In recent decades, the Timed Elastic Band (TEB) algorithm is widely used for the AGV local path panning because of its convenient and efficiency. However, it may make a local detour when encountering a curve turn and cause excessive energy consumption. To solve this problem, this paper proposed an improved TEB algorithm to make the AGV walk along the wall when turning, which shortens the planning time and saves energy. Experiments were implemented in the Rviz visualization tool platform of the robot operating system (ROS). Simulated experiment results reflect that an amount of 5% reduction in the planning time has been achieved and the velocity curve implies that the operation was relatively smooth. Practical experiment results demonstrate the effectiveness and feasibility of the proposed method that the robots can avoid obstacles smoothly in the unknown static and dynamic obstacle environment.
Noise, as undesired sound, severely affects the quality of human life. Currently, active noise control method has demonstrated its capability in low-frequency noise cancellation and the advance in saving money and reducing weight and volume of related materials used in the passive noise control technology. The widespread configuration for active noise control technology is finite impulse response filter with filtered-x least mean squares (FxLMS) algorithm. However, the nonlinearities in the secondary path, which mainly arise from sensors, actuators and amplifiers used in the active noise control system, will cause instability and degrade the performance while using the FxLMS algorithm. In order to cope with this challenge, many new approaches have been proposed and fuzzy logic control is one of these. In this paper, a Takagi–Sugeon–Kang-type fuzzy logic control-based feedforward active noise control system with focus on the geometry configuration is introduced. In contrast to previous work, all physical paths are modelled by pure time delay transfer function and the acoustic feedback is added as part of inputs for the fuzzy logic control. Computational experiments are implemented within the Matlab/Simulink platform, and several case studies are presented with time and frequency domain analyses to demonstrate the cancellation ability of the proposed feedforward active noise control system and investigate the influence of distance ratio on the overall noise cancellation performance.
In this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.
In this article, a switched quantizer is proposed to solve the bandwidth limitation application problem for distributed wireless sensor networks (WSNs). The proposed estimator based on switched quantitative event-triggered Kalman consensus filtering (KCF) algorithm is used to monitor the aircraft cabin environmental parameters when suffering packet loss and path loss issues during the communication process for WSN. The quantization error of the novel switched quantizer structure is bounded, and the corresponding stability theory for the quantitative estimation approach is proved. Compared with other methods, the simulation results for the introduced method verify that the environmental parameters can be estimated accurately and timely and reduce the burden of network communication bandwidth.
This paper presents a physical configuration-based feedforward active noise control scheme with an adaptive secondorder truncated Volterra filter for point source cancellation in three-dimensional free-field acoustic environment. The inertial particle swarm optimization (PSO) algorithm is used as the parameter adjustment mechanism for tuning the coefficients of the adaptive Volterra filter. The first motivation of this paper is to provide a precise description of the relationship between the degree of cancellation and the physical distances between system components. The second motivation is to improve the cancellation performance in the presence of nonlinearities with the adaptive Volterra filter in light of the characteristics of sensing microphone and actuating loudspeaker. The reason for choosing the inertial PSO algorithm is that it can avoid the trap of local optima. The work thus presented makes two main contributions. The first is using the degree of cancellation as a function of the physical distances between system components to provide a quantitative analysis of system performance. The second is the application of the adaptive Volterra filter, which achieves improvements in the cancellation performance of the system under different physical configurations with a reasonable compromise with complexity. For consistency with the numerical analysis, several simulation experiments are conducted using MATLAB/Simulink.
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