Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities

2021

Complexity

Due to the nonlinear characteristics of the vehicle speed system, its stability is difficult to control. This paper analyzes the stability and traceability of the vehicle speed system under nonlinear characteristics. A sliding mode control method of the nonlinear system state observation based on linear matrix inequalities (LMIs) is proposed. In the proposed control method, Lyapunov function is used as the control function to track the position and speed of the vehicle speed system in real time. In the design process of the controller, the successive scaling method (SSM) is designed to improve the tracking accuracy. The simulation results demonstrate that the sliding mode control can effectively track the position of the vehicle speed system, which has better stability and traceability for the nonlinear vehicle speed system.

Section: Resultsmentioning

confidence: 99%

“…In many cases, we can also use other methods to solve the uncertainty of the system. For example, adaptive fuzzy neural network control can be used to solve the uncertainty of the system [17]. In [18], a new adaptive interleaved reinforcement learning algorithm was proposed for nonlinear systems with matching or mismatching uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of the Vehicle Speed System Based on LMIs

Gao

2021

Complexity

“…The estimation and compensation of the manipulator system's uncertainties and external disturbances is a critical issue to resolve [15][16][17]. Fuzzy logic [18] and neural network [19] can provide accurate estimates of uncertainties. However, using approximate principles in a space manipulator system for estimation necessitates a large amount of calculation, which makes it challenging to apply in practical applications.…”

Section: Introductionmentioning

confidence: 99%

TDE-Based Adaptive Integral Sliding Mode Control of Space Manipulator for Space-Debris Active Removal

Wang

et al. 2022

Aerospace

The safe and dependable removal of large-scale space debris has been a long-standing challenge that is critical to the safety of spacecraft and astronauts. In the process of capturing and deorbiting space debris, the space manipulator must achieve extremely high control and precision. However, strong couplings, model uncertainties, and various inevitable unknown disturbances cause many difficulties in coordinated control of the space manipulator. To solve this challenge, this study examines the stabilization control of a space manipulator after capturing non-cooperative large-scale space debris and presents an adaptive integral sliding mode control (AISMC) scheme with time-delay estimation (TDE). The coupling term and lumped uncertainty are estimated by TDE technology, which eliminates the requirement of prior knowledge. Adaptive sliding mode control (ASMC) is used as desired injecting dynamics to compensate TDE errors, and a PID-type integral sliding mode surface is designed to reduce steady-state errors. The Lyapunov criterion is used to prove the global stability of the controller. Simulation results show that the controller has high tracking accuracy and strong robustness.

“…Then, an intelligent control algorithm based on a fuzzy logic and neural network is proposed to control the system, which makes up for the shortcomings of the neural network in fuzzy data processing and the defects of pure fuzzy logic in learning. 15 When the system changes and is subject to external interference, the fuzzy neural network can adjust the parameters through online learning to reduce the fluctuation of the system. Even if the air supply system has obvious nonlinearity, it can also have a good decoupling effect by treating various disturbances and uncertainties as total disturbances and real-time adjustment compensation, realizing the coordinated control of inlet pressure and flow of the air supply system, which is verified by simulation in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…By summarizing the control scheme of the air supply system, it can be concluded that the system model built by many decoupling controllers is only in the form of transfer function. Therefore, it is assumed that the model of the fuel cell intake system is linear, and the external disturbances such as the hydration process of the exchange membrane and the frequent change of load current are not taken into account, but the air mass flow and pressure have extreme coupling characteristics since the air supply subsystem is a strongly nonlinear system, traditional decoupling methods are usually ineffective for nonlinear systems, variable structure systems, and complex systems whose coupling relationship and coupling strength vary with time and load, which makes the control of air flow and pressure unsuitable, resulting in system overshoot, slow response, or oscillation. , …”

Section: Introductionmentioning

confidence: 99%

Coordinated Control of the Fuel Cell Air Supply System Based on Fuzzy Neural Network Decoupling

Jia¹,

et al. 2021

ACS Omega

In order to achieve the goal of carbon neutralization, hydrogen plays an important role in the new global energy pattern, and its development has also promoted the research of hydrogen fuel cell vehicles. The air supply system is an important subsystem of hydrogen fuel cell engine. The increase of air supply can improve the output characteristics of a fuel cell, but excessive gas supply will destroy the pressure balance of the anode and cathode. In the actual operation of a proton-exchange membrane fuel cell, considering the load change, it is necessary not only to ensure the stability of reactor pressure but also to meet the rapid response of inlet pressure and flow in the process of change. Therefore, the coordinated control of the two is the key to improving fuel cell output performance. In this paper, the dynamic model of the intake system is built based on the mechanism and experimental data. On this basis, the double closed-loop proportion integration differentiation (PID) control and feedforward compensation decoupling PID control are carried out for the air supply system, respectively. Then, the fuzzy neural network decoupling control strategy is proposed to make up for the shortcomings that the double closed-loop PID cannot achieve decoupling and the feedforward compensation decoupling does not have adaptability. The results show that the fuzzy neural network control can realize the decoupling between air intake flow and pressure and ensure that the air intake flow and pressure have a good follow-up, and the system’s response speed is fast.