analytical expression is obtained by combining the static optimization with the penalty function method. Finally, the simulation results demonstrate that the proposed improved GS-MPSP algorithm can achieve both high computational efficiency and high terminal precision under the constraints.