A Second-Order Sliding Mode Controller of Quad-Rotor UAV Based on PID Sliding Mode Surface with Unbalanced Load

Kang, Bing; Yan, Miao; Liu, Fu; Duan, Jilu; Wang, Ke; Jiang, Shoukun

doi:10.1007/s11424-020-9306-6

Cited by 27 publications

(12 citation statements)

References 25 publications

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“…This work uses a fully controlled four-engine model similar to that of [14]. The simulated model is based on the dynamic equations used in [14], [15], and [16]. The model is divided into body rotation dynamics, through the angles ϕ (roll angle), θ (pitch angle), and ψ (yaw angle), and translation in Cartesian axes, as show in Fig.…”

Section: Quadcopter Modelingmentioning

confidence: 99%

Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

Sá

Neto

2023

IEEE Access

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An anti-collision method for multi-agent systems based on the centralization and decentralization effects in a control protocol is presented in this article. In this context, a matrix called Anti-Laplacian is defined, it acts as a decentralizing agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved.

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Section: Quadcopter Modelingmentioning

confidence: 99%

Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

Sá

Neto

2023

IEEE Access

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“…To highlight the unmanned concept in CAAC, we manage to apply a second-order sliding mode flight controller (SFC) to the given six-DOF flight model [18]. The logic is to ensure that the system states can converge to the sliding mode manifolds = = 0 in limited time with reference to appropriate control laws [19]- [21]. Rewrite (1) into the following form:…”

Section: Tablementioning

confidence: 99%

High-Fidelity Decision-Making and Simulation for Cooperative Autonomous Air Combat Considering the Effect of Flight Controller

Wang

Wang²,

et al. 2022

IEEE Access

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In the past decade, how improving the fidelity of maneuver gaming and the synergy of target allocation has become key issues in cooperative autonomous air combat researches. To address these two problems, this study proposes a maneuver decision-making algorithm based on an optimized dynamic Bayesian network and a target allocation decision-making algorithm based on an optimized hybrid particle swarm optimization. In maneuvering decision-making, the state transition's reliability and the air combat's autonomy are enhanced through considering the effect of sliding mode control. The Bayesian network is improved through introducing a strategy for dynamic prior probability updating. The computation is reduced and the efficiency is increased through pruning the minimax search tree according to visual prediction. In target allocation decision-making, the algorithm's convergence speed is greatly accelerated and the solution's global optimality is improved through introducing immigrant particles. The algorithm's application scope is expanded through proposing a solution principle about unequal quantity combat situations. Furthermore, the end criterion is specially designed to fit real-world combats through introducing a fire control. The simulation results show that the designed decision-making algorithms are more effective in solving the problem of cooperative autonomous air combat, which shows that the various improvements introduced in this study are reasonable and effective.INDEX TERMS Cooperative autonomous air combat, maneuver decision-making, target allocation decision-making, sliding mode controller, dynamic Bayesian network, hybrid particle swarm optimization.

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“…In order to improve control efficiency, PID control [8], backstepping control [9], sliding mode control (SMC) [10,11], and other control methods are proposed. Among them, the backstepping method can simplify the controller design process of AUVs and is widely used [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

Guo,

Wang,

2023

Drones

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Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.

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A Second-Order Sliding Mode Controller of Quad-Rotor UAV Based on PID Sliding Mode Surface with Unbalanced Load

Cited by 27 publications

References 25 publications

Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

Multi-Agent Collision Avoidance System Based on Centralization and Decentralization Control for UAV Applications

High-Fidelity Decision-Making and Simulation for Cooperative Autonomous Air Combat Considering the Effect of Flight Controller

An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

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