Optimal control method on assembly precision for a remanufactured car engine based on state space model

Purpose The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem. Design/methodology/approach Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed. Findings The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study. Originality/value The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

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Optimal Control Deconvolution for Velocity Meter Signals

Journal of Vibration and Acoustics ◽

10.1115/1.3269473 ◽

1988 ◽

Vol 110 (1) ◽

pp. 17-23

Author(s):

P. M. Clarkson ◽

J. K. Hammond

Keyword(s):

Experimental Data ◽

Optimal Control ◽

State Space ◽

State Space Model ◽

The State ◽

Measurement Noise ◽

Deconvolution Method ◽

System Parameters ◽

Space Model ◽

Inaccurate Estimation

A method of deconvolution has been developed which uses the techniques of optimal control. The application of the technique to velocity meter signals is presented. It is shown that provided a state-space model of the transducer dynamics can be obtained the method can provide effective deconvolution even when the data are corrupted by measurement noise. As well as the deconvolution method and the control of the measurement noise the formation of the state-space model and the effects of inaccurate estimation of system parameters are considered. Results are presented using both simulated and experimental data.

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Bayesian monthly index for building activity based on mixed frequencies: the case of Chile

Journal of Economic Studies ◽

10.1108/jes-01-2021-0022 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Byron J. Idrovo-Aguirre ◽

Javier E. Contreras-Reyes

Keyword(s):

Kalman Filter ◽

Bayesian Inference ◽

State Space ◽

State Space Model ◽

Natural Extension ◽

The State ◽

Likelihood Method ◽

Content Type ◽

Space Model ◽

Construction Activity

PurposeThis paper combines the objective information of six mixed-frequency partial-activity indicators with assumptions or beliefs (called priors) regarding the distribution of the parameters that approximate the state of the construction activity cycle. Thus, this paper uses Bayesian inference with Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon.Design/methodology/approachUnlike other economic sectors of similar importance in aggregate gross domestic product, such as mining and industry, the construction sector lacked a short-term measure that helps to identify its most recent performance.FindingsIndeed, because these priors are susceptible to changes, they provide flexibility to the original Imacon model, allowing for the assessment of risk scenarios and adaption to the greater relative volatility that characterizes the sector's activity.Originality/valueThe classic maximum likelihood method of estimating the monthly construction activity index (Imacon) is rigid to the incorporation of new measures of uncertainty, expectations or different volatility (risks) levels in the state of construction activity. In this context, this paper uses Bayesian inference with 10,000 Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon, inspired by the original works of Mariano and Murasawa (2003) and Kim and Nelson (1998). Thus, this paper consists of a natural extension of the classic method used by Tejada (2006) in the estimation of the old Imacon.

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Optimal control strategy of state feedback control for surface-mounted PMSM drives based on auto-tuning of seeker optimization algorithm

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-201630 ◽

2021 ◽

pp. 1-21

Author(s):

Junhao Cao ◽

Xiaodong Sun ◽

Xiang Tian

Keyword(s):

Optimal Control ◽

State Space ◽

Optimization Algorithm ◽

State Feedback ◽

State Space Model ◽

The State ◽

Load Torque ◽

Space Model ◽

Auto Tuning ◽

Seeker Optimization Algorithm

This paper focus on a state feedback controller (SFC)-based optimal control scheme for surface-mounted permanent-magnet synchronous motor (SPMSM) with auto-tuning of controller built on seeker optimization algorithm (SOA). First, based on the nonlinear state-space model of SPMSM, voltage feedforward compensation is used to design a linear SFC. Then in order to guarantee the steady performance in speed and current, integral models considering the errors of rotor speed and current response in d-axis are added in the state space model of SPMSM. Furthermore, by statically decoupling the load torque in the state equation, feedforward compensation is implemented on the load torque to improve the dynamic performance of the controller. The load torque is estimated using disturbance observer with reasonable parameter selection. Then, with the consideration of the search capacity of seeker optimization algorithm (SOA), it is adopted to acquire matrix coefficient of the presented controller. Furthermore, in order to suppress the speed overshoot, a penalty term is introduced to the fitness index. The performance of the proposed method has been validated experimentally and compared with the conventional method under different conditions.

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Simultaneous quadrotor autopilot system and collective morphing system design

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-01-2020-0026 ◽

2020 ◽

Vol 92 (7) ◽

pp. 1093-1100

Author(s):

Oguz Kose ◽

Tugrul Oktay

Keyword(s):

State Space ◽

State Space Model ◽

Optimization Method ◽

The State ◽

Control Technique ◽

Matlab Simulink ◽

Content Type ◽

Space Model ◽

Proposed Model ◽

Long Time

Purpose The purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach Quadrotor design is made by using Solidworks drawing program and some mathematical performance relations. Modelling and simulation are performed in Matlab/Simulink program by using the state space model approaches with the parameters mostly taken from Solidworks. Proportional integral derivative (PID) approach is used as control technique. Morphing amount and the best PID coefficients are determined by using SPSA algorithm. Findings By using SPSA algorithm, the amount of morphing and the best PID coefficients are determined, and the quadrotor longitudinal and lateral flights are made most stable via morphing. Research limitations/implications It takes quite a long time to model the quadrotor in Solidworks and Matlab/Simulink with the state space model and using the SPSA algorithm. However, this situation is overcome with the proposed model. Practical implications Optimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors. Social implications SPSA optimization method is useful in terms of cost, time and practicality. Originality/value It is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.

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BOUNDARY CONTROL OF THE BLACK–SCHOLES PDE FOR OPTION DYNAMICS STABILIZATION

Annals of Financial Economics ◽

10.1142/s2010495216500093 ◽

2016 ◽

Vol 11 (02) ◽

pp. 1650009

Author(s):

GERASIMOS G. RIGATOS

Keyword(s):

State Space ◽

Boundary Control ◽

Control Method ◽

Tracking Error ◽

State Space Model ◽

The State ◽

Control Input ◽

Space Model ◽

Virtual Control ◽

Black Scholes

The objective of the paper is to develop a boundary control method for the Black–Scholes PDE which describes option dynamics. It is shown that the procedure for numerical solution of Black–Scholes PDE results into a set of nonlinear ordinary differential equations (ODEs) and an associated state equations model. For the local subsystems, into which a Black–Scholes PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the Black–Scholes PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem’s dynamics and can eliminate the subsystem’s tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the Black–Scholes PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the Black–Scholes PDE system so as to assure that all its state variables will converge to the desirable setpoints.

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A Nonlinear Optimal Control Approach for Multi-DOF Brachiation Robots

International Journal of Humanoid Robotics ◽

10.1142/s0219843621500158 ◽

2021 ◽

Author(s):

G. Rigatos ◽

M. Abbaszadeh ◽

K. Busawon ◽

Z. Gao ◽

J. Pomares

Keyword(s):

Optimal Control ◽

Control Problem ◽

State Space ◽

Control Method ◽

State Space Model ◽

Nonlinear Optimal Control ◽

Time Step ◽

Control Approach ◽

Space Model ◽

Optimal Control Approach

This paper proposes a nonlinear optimal control approach for mulitple degrees of freedom (DOF) brachiation robots, which are often used in inspection and maintenance tasks of the electric power grid. Because of the nonlinear and multivariable structure of the related state-space model, as well as because of underactuation, the control problem of these robots is nontrivial. The dynamic model of the brachiation robots undergoes first approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the Jacobian matrices of the brachiation robots’ state-space model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the brachiation robots, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the brachiation robots, under moderate variations of the control inputs.

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Modeling of CCLP in koryo extract production

Chemical Product and Process Modeling ◽

10.1515/cppm-2020-0007 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Ji Chol ◽

Ri Jun Il

Keyword(s):

Process Control ◽

Medical Care ◽

State Space ◽

State Space Model ◽

The State ◽

Space Model ◽

Effective Components ◽

Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

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