Human-simulated Intelligent Control Research Articles

Human-Simulated Intelligent Walking Control for Biped Robots

Human-Simulated Intelligent Walking Control for Biped Robots

Vibration control of MR whole-spacecraft under medium–high frequency and small amplitude considering actuator time-delay

The satellite bears complex vibration loads transmitted from the satellite-rocket interface during launch, where medium–high frequency and small amplitude vibration is highly likely to harm the satellite’s internal precision instruments. Under such special conditions, the time-delay factor of the MR damper as the actuator cannot be ignored in the vibration control of the magnetorheological (MR) whole-spacecraft. To compensate actuator time-delay, and consider the system’s nonlinear characteristics under medium–high frequency and small amplitude conditions, a gray prediction time-delay compensation method is proposed based on the human-simulated intelligent controller (HSIC). After verifying through simulation and experiment that actuator time-delay affects the vibration response of the MR whole-spacecraft, the HSIC based on gray prediction is designed. Then, the control effect of HSIC with or without time-delay is simulated numerically. The results reveal that after considering the time-delay factor, the resonance peak increases by 17%, and the transmissibility of the concerned particularly frequency band (40 Hz) increases by 62%, which implies a significant deterioration in the control effect. Following, to make up for the response deterioration caused by the time-delay, the HSIC based on gray prediction is used for numerical simulation. The results confirm that reasonable time-delay compensation can effectively improve the system dynamics. This study provides a reference for the practical application of the whole-spacecraft controller.

A human-simulated fuzzy membrane approach for the joint controller of walking biped robots

To guarantee their locomotion, biped robots need to walk stably. The latter is achieved by a high performance in joint control. This article addresses this issue by proposing a novel human-simulated fuzzy (HF) membrane control system of the joint angles. The proposed control system, human-simulated fuzzy membrane controller (HFMC), contains several key elements. The first is an HF algorithm based on human-simulated intelligent control (HSIC). This HF algorithm incorporates elements of both multi-mode proportional-derivative (PD) and fuzzy control, aiming at solving the chattering problem of multi-mode switching while improving control accuracy. The second is a membrane architecture that makes use of the natural parallelisation potential of membrane computing to improve the real-time performance of the controller. The proposed HFMC is utilised as the joint controller for a biped robot. Numerical tests in a simulation are carried out with the planar and slope walking of a five-link biped robot, and the effectiveness of the HFMC is verified by comparing and evaluating the results of the designed HFMC, HSIC and PD. Experimental results demonstrate that the proposed HFMC not only retains the advantages of traditional PD control but also improves control accuracy, real-time performance and stability.

Research on Intelligent Trajectory Control Method of Water Quality Testing Unmanned Surface Vessel

In this paper, we take a water quality testing Unmanned Surface Vessel (USV) as the research object. We propose a heading keeping strategy based on Human Simulated Intelligent Control (HSIC) algorithm and a trajectory tracking strategy under line-of-sight (LOS) algorithm. The practicality of the proposed control strategies was verified by combining simulations and experiments. The main contents were constructed with three parts: Firstly, we designed a complete control system of a water quality inspection unmanned boat with Arduino microcontroller as the core processor. Secondly, we derived the mathematical model of motion after reasonable simplification. Combined with the cycle experiment, the mapping relation between virtual rudder angle and motor speed was established. Then, the USV heading direction control strategy of HSIC was presented and the reliability of the proposed strategy was verified by the course control experiment of USV. Finally, aiming at the defects and shortcomings of the upper-level trajectory tracking LOS algorithm in practical application, we propose the trajectory correction and precise steering control strategies, and the practicality of the improved algorithm was verified by multi-point trajectory tracking experiments. The autonomous fixed-point water quality testing experiment was designed and verified the effectiveness of the proposed strategies.

A New Lane Keeping Method Based on Human-Simulated Intelligent Control

In this paper, a novel lane keeping control method for automated vehicles based on human-simulated intelligent control (HSIC) is proposed, which is inspired by human expert drivers’ steering characteristics including good foresight, precise execution and notable intermittency. The novelty of the paper is to introduce the HSIC concept into lateral control of vehicles, which is a multi-mode control scheme implemented by the combination of the feedforward control for curve tracking and act-and-wait control for intermittent error correction. Theoretically, the stabilization problem of the HSIC method is investigated based on the switched system related method. Experiments on the joint simulation platform of PreScan and CarSim show that the newly presented HSIC scheme has better matching performance to the expert driver and good robustness. For automated lane keeping systems, the HSIC method could provide human-like qualities, which may be one of the essential points to determine whether the driver is comfortable or not when the driver hands over the steering authority, improve the transition smoothness in the scenario of human vehicle co-piloting, and eliminate the potential conflicts between manual driving and automated driving vehicles in the future mixed traffic flow.

Controllability analysis and intelligent control of magnetorheological whole-satellite under small amplitude and medium-high frequency vibration

The satellite carried by the launch vehicle is subject to complex loads in the launch process, which can easily lead to the failure of the satellite launching. To improve the response of an existing magnetorheological (MR) whole-satellite system under small amplitude and medium-high frequency vibration during the launch phase, the MR damper is redesigned, and the controllability of the improved system is analyzed, and then a human-simulated intelligent controller (HSIC) is designed. After analyzing the over-damping problem of the existing MR whole-satellite system through sinusoidal sweep and impact simulation tests, the MR damper is redesigned and tested, then the controllability of the improved system is analyzed using vibration theory. Based on the theory of the HSIC, the feature model, control rules, and control modes of the intelligent controller are designed, and the controller parameters are optimized by genetic algorithm. The system simulation model based on HSIC is built to simulate the vibration control of the system. The simulation results show that compared with the skyhook control, the HSIC control method can not only effectively reduce the satellite resonance peak, but also has an obvious vibration reduction at a specific frequency band (40 Hz), which verifies the effectiveness of the algorithm.

Intelligent Optimization Control Strategy for Secondary Pollution of Flue Gas in Municipal Solid Waste Incineration

Aiming at the problem of serious secondary pollution caused by improper control strategy in the waste incineration process, this paper proposes an intelligent optimization control strategy for secondary pollution of flue gas in municipal solid waste incineration. Firstly, the control difficulties of waste incineration and cybernetic characteristics of combustion process in incinerators are analyzed, and the basic algorithm based on human simulated intelligent control is proposed accordingly. Then, genetic algorithm is used to improve particle swarm algorithm and search ability in wide space, which can avoid the premature phenomenon of local optimum. Finally, improved particle swarm algorithm is utilized to optimize the parameters in human simulated intelligent control algorithm to realize intelligent optimization control in the waste incineration process. Based on MATLAB simulation platform, experimental results show that when delay parameters and time constant change significantly, there are large disturbances, and the process response of proposed strategy is fast and stable, which is superior to other comparison strategies.

Human–machine cooperative scheme for car-following control of the connected and automated vehicles

To address the out-of-the-loop problem of the automated driving, a human–machine cooperative scheme for car-following control of the connected and automated vehicles (CAVs) is proposed. The proposed scheme can keep the drivers always in the loop and improve the car-following performance. To be specific, the driving automation system (artificial driver) is assigned to the task of velocity tracking and the human driver is responsible for headway adjustment. For the velocity tracking task, a feedforward–feedback control strategy was designed firstly by considering the advantages of the accurate perception and communication of CAVs, then an H∞ suboptimal control method was developed to optimize the controller parameters according to the desired performance index, further the controller was fine-tuned based on the idea of human-simulated intelligent control (HSIC) to improve the dynamic performance of the velocity tracking. For the operator’s headway adjusting task, the stability analysis based on the Lyapunov function proved that the simple proportional feedback control can be assumed by the driver to ensure the system stability under the cooperation of automated velocity tracking. The experiments based on the driving simulator demonstrated that human–machine cooperative scheme for car-following can reduce the tracking error of vehicle distance effectively, and the human driver can be kept in the control loop with a smaller operating load.

Improvement of Walking Performance of the Biped Robot Based on HSIC in Port Environment

Jing, C. and Zheng, J., 2020. Improvement of walking performance of the biped robot based on HSIC in port environment. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 793–797. Coconut Creek (Florida), ISSN 0749-0208.The generation of walking patterns based on predictive control realizes the control with the use of future information, which also conforms to the principle of human walking process in accordance with future road conditions. However, the target ZMP of the predictive controller is always off-line programmed in advance. Due to the model error and environmental disturbance, gait tracking error of the biped robot in walking process is inevitable. Considering that human-simulated intelligent control (HSIC) can well suppress error, this study designed a human-simulated predictive control (HSPC) system by combining HSIC and predictive control. The control variables can be switched based on ZMP error state so as to realize the control of dynamic system. Therefore, actual ZMP output can well track the expected value of variable ZMP to realize the stable walking of the biped robot, and also adapt to the complex environments such as port robot operation. Finally, the effectiveness of the designed HSPC was validated through simulation.

Magneto-Rheological Variable Stiffness and Damping Torsional Vibration Control of Powertrain System

A novel magneto-rheological variable stiffness and damping torsional vibration absorber (MR-VSDTVB) is proposed, fabricated and tested. According to the test data, the control model of MR-VSDTVB is established. Meanwhile, the analysis of the multi-degree-of-freedom model of the powertrain system provides the key frequency and the rotation speed of the torsional vibration control of the powertrain system and further determines the installation position and structural parameters of MR-VSDTVB. Besides, a human-simulated intelligent controller (HSIC) is developed and numerical simulation of the powertrain system with MR-VSDTVB is carried out. Ultimately, the result verifies the effectiveness of the powertrain system with MR-VSDTVB and the semi-active HSIC algorithm on the torsional vibration control.

Path Following of a Water-Jetted USV Based on Maneuverability Tests

Due to the high propulsive efficiency and better maneuverability under high speed, the water-jetted unmanned surface vehicle (USV) is widely studied and used. This paper presents complete maneuvering tests and control algorithm designed for a twin water-jetted USV model. Firstly, a wireless network control platform is established, and maneuvering tests, for instance, an inertia test, zig-zag test and turning test, are carried out to verify the maneuverability of the USV. In light of the complexity and uncertainty of ship sailing and ship handling, the Human Simulated Intelligent Control (HSIC) method is utilized to optimize the response time, accuracy and robustness of the controller. Finally, for the path following and track rectification part, a Line of Sight (LOS) algorithm is improved and proved practicable with triangle/square path tests. The proposed intelligent navigation algorithm specially designed for matching with the control methods, showing satisfactory improvements on the motion control and path following of the specific USV.

Research on Control of Intelligent Vehicle Human-Simulated Steering System Based on HSIC

The experienced drivers with good driving skills are used as objects of learning, and road steering test data of skilled drivers are collected in this article. First, a nonlinear fitting was made to the driving trajectory of skilled driver in order to achieve human-simulated control. The segmental polynomial expression was solved for two typical steering conditions of normal right-steering and U-turn, and the hp adaptive pseudo-spectral method was used to solve the connection problem of the vehicle segmental driving trajectory. Second, a new Electric Power Steering (EPS) system was proposed, and the intelligent vehicle human-simulated steering system control model based on human simulated intelligent control (HSIC) was established in Simulink/Carsim joint simulation environment to simulate and analyze. Finally, in order to further verify the effectiveness of the proposed algorithm in this article, an intelligent vehicle steering system test bench with a steering resistance torque simulation device was built, and the dSPACE rapid prototype controller was used to realize human-simulated intelligent control law. The results show that the human-simulated steering control algorithm is superior to the traditional proportion integration differentiation (PID) control in the tracking effect of the steering characteristic parameters and passenger comfort. The steering wheel angle and torque can better track the angle and torque variation curve of real vehicle steering experiment of the skilled driver, and the effectiveness of the intelligent vehicle human-simulated steering control algorithm based on HSIC proposed in this article is verified.

Intelligent Vehicle Human-Simulated Steering Characteristics Access and Control Strategy

As the traditional control algorithm is over-dependent on accurate vehicle model in intelligent vehicle steering control, a human-simulated intelligent control method is proposed based on experienced driver steering characteristics. Intelligent vehicle unmanned steering system dynamics model and the driver model are set up. Through experienced drivers’ trial run experiment, the analysis is mainly conducted on the double lanes condition. After the transformation of coordinates on global positioning system (GPS) derivative, the path information of local coordinates is accessed. The ideal driver steering path is obtained through fuzzy C-means clustering algorithm. The human-simulated intelligent controller is designed. Characteristic model is established according to the ideal and practical steering angle deviation and the deviation rate. Besides, the corresponding control rules and control modality set are designed. The joint simulation under CarSim joint/Simulink environment shows that the humanoid steering controller designed in this paper has better tracking performance than the model predictive control.

Human-Simulating Intelligent PID Control

Motivated by PID control simplicity, robustness and validity to deal with the nonlinearity and uncertainties of dynamics, through simulating the intelligent behavior of human manual control, and only using the elementary information on hand, this paper introduces a simple formulation to represent prior knowledge and experiences of human manual control, and proposes a simple and practicable control law, named Human-Simulating Intelligent PID control (HSI-PID), and the simple tuning rules with the explicit physical meaning. HSI-PID control can not only easily incorporate prior knowledge and experiences of experts control into the controller but also automatically acquire knowledge of control experiences from the past control behavior to correct the control action online. The theoretical analysis and simulation results show that: HSI-PID control has the better flexibility, stronger robustness, and especially the faster self-learning ability, and it can make the motion of system identically track the desired response, whether the controlled system has the strong nonlinearity and uncertainties of dynamics or not, even under the actions of uncertain, varying-time and strong disturbances.

Optimization Tuning Model of Control Parameter Based on Artificial Immune Principle in Human Simulated Intelligent Controller

Parameter tuning is the puzzle in control engineering. Aimed at being difficult to make artificially the tuning which results in too many control parameters of intelligent controller, the paper presented a sort of optimization method of control parameters based on immunological principle. In the paper, it made the anatomy of the complexity and existing puzzles of control parameter tuning, presented the mathematical model of controller parameter tuning based on immune principle, gave the optimizing control algorithm of Clone selection. Taking a two-order process with time lag as an example, the control parameters of HSIC algorithm have been optimized by presented tuning model, made the comparative study with PID algorithm parameter optimized by other method, and the process simulation response demonstrated that the optimization tuning method based on immune principle could obtain the control performance and control quality better. Experimental study of simulation shows that it is reasonable and effective to the optimization method of control parameters presented in this paper.

Study on Adaptive Strategy of Task and Parameter in Human Simulated Intelligent Control Algorithm

Study on Adaptive Strategy of Task and Parameter in Human Simulated Intelligent Control Algorithm

Optimizing Control Methods of Airport Pipeline Refueling Process

Due to the non-linearity and long delay of airport pipe line refueling system, it is difficult for classical control methods to ensure stable pumping pressure and refueling flow. To improve the stability of the refueling process, the paper introduces human-simulated intelligent control (HSIC) method to the recognization of refueling status, online self-adjusting and control of refueling parameters. Simulation shows that the stability and accuracy of HSIC control methods were better than that of classical PID and fuzzy PID control.

Human Simulated Intelligent Control with Double-Direction Dead-Zone Compensation for Joint Motion Control of a Large-Sized Boom System

Joint motion control of a 52-meter-long five-boom system driven by proportional hydraulic system is developed. It has been considered difficult due to strong nonlinearities and parametric uncertainties, the effect of which increases with the size of booms. A human simulated intelligent control scheme is developed to improve control performance by modifying control mode and control parameters. In addition, considering the negative effects caused by frequent and redundant reverse actions of the proportional valve, a double-direction compensation scheme is proposed to deal with the dead-zone nonlinearity of proportional valve. Sinusoidal motions are implemented on a real boom system. The results indicate that HSIC controller can improve control accuracy, and dead-zone nonlinearity is effectively compensated by proposed compensation scheme without introducing frequent reverse actions of proportional valve.

Application of Fuzzy Control Simulation Human Intelligence Controller in Ferment Process of Supplying Suger

In accordance with the features of non-linear, large time-delay and time varying for ferment process, an algorithm combining Fuzzy Control and Human-simulation Intelligent control is presented: Fuzzy control and human-simulating control algorithm is used to control the ferment process of supplying suger. The simulation research shows the algorithm is a very effective algorithm with very good anti-delay and anti-disturbance, especially suitable for the objects with large time-delay and strong disturbance.

Research of Vehicle Trajectory Tracking Based on Human-Simulated Intelligent Control

As unmanned ground vehicle is a nonlinear time-varying system, it is difficult to describe the accurate mathematical model. To solve this problem, a control algorithm of vehicle trajectory tracking based Human-Simulated Intelligent Control (HSIC) is proposed, and feature model set, control mode set and reasoning mode set are designed. Finally, this method and traditional preview follower PID algorithm are compared with each other in experimental way. The results show that such method has small dependence on the model with better control effect and overall stability.