Evaluating the performance of a post-translational dynamic metabolic control system

Parallel manipulators with less than six degrees of freedom (DOF) have been increasingly used in high-speed hybrid machine tools. The structural features of parallel manipulators are dynamic, a characteristic that is particularly significant when these manipulators are used in high-speed machine tools. However, normal kinematic control method cannot satisfy the requirements of the control system. Many researchers use model-based dynamic control methods, such as the dynamic feedforward control method. However, these methods are rarely used in hybrid machine tools because of the complex dynamic model of the parallel manipulator. In order to study the dynamic control method of parallel manipulators, the dynamic feedforward control method is used in the dynamic control system of a 3-PSP (prismatic-spherical-prismatic) 3-DOF spatial parallel manipulator used as a spindle head in a high-speed hybrid machine tool. Using kinematic analysis as basis and the Newton-Euler method, we derive the dynamic model of the parallel manipulator. Furthermore, a model-based dynamic feedforward control system consisting of both kinematic control and dynamic control subsystems is established. The dynamic control subsystem consists of two modules. One is used to eliminate the influence of the dynamic characteristics of high-speed movement, and the other is used to eliminate the dynamic disturbances in the milling process. Finally, the simulation model of the dynamic feedforward control system of the 3-PSP parallel manipulator is constructed in Matlab/Simulink. The simulations of the control system eliminating the influence of the dynamic characteristics and dynamic disturbances are conducted. A comparative study between the simulations and the normal kinematic control method is also presented.The simulations prove that the dynamic feedforward control method effectively eliminates the influence of the dynamic disturbances and dynamic characteristics of the parallel manipulator on high-speed machine tools, and significantly improves the trajectory accuracy. This is the first attempt to introduce the dynamic feedfordward control method into the 3-PSP spatial parallel manipulator whose dynamic model is complex and provides a study basis for the real-time dynamic control of the high-speed hybrid machine tools.

Advanced high quality radiation therapy techniques such as IMRT require an accurate delivery of precisely modulated fluence patterns to the target volume. Intrafractional motion, however, may considerably deteriorate the accuracy of the dose delivery. To compensate for these errors, a dynamic real-time capable MLC control system was designed and implemented which compensates during the delivery for the motion and deformation of the target volume. Specialized algorithms were developed which constantly optimize the MLC aperture based on online provided target information allowing the sparing of adjacent organs-at-risk from additional dose. These algorithms were implemented in a dynamic target tracking control system for the Siemens 160 MLCTM. Experiments using different phantom setups were performed to assess and quantify the quality of the tracking technique. The results showed that 2-dimensional target motion can be compensated for with the new control system. For a clinical IMRT dose distribution, the congruence within 2 % / 2 mm with a static reference delivery was increased from 18.75% to 76.79 %. Similar improvements were achieved for the delivery of a complete IMRT treatment fraction to a moving lung phantom. However, the quality of the tracking system is limited by the system’s latency times. The experiments proved that the tracking concept, the new algorithms and the dynamic control system allow to effectively compensate for target motion in real-time. It therefore can increase the accuracy and the quality of the treatment delivery.KeywordsAdaptive RadiotherapyTumor TrackingDynamic Multileaf CollimatorMotion Management

Since no engine clutch exists in the power-split system of the hybrid electric bus, the torque variations of the power sources (i.e., the engine, motor, and generator) are easy to cause system shock during mode switching. To yield a better control effect, the model predictive control (MPC) based dynamic coordination control strategy (DCCS) is considered as an effective solution to guarantee the drivability and reliability of the system. However, the existing MPC-based DCCS still have the challenge of model accuracy and computational complexity. Therefore, to solve the problem, a data-driven Fast-MPC(FMPC) based DCCS is proposed for a power-split hybrid electric bus in this paper. Firstly, to ensure the accuracy of the engine dynamic model, a piecewise data fitting method is proposed by analyzing the historical data of the engine. Then, considering the high dimension of the control model (based on the dynamics model of the power-split system), a fast solving method for the quadratic optimization problem of MPC is proposed with respect to the proper assumption and simplification. Finally, to verify the effectiveness of the proposed control strategy, offline simulation and hardware-in-the-loop (HIL) simulation experiments are carried out. Simulation results show that good driving comfort and real-time performance have been achieved. This research is expected to promote the application of MPC in the DCCS and further improve the dynamic performance of the power-split system.

Dynamic intelligent scheduling and control of reconfigurable parallel architectures for computer vision/image processing

An airbreathing hypersonic vehicle(AHV) generally adopts a scramjet engine as its propulsion, which needs strict conditions of flight dynamic pressure. A new dynamic pressure control system is presented, in which the AHV autopilot directly uses dynamic pressure to track the dynamic pressure command. Firstly, the dynamic pressure model of the AHV is established and the state equations of dynamic pressure control are given. Then, the dynamic pressure control system is implemented using the LQR optimal control theory. The dynamic pressure error is augmented in order to add an integral control to track the dynamic pressure with zero steady error. The structure of the dynamic pressure control system is also obtained. The simulation results show that the dynamic pressure control system has a good performance for guaranteeing the dynamic pressure of the scramjet engine with the disturbance of drag and thrust considered.

Multi-objective non-simultaneous dynamic optimal control for an ejector expansion heat pump with thermal storages

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

In this paper, a reinforcement learning (RL) based dynamic inverse attitude control scheme is proposed for near-space vehicle (NSV). Firstly, the conventional dynamic inverse control is employed to ensure the basic capability of NSV attitude tracking. Subsequently, RL is employed to tackle the system uncertainties. Actor-critic RL method is adopted to generate a compensation control signal in order to track attitude command better. Finally, simulation results illustrate that the proposed RL based dynamic inverse control scheme can obtain a better performance compared with the conventional dynamic inverse control scheme.

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.

To what extent and in what ways can two closed-loop dynamic systems be said to be similar? This question arises in a wide range of dynamic systems modeling and control system applications. For example, bounds on error models are fundamental to the controller optimization with modern control methods. Metrics such as the structured singular value are direct measures of the degree to which properties such as stability or performance are maintained in the presence of specified uncertainties or variations in the plant model. Similarly, controls-related areas such as system identification, model reduction, and experimental model validation employ measures of similarity between multiple realizations of a dynamic system. Each area has its tools and approaches, with each tool more or less suited for one application or the other. Similarity in the context of closed-loop model validation via flight test is subtly different from error measures in the typical controls oriented application. Whereas similarity in a robust control context relates to plant variation and the attendant affect on stability and performance, in this context similarity metrics are sought that assess the relevance of a dynamic system test for the purpose of validating the stability and performance of a similar dynamic system. Similarity in the context of system identification is much more relevant than are robust control analogies in that errors between one dynamic system (the test article) and another (the nominal design model) are sought for the purpose of bounding the validity of a model for control and analysis. Yet system identification typically involves open-loop plant models which are independent of the control system (with the exception of limited developments in closed-loop system identification which is nonetheless focused on obtaining open-loop plant models from closed-loop data). Moreover the objectives of system identification are not the same as a flight test and hence system identification error metrics are not directly relevant. In applications such as launch vehicles where the open loop plant is unstable it is similarity of the closed-loop system dynamics of a flight test that are relevant.

This paper presents a study in the field of electric motor drive testing. One example of propulsion drive load dynamic control system is presented. Mathematical model of the propulsion motor drive of Tesla Roadster is presented. Suggested dynamic load control system is verified using a MATLAB/Simulink computer model and a test bench. The developed methodology can be recommended to adjust the electrical drives for different kinds of testing equipment, including the synchronous, reluctance, induction, and direct current machines.

With high penetration of solar Photovoltaic (PV) resources, controlling voltage of distribution systems with conventional control strategies such as fixed set-point operation mode of Load Tap Changers (LTCs) or even in some cases with Line-Drop Compensation (LDC) operation mode is not possible anymore. In case of LDC operation mode, optimizing the R & X settings is time-consuming and is required with every change in PV penetration level. Hence, a robust and dynamic control strategy is required to handle the challenges induced by high penetration levels of solar PV resources. In this work, dynamic control of LTC and Line Voltage Regulators (LVRs) of a Hawaii substation with multiple voltage control zones is implemented in OpenDSS and controlled by Python through COM interface. Then its performance in terms of reducing voltage volatility, Operation and Maintenance (O&M) costs and ramp-rate, facilitating LDC R&X settings optimization and energy saving is evaluated. Furthermore, the impact of Dynamic Var Controllers (DVCs) in accomplishing the aforementioned objectives is illustrated through comprehensive time-series simulations. Simulation results show that by using dynamic control of voltage regulators and DVCs, PV penetration increases by 45% due to reducing voltage volatility. Moreover, generation ramp-rate reduces by 8%. Furthermore, energy savings are tripled compared to energy savings achieved by existing controls. Finally, the utilization of DVCs reduces the O&M costs by 14%.

In the hot strip rolling process, the performance of a monitoring system for automatic gauge control (MN-AGC) is influenced greatly by the model mismatch which is caused by the variation of model parameters values. A constrained dynamic matrix control (CDMC) strategy that includes a prediction model, rolling optimization, and feedback correction was used in the MN-AGC. First, the conventional Smith prediction-based control strategy for the MN-AGC was analyzed. Second, the performance index function and optimal control of the CDMC strategy were determined. Finally, simulations and industrial experiments were conducted. The results showed that both control strategies provided good control performance. When model mismatch occurred, the Smith predictor-based MN-AGC resulted in significant overshoot or even oscillations but the control performance of the CDMC-based MN-AGC was not influenced by changes in the model parameters.

The control of a small-scale power network incorporating four generators is investigated in this paper. The small-signal dynamic characteristics and control system performance are assessed using individual channel analysis and design (ICAD). ICAD is motivated through the need to understand the effect of system interaction on both the control design task and robustness. System interaction on individual transmissions is explicitly considered through the use of multivariable structure functions which, although scalar, encapsulate the multivariable nature of the system. The strength of such an approach is that highly successful single-input single-output classical control techniques such as Nyquist/Bode can be used without loss of multivariable information. For the example network, ICAD enables the design of the individual controllers to be performed independently of one another and determines that the effect of system interaction is not detrimental to network stability. It therefore potentially provides a strong platform for consideration of large-scale networks with embedded generation. Finally, the ICAD network control analysis and design is corroborated by nonlinear dynamic simulation running in PSS/E.

Improved dynamic event-triggered anti-unwinding control for autonomous underwater vehicles