PID Controller Design for MIMO Systems by Applying Balanced Truncation to Integral-Type Optimal Servomechanism

Ochi, Yoshimasa; Yokoyama, Nobuhiro

doi:10.3182/20120328-3-it-3014.00062

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…In Ref. 8 (Proportional-Integral-Derivative) optimal servomechanism has been derived by using a controller order reduction (or closed-loop balanced truncation) technique. This design method is based on linear quadratic regulator (LQR) theory and fractional balanced reduction (FBR) technique 10 .…”

Section: Figure 1 Prototype Qtw-uavmentioning

confidence: 99%

Design of Gain Scheduled Stability and Control Augmentation System for Quad-Tilt-Wing UAV

Totoki

Ochi

Sato

et al. 2015

AIAA Guidance, Navigation, and Control Conference

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This paper describes design of a stability and control augmentation system (SAS and CAS) for a small prototype quad-tilt-wing (QTW) unmanned aerial vehicle (UAV) developed by Japan Aerospace Exploration Agency (JAXA). Since the dynamics of QTW is originally unstable at most of the tilt angles in the both longitudinal and lateral-directional motions, stabilizing control is necessary for the practical operations. In this paper, a gain scheduled PD-SAS is designed by solving linear matrix inequalities (LMIs) formulated for a static output feedback problem. In addition to SAS, CAS is designed to improve the pilot handling. The CAS is derived based on an integral-type optimal servomechanism and controller order reduction technique. Both the controller gains are scheduled with the tilt angles, since the dynamic characteristics of QTW change widely according to the tilt angles. Nonlinear human-in-the-loop simulation results have shown that the obtained gain scheduled controllers stabilize the QTW in almost all the tilt angles and provide good tracking performance for the attitude commands. NomenclatureA p_i = system matrix of QTW model A c_i = system matrix of linearized PFCS model B p_i = input matrix of QTW model B c_i = input matrix of linearized PFCS model C p_i = measurement matrix of QTW model C c_i = measurement matrix of linearized PFCS model u,v,w = velocity of QTW along body axis p,q,r = angular velocity T d = time constant of PD controller τ w = tilt angle, deg stick θ δ = pitch stick input from remote ground pilot, % clc δ = collective stick input from remote ground pilot, % stick Φ δ = roll stick input from remote ground pilot, % stick Ψ δ = yaw stick input from remote ground pilot, % θ = pitch angle φ = bank angle Q y , Q u = weighting matrix 0 Γ = optimal value of a performance index

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Section: Figure 1 Prototype Qtw-uavmentioning

confidence: 99%

Design of Gain Scheduled Stability and Control Augmentation System for Quad-Tilt-Wing UAV

Totoki

Ochi

Sato

et al. 2015

AIAA Guidance, Navigation, and Control Conference

Self Cite

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“…Using the MATLAB identification toolbox, a linearized model of the boiling process has been obtained around the normal operation point [3]: fuel flow 35.21%, water flow 57.57 %, load level 46.36 %, steam pressure 60 %, oxygen level 50 %, and water level 50 %. The obtained continuous model is the transfer matrix relating the controlled variables to manipulated variables, which is given by (1), where the oxygen level is not shown because it will not be taken account in the design.…”

Section: The Control Structure Design For a Benchmark Boilermentioning

confidence: 99%

“…It can be seen that in this control structure, the steam pressure and the water level changed obviously under the load disturbance, especially when the steam pressure changed, the water level changed more dramatic. The primary cause is that the decentralized control structure does not consider the coupling in the system, so a variety of multivariable PID control strategy are constantly emerging, such as [3] considered applying balanced truncation to integral-type optimal servomechanism; [4] proposed an inverted decoupling method; [5] introduced a method based on H loop-shaping approach; [6] adopted a multivariable decoupling control. The above methods can provide good performance, but the structure and calculation are complex, and lack of generality.…”

Section: Introductionmentioning

confidence: 99%

Partially decentralized control based on IMC for a benchmark boiler

Tan

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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The boiler systems are multivariable nonlinear processes showing great interactions and input constraints under a wide range of operating conditions. This paper proposes a partially decentralized control (PDC) for the problem of a benchmark boiler under the load and pressure change. The design of partially decentralized controllers is based on the non-square internal model control (IMC) principle, which is applied to design non-square decentralized controllers and that are then transformed to partially decentralized ones. This method combines the advantages of decentralized control and multivariate centralized control, and considers the coupling of the plants, thus good performance can be achieved. The proposed method is applied to the boiler model proposed in an IFAC Conference, and the controller used in PID form. Simulation results show that partially decentralized control can achieve better performance than the existing decentralized control, and compared with the multivariable centralized control, it has a simpler structure and is easier to implement and tune, so it is of great practical application potential.

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“…Hence, new multivariable PID design methods are interesting for the process industry. This design can be approached from state space methods that usually try to minimize a H∞ norm as performance measure (Ochi & Yokoyama, 2012;Saeki, 2006;Zheng, Wang, & Lee, 2002). However, these procedures mainly involve delay-free systems, and many industrial processes contain time delays (Zhang et al, 2006).…”

Section: H(s)=g(s)·k(s)·[i + G(s)·k(s)]mentioning

confidence: 99%

“…(63) Figure 12 shows the closed loop system response of the proposed controller in comparison with those obtained with the reference decentralized PI controller in (Morilla, 2012) and the multivariable PI control in (Ochi & Yokoyama, 2012). There is a unit step change in the first reference at t= 0 s, and at t=1000 s, in the second one.…”

Section: Example 2: Boiler Processmentioning

confidence: 99%

Multivariable PID control by decoupling

Garrido

Vázquez

Morilla

2014

International Journal of Systems Science

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This paper presents a new methodology to design multivariable PID controllers based on decoupling control. The method is presented for general n×n processes.In the design procedure, an ideal decoupling control with integral action is designed to minimize interactions. It depends on the desired open loop processes that are specified according to realizability conditions and desired closed loop performance specifications. These realizability conditions are stated and three common cases to define the open loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the windup problem is also considered and a new anti-windup scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

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PID Controller Design for MIMO Systems by Applying Balanced Truncation to Integral-Type Optimal Servomechanism

Cited by 14 publications

References 9 publications

Design of Gain Scheduled Stability and Control Augmentation System for Quad-Tilt-Wing UAV

Design of Gain Scheduled Stability and Control Augmentation System for Quad-Tilt-Wing UAV

Partially decentralized control based on IMC for a benchmark boiler

Multivariable PID control by decoupling

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