Parameter Adaptive Sliding Mode Trajectory Tracking Strategy with Initial Value Identification for the Swing in a Hydraulic Construction Robot

A novel trajectory tracking strategy is developed for a double actuated swing in a hydraulic construction robot. Specifically, a nonlinear hydraulic dynamics model of a double actuated swing is established, and a robust adaptive control strategy is designed to enhance the trajectory tracking performance. When an object is grabbed and unloaded, the inertia of a swing considerably changes, and the performance of the estimation algorithm is generally inadequate. Thus, it is necessary to establish an algorithm to identify the initial value of the moment of inertia of the object. To this end, this paper proposes a novel initial value identification algorithm based on a two-DOF robot gravity force identification method combined with computer vision information. The performance of the identification algorithm is enhanced. Simulations and experiments are performed to verify the effect of the novel control scheme.

Novel adaptive backstepping control for uncertain manipulator robots using state and output feedback

In this paper, a new adaptive control strategy, based on the Modified Function Approximation Technique, is proposed for a manipulator robot with unknown dynamics. This novel strategy benefits from the backstepping control approach and the use of state and output feedback. Unlike the conventional Function Approximation Technique approach, the use of basis functions to approximate the dynamic parameters is completely eliminated in the proposed scheme. Another improvement is eliminating the need to measure velocity by means of integrating a high-order sliding mode observer. Furthermore, utilizing the Lyapunov function theory, it is demonstrated that all controller signals are uniformly ultimately bounded in the closed-loop form. Lastly, simulation and comparative studies are carried out to validate the effectiveness of the proposed control approach.

A Renewable Energy Community of DC Nanogrids for Providing Balancing Services

The massive expansion of Distributed Energy Resources and schedulable loads have forced a variation of generation, transmission, and final usage of electricity towards the paradigm of Smart Communities microgrids and of Renewable Energy Communities. In the paper, the use of multiple DC microgrids for residential applications, i.e., the nanogrids, in order to compose and create a renewable energy community, is hypothesized. The DC Bus Signaling distributed control strategy for the power management of each individual nanogrid is applied to satisfy the power flow requests sent from an aggregator. It is important to underline that this is an adaptive control strategy, i.e., it is used when the nanogrid provides a service to the aggregator and when not. In addition, the value of the DC bus voltage of each nanogrid is communicated to the aggregator. In this way, the aggregator is aware of the regulation capacity that each nanogrid can provide and which flexible resources are used to provide this capacity. The effectiveness of the proposed control strategy is demonstrated via numerical experiments. The energy community considered in the paper consists of five nanogrids, interfaced to a common ML-LV substation. The nanogrids, equipped with a photovoltaic plant and a set of lithium-ion batteries, participate in the balancing service depending on its local generation and storage capacity.

VSG damping adaptive adjustment based on BP neural network

The moment of inertia and damping of virtual synchronous generator (VSG) can be adjusted flexibly, which also has a significant impact on the transient performance of VSG. Constant damping or moment of inertia can not reduce frequency overshoot and fast response performance, so it is necessary to introduce adaptive damping control. Based on universal approximation theorem, BP neural network can fit continuous nonlinear function well. At the same time, it has the advantages of simple algorithm, powerful learning ability and fast learning speed. Based on the characteristics of the control object, the BP neural network is improved and a new adaptive control strategy is designed. The strategy uses improved BP neural network to adjust VSG virtual damping D online. Python-MATLAB-Simulink was used for co-simulation, BP neural network algorithm was integrated into the control object to establish an adaptive simulation model, and the proposed control strategy was simulated and verified. Simulation results show that the adaptive control strategy can eliminate overshoot and respond quickly when the frequency and active power of virtual synchronous generator change.

Research on Frequency Characteristics of VSG Virtual Parameter Adaptive Control Strategy Based on Fuzzy Control Theory

Traditional virtual synchronous generators (VSG) control inverters. Inverter output frequency characteristic of the virtual inertia (J) and virtual damping (D) coefficient, and the virtual parameters need to be modified and adjusted according to the purpose. To solve this problem, this paper proposes a virtual parameter adaptive control strategy based on fuzzy control theory to adjust the frequency characteristics of VSG. MATLAB/Simulink is used to build a simulation model to verify the correctness of the proposed fuzzy control theory’s adaptive virtual parameter theory.

Adaptive Coordinated Control Strategy of Multi Manipulator System based on Multi-Agent

With people's increasing awareness of life and the increasing complexity of exploration in unknown environment, a single robot can not meet the increasing demand, including the price, flexibility and efficiency of robots. As a common mechanical control system in industrial production instead of human production, multi manipulator system can be applied in complex environment, multi task and other conditions. In order to settle the coordinated control fault of multi manipulator system, we study adaptive coordinated control strategy with the help of multi-agent research method in this paper, which can simplify the complexity of the problem and design an efficient and feasible system control protocol. The complex items in the multi manipulator system are treated as non affine systems. Using the design idea of non affine algorithm, combined with implicit function theorem and median theorem, the non affine system is transformed into affine systems, the controller is separated, and a distributed adaptive control strategy is designed. The results indicate that manipulator systems can effectively track the active manipulator system in finite time and the significance of the algorithm is proven by MATLAB simulation analysis.