Simplification of the dynamic model of a hydraulic rockbreaker for implementation in a model-based control scheme

2018 ◽  
Vol 42 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Louis-Francis Y. Tremblay ◽  
Marc Arsenault ◽  
Meysar Zeinali
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Tracking Error ◽  
Planar Motion ◽  
Real Time Control ◽  
Joint Torques ◽  
Model Based ◽  
Lagrange Formulation ◽  
Time Control ◽  
Hydraulic Excavators

A model-based approach to control the automation of hydraulic excavators and rockbreakers necessitates an adequate dynamic model to increase the robustness of the controller and improve its performance (e.g., reduce tracking error). Most previous efforts in developing dynamic models for excavators have assumed planar motion while neglecting the dynamic effects of hydraulically driven prismatic actuators. In this paper, a dynamic model of the mechanical subsystem of a hydraulic rockbreaker is developed using the Euler–Lagrange formulation. The model considers the contributions of the hydraulic actuators and does not assume planar motion. Potential simplifications to the dynamic model are then introduced to facilitate the model’s parameterization for developing an adaptive control algorithm. To evaluate their level of accuracy, these simplified dynamic models are then evaluated based on the required joint torques for specified trajectories. It is shown that the proposed simplifications reduce the complexity of the dynamic model while preserving its accuracy, which is attractive for real-time control applications.

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

Optimal control of a novel uninterrupted multi-speed transmission for hybrid electric mining trucks

2019 ◽  
Vol 233 (12) ◽  
pp. 3235-3245 ◽  
Author(s):  
Weiwei Yang ◽  
Jiejunyi Liang ◽  
Jue Yang ◽  
Nong Zhang
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Strategy ◽  
Dynamic Models ◽  
Fuel Efficiency ◽  
Real Time Control ◽  
Traction Motor ◽  
Time Control ◽  
Power Split ◽  
Shift Strategy

Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.

scholarly journals Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process

Pharmaceutics ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 181 ◽  
Author(s):  
Brecht Vanbillemont ◽  
Niels Nicolaï ◽  
Laurens Leys ◽  
Thomas De Beer
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Freeze Drying ◽  
Fixed Time ◽  
Chamber Pressure ◽  
Interface Temperature ◽  
Real Time Control ◽  
Choked Flow ◽  
Model Based ◽  
Time Control

The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature ( T i ) exceeds the the collapse temperature ( T c ) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of T i and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.

A parameter identification method for robot dynamic models using a balancing mechanism

Robotica ◽  
1989 ◽  
Vol 7 (4) ◽  
pp. 327-337 ◽  
Author(s):  
T. G. Lim ◽  
H. S. Cho ◽  
W. K. Chung
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Least Square ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Robot Dynamics ◽  
Dynamic Parameters ◽  
Joint Torques ◽  
Motion Data ◽  
Identification Method

SUMMARYAccurate modeling of robot dynamics is a prerequisite for the design of model-based control schemes and enhancement of the performance of the robot. The dynamic parameters associated with a pseudo-inertia matrix are often difficult to identify accurately because the inertia torques are small in comparison to gravity loadings, thus creating signal processing problem. The identification method presented in this paper utilizes a balancing mechanism which increases the estimation accuracy of the dynamic parameters. The balancing mechanism has the effect of amplifying the inertia-related torque signal by eliminating gravity loadings acting on the robot joints. A series of motion data were experimentally obtained through sequential test steps. By incorporating the measured information about joint torques, angular positions, velocities and accelerations the least square algorithm was used to identify the dynamic parameters. The estimated values were converted to those of the original robot model to obtain its dynamic model parameters. The identified robot dynamic model was shown to be accurate enough to predict the actual robot motions.

Model-based, real-time control of electrical power systems

1996 ◽  
Vol 26 (4) ◽  
pp. 470-482 ◽  
Author(s):  
A.J. Gonzalez ◽  
R.A. Morris ◽  
F.D. McKenzie ◽  
D.J. Carreira ◽  
B.K. Gann
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Electrical Power ◽  
Real Time Control ◽  
Electrical Power Systems ◽  
Model Based ◽  
Time Control

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

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