Adaptive Dynamic Programming for Flight Control

Adaptive dynamic programming (ADP) is a sub-field of approximate dynamic programming that deals with the adaptive control of continuous nonlinear dynamic systems. Its origins stem from dynamic programming in optimal control, but it is extended into a form where approximations are used to reduce the curse of dimensionality and reduce the need...

Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on an explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in the proposed method...

Event-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic system

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic...

Vision-Based Nonlinear Incremental Control for A Morphing Wing with Mechanical Imperfections

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the...

Intelligent Adaptive Control Using LADP and IADP Applied to F-16 Aircraft with Imperfect Measurements

Linear Approximate Dynamic Programming (LADP) and Incremental Approximate Dynamic Programming (IADP) are Reinforcement Learning methods that seek to contribute to the field of Adaptive Flight Control. This paper assesses their performance and convergence, as well as the impact of sensor noise on policy convergence, online system...

Reinforcement-learning-based adaptive optimal flight control with output feedback and input constraints

Intelligent adaptive optimal control using incremental model-based global dual heuristic programming subject to partial observability

The scarcity of information regarding dynamics and full-state feedback increases the demand for a model-free control technique that can cope with partial observability. To deal with the absence of prior knowledge of system dynamics and perfect measurements, this paper develops a novel intelligent control scheme by combining global dual...

Incremental model-based global dual heuristic programming with explicit analytical calculations applied to flight control

A novel adaptive dynamic programming method, called incremental model-based global dual heuristic programming, is proposed to generate a self-learning adaptive flight controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online local dynamics identification, instead of the...

Incremental model-based heuristic dynamic programming with output feedback applied to aerospace system identification and control

Sufficient information about system dynamics and inner states is often unavailable to aerospace system controllers, which requires model-free and output feedback control techniques, respectively. This paper presents a novel self-learning control algorithm to deal with these two problems by combining the advantages of heuristic dynamic...

Launch vehicle discrete-time optimal tracking control using global dual heuristic programming

Optimal tracking is a widely researched control problem, but the unavailability of sufficient information referring to system dynamics brings challenges. In this paper, an optimal tracking control method is proposed for an unknown launch vehicle based on the global dual heuristic programming technique. The nonlinear system dynamics is...

Incremental Model-Based Global Dual Heuristic Programming for Flight Control

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic Programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial...

Quadrotor Fault Tolerant Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers

This paper proposes an Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers (INDI-SMC/SMDO), with application to a quadrotor fault tolerant control problem. By designing the SMC/SMDO based on the control structure of the sensor-based Incremental Nonlinear Dynamic Inversion (INDI), instead of the model-based Nonlinear...

Multivariate spline-based adaptive control of high-performance aircraft with aerodynamic uncertainties

In this paper, a new modular adaptive control system is presented to compensate for aerodynamic uncertainties in high-performance flight control systems. This approach combines nonlinear dynamic inversion with multivariate spline-based adaptive control allocation. A new real-time identification routine for multivariate splines is presented to...