In this paper, we propose an electronic compensation control scheme for pump-valve coordinated (PVC) systems to replace post-valve hydro-mechanical compensation in load-sensitive (LS) systems of truck cranes. This approach aims to address the issues of high energy consumption and low tracking accuracy associated with pressure compensators. A simplified interval type-2 fuzzy proportional-integral-derivative feedback (SIT2FPIDFb) controller is developed to determine the main valve opening based on flow parameters, reference signal, and actuator status. An innovative type-reduction algorithm, utilizing iterative computation, is introduced to enhance the solving process. Considering load variation disturbances in truck crane luffing systems, a type-1 fuzzy feedforward (T1FFf) controller collaborates with the primary controller to effectively resist the impact of disturbances. Experiments conducted on truck cranes demonstrate the feasibility and effectiveness of the proposed control strategy.
LiZLinYChenQ, et al. Control strategy of speed segmented variable constant power powertrain of electric construction machinery. Appl Sci2022; 12(19): 9734.
2.
LinTLinYRenH, et al. A double variable control load sensing system for electric hydraulic excavator. Energy2021; 223: 119999.
3.
LisowskiEFiloGRajdaJ.Analysis of the energy efficiency improvement in a load-sensing hydraulic system built on the ISO plate. Energies2021; 14(20): 6735.
4.
WuDMaZZhangJ, et al. Simulation analysis of working circuit performance of mountain Pepper Harvester based on improved load-sensitive system. Appl Sci2023; 13(18): 10008.
5.
DuWLuoYLuoY, et al. A simulation study of an electro-hydraulic load-sensitive variable pressure margin diverter synchronous drive system with time-varying load resistance. Processes2024; 12(1): 170.
6.
YuPSunZXuH, et al. Design and analysis of Brake-by-Wire unit based on direct drive pump–valve cooperative. Actuators2023; 12(9): 360.
7.
BaKWangYHeX, et al. Force compensation control for electro-hydraulic servo system with pump–valve compound drive via QFT–DTOC. Chin J Mech Eng2024; 37(1): 27.
8.
WangHZhangYLiuR.Energy-saving cruise control method for heavy-duty transport vehicle based on pump-valve coordinated drive. Proc IMechE, Part D: J Automobile Engineering2024; in press. DOI: 10.1177/09544070241283119
9.
HanXChenJPanS, et al. Pump-valve coordinated control of robotic arm driven by electro-hydraulic system. In 2024 7th international conference on mechatronics, robotics and automation (ICMRA), 2024, pp.77–83. Wuhan, China: IEEE. DOI: 10.1109/ICMRA62519.2024.10809059.
10.
SunNYangTFangY, et al. Transportation control of double-pendulum cranes with a nonlinear Quasi-PID scheme: design and experiments. IEEE Trans Syst Man Cybern Syst2019; 49(7): 1408–1418.
11.
LiZLiS.Saturated PI control for nonlinear system with Provable Convergence: an optimization perspective. IEEE Trans. Circuits Syst. II2021; 68(2): 742–746.
12.
HeSFangHZhangM, et al. Adaptive optimal control for a class of nonlinear systems: the online policy iteration approach. IEEE Trans Neural Netw Learn Syst2020; 31(2): 549–558.
13.
LyuXLinZ.PID control of planar nonlinear uncertain systems in the presence of actuator saturation. IEEE/CAA J. Autom. Sinica2022; 9(1): 90–98.
14.
LiuJHeJIuHH-C. Realization of low-voltage and high-current rectifier module control system based on nonlinear feed-forward PID control. Electronics2021; 10(17): 2138.
15.
SałabunWWięckowskiJShekhovtsovA, et al. How to apply Fuzzy MISO PID in the industry? An empirical study case on simulation of crane relocating containers. Electronics2020; 9(12): 2017.
16.
HaoWKanJ.Application of self-tuning fuzzy proportional–integral–derivative control in hydraulic crane control system. Adv Mech Eng2016; 8(6): 168781401665525.
17.
WuT-SKarkoubMWangH, et al. Robust tracking control of MIMO underactuated nonlinear systems with Dead-zone band and delayed uncertainty using an adaptive fuzzy control. IEEE Trans Fuzzy Syst2017; 25(4): 905–918.
18.
FanZLiLLiaoK, et al. Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator. Mech Ind2023; 24: 30.
19.
HanJWangFSunC.Trajectory tracking control of a manipulator based on an adaptive neuro-fuzzy inference system. Appl Sci2023; 13(2): 1046.
20.
WuWTongS.Robust adaptive fuzzy control for non-strict feedback switched nonlinear systems with unmodeled dynamics. Int J Syst Sci2021; 52(2): 307–320.
21.
ZhangJJiangWGeSS.Adaptive fuzzy control for uncertain strict-feedback nonlinear systems with full-state constraints using disturbance observer. IEEE Trans Syst Man Cybern Syst2022; 52(1): 6145–6213.
22.
LiYMinXTongS.Observer-based fuzzy adaptive inverse optimal output feedback control for uncertain nonlinear systems. IEEE Trans Fuzzy Syst2021; 29(6): 1484–1495.
23.
WangHLuJ.Research on fractional order fuzzy PID control of the pneumatic-hydraulic upper limb rehabilitation training system based on PSO. Int J Control Autom Syst2022; 20(1): 310–320.
24.
ZhangKLiuYJiaH, et al. Research on a three-dimensional fuzzy active disturbance rejection controller for the mechanical arm of an iron Roughneck. Processes2023; 11(5): 1409.
25.
Haj-AliAYingH.Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains. Automatica2004; 40(9): 1551–1559.
26.
ZhouESunQZhouE.New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive. IEEE Trans Ind Electron2006; 53(3): 814–821.
27.
PrabhakarGSelvaperumalSNedumal PugazhenthiP.Fuzzy PD plus I control-based adaptive cruise control system in simulation and real-time environment. IETE J Res2019; 65(1): 69–79.
28.
El-NagarAMEl-BardiniM.Parallel realization for self-tuning interval type-2 fuzzy controller. Eng Appl Artif Intell2017; 61: 8–20.
29.
DongJDuanX.Robust control based on fast terminal sliding mode control with adaptive interval type-2 fuzzy PID. Int J Fuzzy Syst2024; 26(3): 849–859.
30.
YaoXSunZSuX.Disturbance-observer-based hierarchical control for vehicle front steering: interval type-2 fuzzy system approach. IEEE Trans Fuzzy Syst2024; 32(1): 137–151.
31.
WangWZhangJJiaoR.Optimized fractional-order type-2 fuzzy PID attitude controller for fixed-wing aircraft. J Optim Theory Appl2024; 203(3): 2592–2616.
32.
El-BardiniMEl-NagarAM.Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system. ISA Trans2014; 53(3): 732–743.
33.
ShiJ.A unified general type-2 fuzzy PID controller and its comparative with type-1 and interval type-2 fuzzy PID controller. Asian J Control2022; 24(4): 1808–1824.
34.
ShiJSongY.Mathematical analysis of a simplified general type-2 fuzzy PID controller. Math Biosci Eng2020; 17(6): 7994–8036.
35.
ChenCWuDGaribaldiJM, et al. A comment on “A direct approach for determining the switch points in the Karnik–Mendel algorithm”. IEEE Trans Fuzzy Syst2021; 29(6): 3905–1566.
36.
DodurkaMFKumbasarTSakalliA, et al. Boundary function based Karnik-Mendel type reduction method for interval type-2 fuzzy PID controllers. In: 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE), July 2014, pp. 619–625. Beijing, China: IEEE. DOI: 10.1109/FUZZ-IEEE.2014.6891832
37.
ChenY.Study on centroid type-reduction of general type-2 fuzzy logic systems with sensible beginning weighted enhanced karnik–Mendel algorithms. Soft Comput2023; 27(14): 9261–9279.
38.
Al JanaidehMRakotondrabeM. Precision motion control of a piezoelectric cantilever positioning system with rate-dependent hysteresis nonlinearities. Nonlinear Dyn2021; 104(4): 3385–3405.
39.
ZhouZGuoR.A disturbance-observer-based feedforward-feedback control strategy for driveline launch oscillation of hybrid electric vehicles considering nonlinear backlash. IEEE Trans Vehicular Technol2022; 71(4): 3727–3736.
40.
FanYTanU-X.Design of a feedforward-feedback controller for a piezoelectric-driven mechanism to achieve high-frequency nonperiodic motion tracking. IEEE/ASME Trans Mechatron2019; 24(2): 853–862.
41.
MendelJMMendelJM.Connect Karnik-Mendel algorithms to root-finding for computing the centroid of an interval type-2 fuzzy set. IEEE Trans Fuzzy Syst2011; 19(4): 652–665.
