Prediction-based stochastic dynamic programming control for excavator

Zhou, Hua; Zhao, Peiwei; Chen, Yinglong; Yang, Huayong

doi:10.1016/j.autcon.2017.08.014

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…By analyzing these sub-samples, the load changes and working characteristics were extracted to predict the multi-virtual torques. On that basis, a stochastic dynamic programming (SDP) control strategy was formulated in [55]. It can extract the working cycle characteristics of excavators through empirical mode decomposition (EMD), truncate the required torque signal samples into several sub-samples, and calculate the required torque to obtain the predicted torque and then use it as input, thereby simplifying the computation and achieving the real-time energy of the system.…”

Section: Global Optimization Strategiesmentioning

confidence: 99%

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Zhang

Wang

et al. 2019

Energies

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Hybrid Construction Machinery (HCM), known as an effective and crucial solution for the issues of environment pollution and energy shortage, has aroused increasing attention from manufacturers and researchers. A suitable energy management strategy is the vital technology to determine the energy saving and emission reduction performance of HCM. In the present paper, the difference between construction machinery and automobiles is first analyzed from the perspective of configuration, and the energy-based HCM configuration classification method is introduced and analyzed. Second, the development of HCM energy management strategy is reviewed along with relevant references, and the HCM energy management strategies are classified and summarized. In the meantime, the characteristics of each strategy are compared and analyzed, and the application of HCM energy management strategy is analyzed based on the relevant research results. Lastly, the state, challenges facing and the trend of HCM energy management strategy are analyzed on the levels of theory, manufacturer and market. According to the analysis, though progress has been achieved in energy management technology of HCM driven by market and policy, many challenges and problems remain in the electrification and intellectualization of HCM and the testing, application and improvement of the strategy. The contribution of this paper can be identified in three points: First, it can be referenced to solve relevant engineering problems. Second, it lays the foundation for the proposal of new ideas. Third, it highlights the state-of-the-art trends and avoids what has already been done.

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Section: Global Optimization Strategiesmentioning

confidence: 99%

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Zhang

Wang

et al. 2019

Energies

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“…However, due to the uncertain real driving cycles, the EMS designed offline based on DP is not feasible in practice, although it is globally optimal in theory. Consequently, stochastic DP (SDP) [14] is a natural alternative to DP, which utilizes the probability distribution extracted from multiple historical driving cycles to deal with the uncertain driving cycles in practice. From the aspect of a real-time implementable EMS, the equivalent consumption minimization strategy (ECMS) [15] is a popular instantaneous optimization-based method.…”

Section: Introductionmentioning

confidence: 99%

“…This scenario is used to predict the probability of reaching a node. Each node of the scenario represents a predicted state which is the weight factor in the optimization problem (14). This scenario is generally described in terms of trees, as shown in Figure 6, and this description method requires the introduction of the following variables and terms:…”

mentioning

confidence: 99%

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Zhong

Jiao

2020

Applied Sciences

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Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.

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“…Although they suggested an alternative simulation method for the optimization of excavation work, they failed to provide details of the methodology utilized or the results of field tests of unmanned excavators. Zhou et al [29] implemented a linkage algorithm from the boom cylinder to the bucket cylinder to optimize excavator driving and save energy; they also proposed a new prediction-based stochastic dynamic programming control methodology. Although this provides useful information for optimizing the excavator control process, it is of only limited utility when it comes to unmanned excavator operations.…”

Section: Introductionmentioning

confidence: 99%

Development of Unmanned Excavator Vehicle System for Performing Dangerous Construction Work

Lee

Kim

Sun

et al. 2019

Sensors

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Of all the machinery and equipment used on construction sites, excavators are responsible for the greatest number of fatal accidents. Excavation is an inherently risky process due to imprecise work processes and the unstable external environment on most work sites. The resulting accidents cause serious injuries that threaten the lives of not only the excavator pilots but also those working around them. In this study, we propose a mechanical device that is capable of operating the excavator remotely from a nearby safe location such as a site office. To ensure worker safety and at the same time boost the productivity of excavation operations, data from 3D scanners, cameras, and sensors were combined to create a detailed 3D picture of the area surrounding the excavation site to guide a remotely operated excavating system. Rather than modifying the excavator itself, a removable manipulator was mounted on the joystick in the excavator’s cockpit. Our proposed system was tested on a standard commercial excavator to verify its performance for a real-world excavation task.

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Prediction-based stochastic dynamic programming control for excavator

Cited by 21 publications

References 17 publications

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Development of Unmanned Excavator Vehicle System for Performing Dangerous Construction Work

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