LQ tracking controls with fixed terminal states and their application to receding horizon controls

Park, Jung Hun; Han, Seung Baik; Кwon, Wook Hyun

doi:10.1016/j.sysconle.2008.03.006

Cited by 24 publications

(13 citation statements)

References 15 publications

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“…The goal with the example is to numerically compare the performance of the solutions. The second example from the work Park et al (2008) was chosen to be implemented because it fits the need to present tracking results on a higher dimensional (four states, two inputs) but not overly complicated system. The example is further related to the topic as it was applied to test receding horizon LQ tracking control in Park et al (2008).…”

Section: Application Examplementioning

confidence: 99%

“…The second example from the work Park et al (2008) was chosen to be implemented because it fits the need to present tracking results on a higher dimensional (four states, two inputs) but not overly complicated system. The example is further related to the topic as it was applied to test receding horizon LQ tracking control in Park et al (2008). The parameters of the longitudinal dynamical model of the Navion aircraft presented in Park et al (2008) The control inputs are limited by saturation to comply with the capabilities of the Navion aircraft.…”

Section: Application Examplementioning

confidence: 99%

“…The example is further related to the topic as it was applied to test receding horizon LQ tracking control in Park et al (2008). The parameters of the longitudinal dynamical model of the Navion aircraft presented in Park et al (2008) The control inputs are limited by saturation to comply with the capabilities of the Navion aircraft. The maximum deflections of the elevator are −30 • up / +20 • down so the symmetrical limit is ±0.349 rad.…”

Section: Application Examplementioning

confidence: 99%

See 2 more Smart Citations

Development and performance evaluation of an infinite horizon LQ optimal tracker

Bauer

Bokor

2018

European Journal of Control

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The paper presents an infinite horizon LQ optimal tracking control solution (LQ tracker) for discrete time linear time invariant systems. The reference preview need is reduced to only two steps irrespective of the type of reference signal making real-time implementation an achievable goal. A rigorous proof of optimality is provided for a set of infinite horizon reference commands which includes the linear combination of constant and exponentially bounded signals. Dissipativity, finite gain and l 1 performance of the controlled system are also evaluated. The behaviour of the proposed LQ tracker and its previously published sub-optimal version with one-step preview is demonstrated in conjunction with an application example. Their performances are compared to those of alternative solutions including set point control and model predictive control. Finally, it is concluded that the proposed rigorous solution of the infinite horizon tracking problem is real-time realizable and performs advantageously compared to other solutions.

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Section: Application Examplementioning

confidence: 99%

Section: Application Examplementioning

confidence: 99%

Section: Application Examplementioning

confidence: 99%

See 1 more Smart Citation

Development and performance evaluation of an infinite horizon LQ optimal tracker

Bauer

Bokor

2018

European Journal of Control

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“…These stability conditions of RHC for linear systems have been extended to cover the stability problem of various control methods. And matrix inequality condition was applied to the tracking control problem [10,11] and robust control problems [12,13]. Therefore, due to such a wide variety of usage, any proposed stability criterion is significant even though it is only a slight deviation from the conventional method to guarantee stability.…”

Section: Introductionmentioning

confidence: 99%

On the stability of receding horizon control based on horizon size for linear discrete systems

2011

J Mech Sci Technol

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In this study, stability conditions of receding horizon control (RHC) based on a horizon size are proposed for linear discrete systems. The proposed stability conditions present a relevant horizon size which can guarantee the stability of RHC even though a final state weighting matrix does not satisfy non-increasing monotonicity of optimal cost. Therefore, the possible range of the final state weighting matrix ensuring the stability of RHC is extended to zero and also it can be applied to the stability problems of other forms of model predictive control like the conventional stability conditions.

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“…The LQ optimal control problem has received a great deal of attention from control theorists and engineers, since the resulting control law is linear with respect to the state and is therefore easy to compute [2][3]. There are many different approaches for solving this problem, such as minimization method using Lagrange multipliers, dynamic programming, Lyapunov function and so on.…”

Section: Introductionmentioning

confidence: 99%

Information fusion based solving method for linear quadratic optimal control problem

Zhen

Wang

Zhou

2009

2009 IEEE International Conference on Control and Automation

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A new solving method based on information fusion estimation (IFE) for linear quadratic optimal control problem is proposed in this paper. According to information fusion estimation theory, by fusing the hard constraint information of system dynamic equation and the soft constraint information of the desired state/output information from the quadratic performance index function, an estimation of the costate sequence is achieved. By fusing the costate information and the constraint information of the control sequence from the quadratic performance index function, an estimation of the optimal control law is obtained. Based on this method, an information fusion state regulator and an information fusion tracker are deduced, the computational results of which are identical to the traditional solving methods through the theoretical analysis. I. INTRODUCTIONHE linear quadratic (LQ) optimal control problem is a well-known fundamental problem in optimal control theory [1]. The LQ optimal control problem has received a great deal of attention from control theorists and engineers, since the resulting control law is linear with respect to the state and is therefore easy to compute [2][3]. There are many different approaches for solving this problem, such as minimization method using Lagrange multipliers, dynamic programming, Lyapunov function and so on.Information fusion estimation (IFE) is the problem of how to best utilize useful information obtained from multiple sources (e.g., multiple sensors) or from a single source over a time period, for estimating an unknown quantity-a parameter or process. The most important application area of IFE is track fusion or track-to-track fusion in target tracking system [4]. Li (2003) established three estimation fusion architectures including centralized, distributed, and hybrid, moreover, he proposed an unified linear data model and two Manuscript

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LQ tracking controls with fixed terminal states and their application to receding horizon controls

Cited by 24 publications

References 15 publications

Development and performance evaluation of an infinite horizon LQ optimal tracker

Development and performance evaluation of an infinite horizon LQ optimal tracker

On the stability of receding horizon control based on horizon size for linear discrete systems

Information fusion based solving method for linear quadratic optimal control problem

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