Binrui Wang, Jiqing Huang, Guoyang Shen and Dijian Chen
Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make…
Abstract
Purpose
Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make the robot arm shake hands compliantly with people; the paper proposed two closed-loop-compliant control schemes for the dynamic identification of cascade elbow joint.
Design/methodology/approach
The active compliance control strategy consists of inner and outer loops. The inner loop is the position control using sliding mode control with disturbance observer (SMCDO), in which a new saturation function is designed to replace the traditional signal function of sliding mode control (SMC) law so as to mitigate chatter. The outer loop is the admittance control to regulate the dynamic behaviours of the elbow joint, i.e. its impedance. The simulation is carried out to verify the performance of the proposed control scheme.
Findings
The results show that the chatter of traditional SMC can be effectively eliminated by using SMCDO with this saturation function. In addition, for the handshake task, the value of threshold force and elbow joint compliance is defined. Then, the threshold force tests, impact tests and elbow-joint compliance tests are carried out. The results show that, in the impedance model, the elbow joint compliance only depends on the stiffness parameters, not on the position control loop.
Practical implications
The effectiveness of the admittance control based on SMCDO can improve the adaptability of industrial manipulator in different working environments to some degree.
Originality/value
The admittance control with SMCDO completed trajectory tracking has higher accuracy than that based on SMC.
Details
Keywords
Jian Di, Yu Kang, Haibo Ji, Xinghu Wang, Shaofeng Chen, Fei Liao and Kun Li
A low-level controller is critical to the overall performance of multirotor unmanned aerial vehicles. The purpose of this paper is to propose a nonlinear low-level angular…
Abstract
Purpose
A low-level controller is critical to the overall performance of multirotor unmanned aerial vehicles. The purpose of this paper is to propose a nonlinear low-level angular velocity controller for multirotor unmanned aerial vehicles in various operating conditions (e.g. different speed and different mode).
Design/methodology/approach
To tackle the above challenge, the authors have designed a nonlinear low-level controller taking the actuator dynamics into account. The authors first build the actuator subsystem by combining the actuator dynamics with the angular velocity dynamics model. Then, a recursive low-level controller is developed by designing a high-gain observer to estimate unmeasurable states. Furthermore, a detailed stability analysis is given with the Lyapunov theory.
Findings
Simulation tests and real-world flying experiments are provided to validate the proposed approach. In particular, we illustrate the performance of the proposed controller using violent random command test, attitude mode flight and high-speed flight of up to 18.7 m/s in real world. Compared with the classical method used in PX4 autopilot and the estimation-based incremental nonlinear dynamic inversion method, experimental results show that the proposed method can further reduce the control error.
Research limitations/implications
Low-level control of multirotor UAVs is challenging due to the complex dynamic characteristics of UAVs and the diversity of tasks. Although some progress has been made, the performance of existing methods will deteriorate as operating conditions change due to the disregard for the electromechanical characteristics of the actuator.
Originality/value
To solve the low-level angular velocity control problem in various operating conditions of multirotor UAVs, this paper proposes a nonlinear low-level angular velocity controller which takes the actuator dynamics into account.
Details
Keywords
Kaizheng Zhang, Jian Di, Jiulong Wang, Xinghu Wang and Haibo Ji
Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual…
Abstract
Purpose
Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.
Design/methodology/approach
The authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.
Findings
The proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.
Originality/value
Infeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.
Details
Keywords
Amir Emami, Zeinab Taheri and Rasim Zuferi
This paper aims to investigate the interactive relationship between learning styles and cognitive biases as two essential factors affecting information processing in online…
Abstract
Purpose
This paper aims to investigate the interactive relationship between learning styles and cognitive biases as two essential factors affecting information processing in online purchases.
Design/methodology/approach
This research is applied in nature but extends the knowledge in the area of consumer behavior. By using the correlational research method, the present study uncovers the relationship between various sorts of decision biases and learning styles among online buyers.
Findings
According to the results, the most affected learning style among all is reflective observation. Several biases influence people with this learning style, namely, risky framing, attribute framing and aggregated/segregated framing. In the case of active experimentation, online customers can undo its effect. Therefore, online sellers should be aware of their target customers with such a learning style. In addition, online purchasers with the reflective observation learning style are more prone to aggregation and segregation of sales information.
Originality/value
The findings enhance the understanding of consumer buying behavior and the extent to which learning styles impact cognitive biases and framing effects in online shopping.