Linear quadratic optimal model-following control of a helicopter in hover

Pieper, J.K.; Baillie, S.W.; Goheen, K. R.

doi:10.1002/(sici)1099-1514(199604/06)17:2<123::aid-oca568>3.0.co;2-3

Cited by 15 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the model in (7), the control objective of this paper is to derive an inverse optimal controller based on the disturbance observer to stabilize the system and to compensate for the unknown disturbance D. To design this composite control law, the related lemmas and assumptions are given as follows:…”

Section: Problem Statement and Modelingmentioning

confidence: 99%

“…For system (7), under the result of Lemma 2, the following nonlinear disturbance observer is used to estimate the unknown disturbances D, which is given by…”

Section: Design Of Disturbance Observermentioning

confidence: 99%

“…To apply the result of Lemma 1, a control Lyapunov function should be constructed for system (7). The backstepping method is adopted along with a particular form for the system in (7), which has been applied in the work of Krstic and Tsiotras. 13 Step 1.…”

Section: Design Of Inverse Optimal Control Lawmentioning

confidence: 99%

“…Theorem 1. Consider the attitude and altitude nonlinear system (7) of the UAH with unknown external disturbances, which satisfies Assumptions 1 and 2. The composite control law u is designed according to (26).…”

Section: Design Of Inverse Optimal Control Lawmentioning

confidence: 99%

“…In the work of Budiyono and Wibowo, 6 an optimal tracking controller has been designed for a small-scale unmanned helicopter by using feedback linearization. For the purpose of tracking the hover attitude trajectory, a linear quadratic optimal model-following control has been developed for a helicopter in the work of Pieper et al 7 Under the requirement of the precision of the modeling, the optimal control with respect to nonlinear systems, which requires solving the nonlinear HJB equation, are further studied. In the work of Enns and Si, 8 a neural network (NN) dynamic programming control method under optimal condition for the UAH has been utilized.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Inverse optimal control for unmanned aerial helicopters with disturbances

2018

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary This paper proposes an optimal control method of an unmanned aerial helicopter (UAH) with unknown disturbances. Solving the Hamilton‐Jacobi‐Bellman (HJB) equation is considered as the common approach to design an optimal controller under a meaningful cost function when facing the nonlinear optimal control problem. However, the HJB equation is hard to solve even for a simple problem. The inverse optimal control method that avoids the difficulties of solving the HJB equation has been adopted. In this inverse optimal control approach, a stabilizing optimal control law and a particular cost function that are obtained by a control Lyapunov function are required. An integrator backstepping method is used in designing the optimal control law of the UAH. Furthermore, a disturbance‐observer–based control (DOBC) approach has been adopted in the optimal control law for dealing with the unknown disturbances of the UAH system. Simulation results have been given to certify the stability of the nonlinear UAH system and the validity of this developed control method.

show abstract

Section: Problem Statement and Modelingmentioning

confidence: 99%

“…For system (7), under the result of Lemma 2, the following nonlinear disturbance observer is used to estimate the unknown disturbances D, which is given by…”

Section: Design Of Disturbance Observermentioning

confidence: 99%

Section: Design Of Inverse Optimal Control Lawmentioning

confidence: 99%

Section: Design Of Inverse Optimal Control Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Inverse optimal control for unmanned aerial helicopters with disturbances

2018

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

Model following adaptive control for the complex dynamic network with the dynamic matrix‐relation links

Wang

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

This article formulates and solves a problem of model following adaptive control (MFAC) of nodes in the complex dynamic network (CDN) with the dynamic matrix-relation links. From the perspective of composite systems, the CDN with the dynamic matrix-relation links is considered as an interconnected system composed of intercoupling node subsystem (NS) and link subsystem (LS). Based on this idea, employing the Riccati matrix differential equation as the dynamic equation of LS, and the vector differential equation with the second derivative term as the dynamic equation of NS. Then, to remove the restriction that the state of LS is unavailable, an adaptive control scheme is proposed for NS with the help of constructed dynamic auxiliary tracking target for LS, such that the problem of MFAC of nodes is solved. Besides, it is proved mathematically that the state of NS is asymptotically tracking the state of the given reference model when the state of LS is asymptotically tracking the constructed dynamic auxiliary tracking target. Finally, the numerical simulation is given to verify the effectiveness of the theoretical results in this article.

show abstract

Discrete-time Sliding Mode Control

Monsees¹

2006

Lecture Notes in Control and Information Science

View full text Add to dashboard Cite

This chapter discusses the concept of discrete-time sliding mode control (DTSMC) and its design procedure. It also covers how the states are brought to a predefined sliding surface mathematically and kept in a region near to the surface within a small band. This band is termed as an ultimate band in the field of DTSMC, which denotes the degree of robustness. Researchers have been working to find out different approaches to reach to that surface, but the most promising and welldefined way is reaching law approach. The idea of reaching law is discussed briefly in this chapter with examples for better understanding of the design procedure. In this chapter, a small introduction of continuous time sliding mode control (CTSMC) is given. Finally, the current state of the art is presented.

show abstract

Linear quadratic optimal model-following control of a helicopter in hover

Cited by 15 publications

References 0 publications

Inverse optimal control for unmanned aerial helicopters with disturbances

Inverse optimal control for unmanned aerial helicopters with disturbances

Model following adaptive control for the complex dynamic network with the dynamic matrix‐relation links

Discrete-time Sliding Mode Control

Contact Info

Product

Resources

About