Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.
The article introduces in brief that with improvement of system's complexity, the traditional control gradually can't satisfy the requirement of control in the temperature control system. The requirement of control can be completed by application of fuzzy control and the prospect of fuzzy control in systems also brought up.controlled object, enables the controlled process to achieve the anticipated effect.
Urban rail trains have undergone rapid development in recent years due to their punctuality, high capacity and energy efficiency. Urban trains require frequent start/stop operations and are, therefore, prone to high energy losses. As trains have high inertia, the energy that can be recovered from braking comes in short bursts of high power. To effectively recover such braking energy, an onboard supercapacitor system based on a radial basis function neural networkbased sliding mode control system is proposed, which provides robust adaptive performance. The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to provide traction energy or absorb regenerative braking energy. In the Boost and Buck modes, the state-space averaging method is used to establish a model and perform exact linearization. An adaptive sliding mode controller is designed, and simulation results show that it can effectively solve the problems of low energy utilization and large voltage fluctuations in urban rail electricity grids, and maximise the recovery and utilization of regenerative braking energy.
The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.