Characteristic Model-Based All-Coefficient Adaptive Control Method and Its Applications

Wu, Huai‐Ning; Hu, Jun; Xie, Yongchun

doi:10.1109/tsmcc.2006.887004

Cited by 135 publications

(79 citation statements)

References 9 publications

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“…Based on the principle of input and output equivalence, we differentiate system outputs α, β, ϕ to second order, apply identical substitution and Taylor series approximate expansion (Wu et al, 2007). The discrete model of input-output time-varying second-order of α, β, and ϕ is as follows:…”

Section: Characteristic Model Of the Reentry Hypersonic Vehiclementioning

confidence: 99%

“…Characteristic modeling, which was proposed by Wu et al (2007), is constructed based on dynamical characteristics, environmental characteristics, and control performance, no only based on accurate system dynamics in conventional modeling . The main advantages of the characteristic modeling are depicting high-order linear time-varying systems and non-linear systems by low-order linear time-varying systems with a simple structure.…”

Section: Introductionmentioning

confidence: 99%

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Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Zhang

Sun

2017

Front. Robot. AI

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Citation:Zhang J, Sun T and Pan Y (2017) Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints. Front. Robot. AI 4:23. doi: 10.3389/frobt.2017 Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC) for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H 2 /H ∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs) and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.

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Section: Characteristic Model Of the Reentry Hypersonic Vehiclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Zhang

Sun

2017

Front. Robot. AI

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“…The general all-coefficient adaptive control u(k) consists of a maintaining/tracking control u m (k), a golden-section feedback control u f (k), a logic differential control u d (k) and a logic integral control u i (k) [8]. It can be written as…”

Section: All-coefficient Adaptive Control Methodsmentioning

confidence: 99%

“…According to the characteristic modeling method [8], if a plant G(s) is given and the control objective is position keeping or tracking, for a sufficiently small sampling period T that satisfies the sampling theorem and guarantees the controllability requirement for the discretized system, its characteristic model is a second-order time-varying difference equation,…”

Section: Characteristic Model Of Lti Systemsmentioning

confidence: 99%

“…The characteristic model based all-coefficient adaptive control (ACAC) method has been widely used in process control and aerospace industry, and numerous applications have demonstrated its effectiveness [8]. This method is able to provide robust control performance on multi-dimensional complex dynamical systems, while maintaining a simple structure and not requiring the actual plant model [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Characteristic Model Based All-Coefficient Adaptive Control on Flexible Rotor Supported by Active Magnetic Bearings

Long

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Active magnetic bearings (AMBs) have found a wide range of high-speed rotating machinery applications in energy storage, electric power generation, machine tool operation, heart pumps and hybrid vehicles. Compared with conventional mechanical bearings, AMBs need feedback control to support and constrain the rotor and demand additional electronic devices.When AMBs are used to support flexible rotors, their nonlinear characteristics and complex dynamics of flexible rotors cause problems with stabilization and disturbance rejection. Proportionalintegral-derivative (PID) controllers are the most popular control method for AMB applications.They are easy to implement and reasonable control performance can be achieved. However, it is difficult for PID controllers to deliver robust and near optimal performance for complex dynamic systems. In recent years, robust control techniques such as µ-synthesis have also been applied to AMB applications. The µ-synthesis is able to better handle the uncertainties in complex systems and to achieve reliable performance. However, it requires plant and uncertainty models to generate the most suitable solution, which makes this control design not as practical as PID controllers.

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Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

Huang

2014

Adaptive Control & Signal

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The characteristic model-based golden-section adaptive control (CM-GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM-GSAC from its very beginning and explores the underlying implications of the so-called golden-section parameter l 2 0:618. The closed-loop system, which consists of the CM and the GSAC, is a discrete time-varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed-loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time-varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time-varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. 878H. HUANG INTRODUCTIONAs being mentioned in [1], 'It is always better to use an accurate model than not to do so: if there is time delay present and it is neglected, or if there are high frequency dynamics present and they are neglected, an algorithm is more likely to fall over'. However, in practical applications such as the attitude control of hypersonic vehicles that are characterized by strongly coupled multiple states, nonlinearity, high order, and, most importantly, the lack of ground test facilities, it would be rather complicated to develop controllers based on the accurate dynamical model that is highly nonlinear with various uncertainties. Meanwhile, the dynamic performance during controller design is usually unknown when dealing with the nonlinear dynamics directly. A common way is to use the linearization technique at several preselected operation points, for example, the trim conditions of hypersonic vehicles, so as to develop linear controllers. This would be time consuming because of a tremendous amount of points if the vehicle maneuvers frequently and flies in a rapidly changing environment. Meanwhile, controller design based on the linearized model generally lacks adaptability and flexibility. The characteristic model (CM)-based adaptive control thus appears so as to build a bridge between the model complexity and the adaptability.The CM was proposed by Hongxin Wu in the 1990s and has further been developed for more than 20 years [2,3]. The essence of the CM is to use a low-order discrete time-varying system to approach a high-order nonlinear/linear system based on the main features of the plant and the control demands. Rather than dropping information as in the reduced-order m...

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Characteristic Model-Based All-Coefficient Adaptive Control Method and Its Applications

Cited by 135 publications

References 9 publications

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Characteristic Model Based All-Coefficient Adaptive Control on Flexible Rotor Supported by Active Magnetic Bearings

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

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