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Adaptive neural tracking control for upper limb rehabilitation robot with output constraints

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IET Cyber-Systems and Robotics 2023年第4期5卷 49-62页

作者： Zibin Zhang Pengbo Cui Aimin An college of electrical and information engineering Lanzhou University of TechnologyLanzhouGansuChina

The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot,an adaptive neural full-state feedback control is proposed.The neural network is utilised to approximate the dy-namics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient.By incorporating a high-gain observer,unmeasurable state information is integrated into the output feedback control.Taking into consider-ation the issue of joint position constraints during the actual rehabilitation training process,an adaptive neural full-state and output feedback control scheme with output constraint is further designed.From the perspective of safety in human–robot interaction during rehabilitation training,log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region.The stability of the closed-loop system is proved by Lyapunov stability theory.The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.

关键词： adaptive control full-state and output feedback control output constraints upper limb rehabilitation robot

Calculation Method for Commutation Failure Fault Level in LCC-HVDC System Under Single-line-to-ground Faults Considering DC Current Variation

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1995-2002页

作者： Renlong Zhu Xiaoping Zhou Hanhang Yin Lerong Hong An Luo Yandong Chen Hanting Peng college of electrical and information engineering Hunan UniversityChangsha 410082China School of electrical and information engineering Changsha University of Science and TechnologyChangsha 410114China

Earlier studies have reported some calculation methods for commutation failure fault level(CFFL) in line-commutated-converter based high-voltage direct current(LCCHVDC) system under single-line-to-ground(SLG) faults. The accuracy of earlier methods is limited because they only consider the commutating voltage drop and phase shift, while neglecting the DC current variation. Hence, this paper proposes a CFFL calculation method under SLG faults considering DC current variation, for better planning and designing of LCC-HVDC systems. First, the fault commutating voltage magnitude and phase shift are calculated. Then, the fault DC voltage during different commutation processes is deduced. Based on the commutating voltage magnitude and phase shift, and DC voltage during different commutation processes under SLG faults, the characteristics of CFFL with different fault time are demonstrated and analyzed. Next, the transient time-domain response of the DC current after the fault is obtained based on the DC transmission line model. Discrete commutation processes are constructed based on the commutation voltage-time area rule to solve the extinction angle under different fault levels and fault time. Finally, the CFFL is calculated considering the fault time, commutating voltage drop, phase shift, and DC current variation. The accuracy of the proposed method compared with the traditional method is validated based on the CIGRE benchmark model in PSCAD/EMTDC.

关键词： Commutation failure(CF) commutation failure fault level(CFFL) line-commutated-converter-based highvoltage direct current(LCC-HVDC) single-line-to-ground(SLG)fault

Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and engineering Optimization

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International Journal of Automation and computing 2018年第1期15卷 103-114页

作者： Guo-Han Lin Jing Zhang Zhao-Hua Liu college of electrical and information engineering Hunan University college of electrical and information Hunan Institute of Engineering School of information and electrical engineering Hunan University of Science and Technology

In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.

关键词： Particle swarm optimization （PSO） active disturbance rejection control （ADRC） differential evolution algorithm chaoticmap parameter tuning.

Ant Colony Algorithm for Path Planning Based on Grid Feature Point Extraction

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 86-99页

作者：李二超齐款款 college of electrical and information engineering Lanzhou University of TechnologyLanzhou 730050China

Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony algorithm for use in a new environment is proposed.First,the feature points of an obstacle are extracted to preprocess the grid map environment,which can avoid entering a trap and solve the deadlock problem.Second,these feature points are used as pathfinding access nodes to reduce the node access,with more moving directions to be selected,and the locations of the feature points to be selected determine the range of the pathfinding field of view.Then,based on the feature points,an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution,an improved heuristic function is used to enhance the guiding role of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the algorithm.Third,a Bezier curve is used to smooth the shortest path obtained.Finally,using grid maps with a different complexity and different scales,a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.

关键词： ant colony algorithm mobile robot path planning feature points Bezier curve grid map

Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data

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International Journal of Automation and computing 2018年第3期15卷 335-344页

作者： Shao-Xue Jing Tian-Hong Pan Zheng-Ming Li School of electrical information and engineering Jiangsu University Department of electrical engineering Huaian College of Information and Technology

To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.

关键词： Parameter estimation discrete time systems Gaussian noise Bayesian algorithm covariance resetting.

Dynamic Balance Control Based on an Adaptive Gain-scheduled Backstepping Scheme for Power-line Inspection Robots

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 198-208页

作者： Songyi Dian Lin Chen Son Hoang Ming Pu Junyong Liu the School of electrical engineering and information Sichuan University the college of Control engineering Chengdu University of Information Technology

This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.

关键词： Adaptive backstepping balance control gain-scheduled inspection robot power line

Generalized load graphical forecasting method based on modal decomposition

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Global Energy Interconnection 2024年第2期7卷 166-178页

作者： Lizhen Wu Peixin Chang Wei Chen Tingting Pei college of electrical and information engineering Lanzhou University of TechnologyLanzhou 730050P.R.China School of electrical Data engineering University of Technology SydneyNSW2007Australia

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

关键词： Load forecasting Generalized load Image processing DenseNet Modal decomposition

Starting Control of Free Piston Stirling Linear Generator System Based on FOC

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CES Transactions on electrical Machines and Systems 2022年第2期6卷 195-200页

作者： Qiaoling Yang Kechun Zhang Shenghui Guo Boliang Song Xiaoyu Zhang college of electrical engineering and information engineering Lanzhou University of TechnologyLanzhou730050China

Aiming at the problem of poor system dynamic performance caused by low parameter matching in the coordinated control of Stirling engine and linear generator in the starting stage control of free piston Stirling linear generator system,a joint control method of free piston Stirling permanent magnet synchronous linear generator system based on field orientation control is proposed,based on the theoretical derivation of the mathematical model of the system and the principle of controller parameters setting,the simulation experiments of the system starting stage under several Stirling engine working conditions are carried out under simulation.The experimental results show that the stability and rapidity of the system are improved,and the dynamic response speed of generator parameters under different working conditions is accelerated,what fully verifies the correctness and effectiveness of the method.It provides an effective way to improve the control performance of the system and stabilize the power generation operation.

关键词： Parameter setting Field orientation control Double closed loop Permanent magnet synchronous linear generator

Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control

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Frontiers of information technology & Electronic engineering 2024年第2期25卷 260-271页

作者： Mingguang ZHANG Feng LI Yang YU Qingfeng CAO School of electrical&information engineering Jiangsu University of TechnologyChangzhou213001China college of electrical Energy and Power EngineeringYangzhou UniversityYangzhou225127China

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches.The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network(NFN)and a linear state`-space model.The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals,which consist of step signals and random signals.First,based on the characteristic that step signals do not excite static nonlinear systems,that is,the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input,the unknown intermediate variables can be replaced by inputs,solving the problem of unmeasurable intermediate variable information.In the presence of step signals,the parameters of the state-space model are estimated using the recursive extended least squares(RELS)algorithm.Moreover,to effectively deal with the interference of measurement noises,a data filtering technique is introduced,and the filtering-based RELS is formulated for estimating the NFN by employing random signals.Finally,according to the structure of the Hammerstein system,the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system,and it can then be easily controlled by applying a linear controller.The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

关键词： Hammerstein nonlinear systems Neural fuzzy network Data filtering Hybrid signals Industrial control