Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Batch-to-batch Optimization of Batch Crystallization Processes

It is the fact that several process parameters are either unknown or uncertain. Therefore, an optimal control, profile calculated with developed process models with respect to such process parameters may not give an optimal performance when implemented to real processes. This study proposes a batch-to-batch optimization strategy for the estimation of uncertain kinetic.par.ameters in a batch crystallization process of potassium sulfate production. The knowledge of a crystal size distribution of the product at the end of batch operation is used in the proposed methodology. The updated kinetic parameters are applied for determining an optimal operating temperature policy for the next batch run.

Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization

The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability,especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure.In this study,an optimal control method for slurry pressure during shield tunnelling is developed,which is composed of an identifier and a controller.The established identifier based on the random forest(RF)can describe the complex non-linear relationship between slurry pressure and its influencing factors.The proposed controller based on particle swarm optimization(PSO)can optimize the key factor to precisely control the slurry pressure at the normal state of advancement.A data set from Tsinghua Yuan Tunnel in China was used to train the RF model and several performance measures like R2,RMSE,etc.,were employed to evaluate.Then,the hybrid RF-PSO control method is adopted to optimize the control of slurry pressure.The good agreement between optimized slurry pressure and expected values demonstrates a high identifying and control precision.

Process Control and Optimization for Heterologous Protein Production by Methylotrophic Pichia pastoris

The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts have been made to increase heterologous protein productivity by P. pastoris in recent years. When new engineered yeast strains are constructed and are ready to use tot industrial protein production, process control and optimization techniques should be applied to improve the fermentation performance in the following aspects： （1） increase recombinant cell concentrations in fermentor to high density during growth phase; （2） effectively induce heterologous proteins by enhancing/stabilizing titers or concentrations of the proteins during induction phase; （3） decrease operation costs by relieving the working loads of heat-exchange and oxygen supply. This article reviews and discusses the key and commonly used techniques in heterologous protein production by P. pastoris, with the focus on optimizations of fermentation media and basic operation conditions, development of optimal glycerol feeding strategies for achieving high density cultivation of P. pastoris and effective heterologous protein induction methods by regulating specific growth rate, methanol concentration, temperatures, mixture ratio of multi-carbon substrates, etc. Metabolic analysis for recombinant protein production by P. pastoris is also introduced to interpret the mechanism of sub-optimal heterologous protein production and to explore further optimal expression methods.

Multiconstraint adaptive three-dimensional guidance law using convex optimization

The traditional guidance law only guarantees the accuracy of attacking a target. However, the look angle and acceleration constraints are indispensable in applications. A new adaptive three-dimensional proportional navigation(PN) guidance law is proposed based on convex optimization. Decomposition of the three-dimensional space is carried out to establish threedimensional kinematic engagements. The constraints and the performance index are disposed by using the convex optimization method. PN guidance gains can be obtained by solving the optimization problem. This solution is more rapid and programmatic than the traditional method and provides a foundation for future online guidance methods, which is of great value for engineering applications.

Study on optimization control method based on artificial neural network

In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

Study of trajectory optimization using terminal-node adaptive-altered spline algorithm

The advantage of using a spline function to evaluate the trajectory parameters optimization is discussed. A new method that using adaptive varied terminal-node spline interpolation for solving trajectory optimization is proposed. And it is validated in optimizing the trajectory of guided bombs and extended range guided munitions （ERGM）. The solutions are approximate to the real optimization results. The advantage of this arithmetic is that it can be used to solve the trajectory optimization with complex models. Thus, it is helpful for solving the practical engineering optimization problem.

Optimization of projectile state and trajectory of reentry body considering attainment of carrying aircraft

As an improvement of traditional maximum range trajectory optimization, both the projectile state and control sequence of reentry body are optimized to achieve the maximum range. To ensure the optimal projectile state is in the reachable domain of the carrying aircraft, the climb of the carrying aircraft and the flight of reentry body are optimized as a whole based on the hp-adaptive pseudospectral method. Meanwhile, the path constraints, control constraints and initial/terminal states constraints are considered in the optimization as well. In face of the fact that the range of the optimal control solution resulted from the pseudospectral method may be unsmooth, virtual control variables (second derivatives of the angle of attack and bank angle) are proposed in the optimization, while the angle of attack and bank angle are treated as the virtual state variables to guarantee the smoothness of the optimal control solution. The validity of the virtual control variables in smoothing the optimal control solution is verified by simulation, and the optimality of the resulting projectile state and control sequence for reaching the maximum range is confirmed using the indirect approach. © 1990-2011 Beijing Institute of Aerospace Information.

System-L evel Efficiency Optimization of a Linear Induction Motor Drive System

Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.

A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.

A CALCULATION OF OPTIMAL FLIGHT TRAJECTORY USING THE PARAMETERIZED OPTIMIZATION METHOD

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CONTROLLING ROBOT MANIPULATORS BY DYNAMIC PROGRAMMING

A certain number of considerations should be taken into account in the dynamic control of robot manipulators as highly complex non-linear systems.In this article,we provide a detailed presentation of the mechanical and electrical impli- cations of robots equipped with DC motor actuators.This model takes into account all non-linear aspects of the system.Then,we develop computational algorithms for optimal control based on dynamic programming.The robot's trajectory must be predefined,but performance criteria and constraints applying to the system are not limited and we may adapt them freely to the robot and the task being studied.As an example,a manipulator arm with 3 degrees of freedom is analyzed.

Radial Basis Function Interpolation and Galerkin Projection for Direct Trajectory Optimization and Costate Estimation

This work presents a novel approach combining radial basis function(RBF)interpolation with Galerkin projection to efficiently solve general optimal control problems.The goal is to develop a highly flexible solution to optimal control problems,especially nonsmooth problems involving discontinuities,while accounting for trajectory accuracy and computational efficiency simultaneously.The proposed solution,called the RBF-Galerkin method,offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points.The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush-Kuhn-Tucker(KKT)conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem,if a set of discrete conditions holds.The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem.In addition,the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.

Trajectory optimization and applications using high performance solar sails

The high performance solar sail can enable fast missions to the outer solar system and produce exotic non-Keplerian orbits.As there is no fuel consumption,mission trajectories for solar sail spacecraft are typically optimized with respect to flight time.Several investigations focused on interstellar probe missions have been made,including optimal methods and new objective functions. Two modes of interstellar mission trajectories,namely 'direct flyby' and 'angular momentum reversal trajectory',are compared and discussed.As a foundation,a 3D non-dimensional dynamic model for an ideal plane solar sail is introduced as well as an optimal control framework.A newly found periodic double angular momentum reversal trajectory is presented,and some properties and potential applications of this kind of inverse orbits are illustrated.The method how to achieve the minimum periodic inverse orbit is also briefly elucidated.

Optimization of rice wine fermentation process based on the simultaneous saccharification and fermentation kinetic model

Chinese rice wine making is a typical simultaneous saccharification and fermentation （SSF） process. During the fermentation process, temperature is one of the key parameters which decide the quality of Chinese rice wine. To optimize the SSF process for Chinese rice wine brewing, the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model. The kinetic parameters as a function of temperature were evaluated using the software Origin8.0. Combing these functions with the mathematical model, an appropriate form of the model equations for the SSF considering the effects of temperature were developed. The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model. The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm. The optimum temperature profile began at a low temperature of 26℃ up to 30 h. The operating temperature increased rapidly to 31.9 ℃, and then decreased slowly to 18℃ at 65 h. Thereafter, the temperature was maintained at 18 ℃ until the end of fermentation. A maximum ethanol production of 89.3 g.L 1 was attained. Conceivably, our model would facilitate the improvement of Chinese rice wine production at the industrial scale.

Optimal control of stretching process of flexible solar arrays on spacecraft based on a hybrid optimization strategy

The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated,and a hybrid optimization strategy based on Gauss pseudospectral method（GPM） and direct shooting method（DSM） is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions.The results indicate that the method is effective with good robustness.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

Design of hybrid damping controller based on multi-target gravitational search optimization algorithm in a multi-machine power system with high penetration of PV park

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.

Aircraft Optimal Separation Allocation Based on Global Optimization Algorithm

A dynamic programming-sequential quadratic programming(DP-SQP)combined algorithm is proposed to address the problem that the traditional continuous control method has high computational complexity and is easy to fall into local optimal solution.To solve the globally optimal control law sequence,we use the dynamic programming algorithm to discretize the separation control decision-making process into a series of sub-stages based on the time characteristics of the separation allocation model,and recursion from the end stage to the initial stage.The sequential quadratic programming algorithm is then used to solve the optimal return function and the optimal control law for each sub-stage.Comparative simulations of the combined algorithm and the traditional algorithm are designed to validate the superiority of the combined algorithm.Aircraft-following and cross-conflict simulation examples are created to demonstrate the combined algorithm’s adaptability to various conflict scenarios.The simulation results demonstrate the separation deploy strategy’s effectiveness,efficiency,and adaptability.