Time-Varying Formation Control for Heterogeneous Planar Underactuated Multi-Vehicle Systems

2021 ◽  
Author(s):  
Bo Wang ◽  
Sergey Nersesov ◽  
Hashem Ashrafiuon
Keyword(s):  
Formation Control ◽  
Sliding Mode ◽  
Nonholonomic Constraints ◽  
Rigid Bodies ◽  
Control Law ◽  
Time Varying ◽  
Local Motion ◽  
Order Error ◽  
Control Approach ◽  
Vehicle Networks

Abstract This paper presents a distributed control approach for time-varying formation of heterogeneous planar underactuated vehicle networks without global position measurements. All vehicles in the network are modeled as generic three degree of freedom planar rigid bodies with two control inputs, and are allowed to have non-identical dynamics. Feasible trajectories are generated for each vehicle using the nonholonomic constraints of the vehicle dynamics. By exploiting the cascaded structure of the planar vehicle model, a transformation is introduced to define the reduced order error dynamics, and then, a sliding-mode control law is proposed. Low level controller for each vehicle is derived such that it only requires relative position and local motion information of its neighbors in a given directed communication network. The proposed formation control law guarantees the uniform global asymptotic stability (UGAS) of the closed-loop system subject to bounded uncertainties and disturbances. The proposed approach can be applied to underactuated vehicle networks consisting of mobile robots, surface vessels and planar aircraft. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.

Formation Control for Underactuated Surface Vessel Networks

2020 ◽  
Author(s):  
Bo Wang ◽  
Sergey Nersesov ◽  
Hashem Ashrafiuon
Keyword(s):  
Dynamic Model ◽  
Formation Control ◽  
Sliding Mode ◽  
Nonlinear Dynamical System ◽  
Order Error ◽  
Reduced Order ◽  
Control Approach ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
Error Dynamics

Abstract Developing distributed control algorithms for multi-agent systems is difficult when each agent is modeled as a nonlinear dynamical system. Moreover, the problem becomes far more complex if the agents do not have sufficient number of actuators to track any arbitrary trajectory. In this paper, we present the first fully decentralized approach to formation control for networks of underactuated surface vessels. The vessels are modeled as three degree of freedom planar rigid bodies with two actuators. Algebraic graph theory is used to model the network as a directed graph and employing a leader-follower model. We take advantage of the cascade structure of the combined nonlinear kinematic and dynamic model of surface vessels and develop a reduced-order error dynamic model using a state transformation definition. The error dynamics and consequently all system states are then stabilized using sliding mode control approach. It is shown that the stabilization of the reduced-order error dynamics guarantees uniform global asymptotic stability of the closed-loop system subject to bounded uncertainties. The proposed control method can be implemented in directed time-invariant communication networks without the availability of global position measurements for any of the vehicles participating in the network. An example of a a network of five surface vessels is simulated to verify the effective performance of the proposed control approach.

Discrete-Time Sliding Mode Controller With Time-Varying Band for Unfixed Sampling-Time Systems

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Chidentree Treesatayapun
Keyword(s):  
Discrete Time ◽  
Sliding Mode ◽  
Fuzzy Rule ◽  
Current Control ◽  
Sampling Time ◽  
Control Law ◽  
Time Varying ◽  
Control Effort ◽  
Time Systems ◽  
The Mathematical Model

An adaptive discrete-time controller is developed for a class of practical plants when the mathematical model is unknown and the sampling time is nonconstant or unfixed. The data-driven model is established by the set of plant's input–output data under the pseudo-partial derivative (PPD) which represents the change of output with respect to the change of control effort. The multi-input fuzzy rule emulated network (MiFREN) is utilized to estimate PPD with an online-learning phase to tune all adjustable parameters of MiFREN with the convergence analysis. The proposed control law is developed by the discrete-time sliding mode control (DSMC), and the time-varying band is established according to the unfixed sampling time and unknown boundaries of disturbances and uncertainties. The prototype of direct current-motor current control with uncontrolled-sampling time is constructed to validate the performance of the proposed controller.

Fault-tolerant formation control of stochastic nonlinear multi-agent systems with time-varying weighted topology

2020 ◽  
Vol 42 (8) ◽  
pp. 1461-1474 ◽  
Author(s):  
Mahdi Siavash ◽  
Vahid Johari Majd ◽  
Mahdie Tahmasebi
Keyword(s):  
Formation Control ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Design Method ◽  
Time Varying ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Rlc Circuit ◽  
Adaptive Laws ◽  
Multi Agent

In this paper, the fault-tolerant formation control of nonlinear stochastic multi-agent systems in the presence of actuator faults, disturbances, and time-varying weighted topology is considered. While most traditional fault-tolerant control methods in the literature use fixed weights on the topology edges, in this study these weights are considered time-varying using a pre-designed function, which allows formulating the system more realistically. Moreover, in contrast with previous works on fault-tolerant multi-agent systems, in this study, the model of the agents is considered to be stochastic in general. Furthermore, the actuators of the agents are considered to have a time-varying fault of additive and multiplicative types. A passive and an active fault-tolerant controllers are designed based on the back-stepping sliding-mode approach. In the passive method, a constant robust controller is proposed using an upper bound of the faults while, in the active controller, the additive and multiplicative faults are estimated using adaptive laws. The active and passive fault-tolerant controllers guarantee that the formation errors converge to a bounded region near the origin in a mean-square sense and all of the existing signals in the closed-loop system remain bounded in probability. The results of the formation control are extended to consensus control as well. Finally, a stochastic multi-aircraft model and an RLC circuit with stochastic part are used as two case studies to illustrate the effectiveness of the proposed design method.

scholarly journals Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jinghua Guo ◽  
Keqiang Li ◽  
Jingjing Fan ◽  
Yugong Luo ◽  
Jingyao Wang
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Law ◽  
Time Varying ◽  
Steering Control ◽  
Structure Control ◽  
Adaptive Sliding Mode ◽  
Automatic Steering ◽  
Neural Fuzzy

AbstractThis paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Study and Application of a Slide Mode Control on Pneumatic Force Servo System

2012 ◽  
Vol 466-467 ◽  
pp. 896-900
Author(s):  
Yan Li Yang ◽  
Wei Xiang Shi ◽  
Yan Cao ◽  
Lei Lei
Keyword(s):  
Pid Control ◽  
Sliding Mode ◽  
Servo System ◽  
Servo Control ◽  
Time Varying ◽  
Slide Mode Control ◽  
Control Approach ◽  
Mode Control ◽  
Servo Control System ◽  
Identification Approach

In this paper, a sliding mode control approach combined with the boundary saturation function approach is put forward and used in a pneumatic force servo system to achieve an exact force control. First, a comparatively accurate model of the system is obtained by using the system identification approach and an analysis is made on the time-varying nature of the model. Then, it is designed by use of the boundary saturation approach, thus overcoming the system instability caused by the non-linearity of the proportional pressure valve and the change of the temperature inside the air cylinder. Finally, the performance of the pneumatic force servo control system is simulated and a comparison is made with the PID control. Results show the feasibility and effectiveness of the approach.

scholarly journals Adaptive Global Sliding Mode Controller Design for Perturbed DC-DC Buck Converters

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1249
Author(s):  
Saleh Mobayen ◽  
Farhad Bayat ◽  
Chun-Chi Lai ◽  
Asghar Taheri ◽  
Afef Fekih
Keyword(s):  
Controller Design ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Buck Converter ◽  
Control Law ◽  
Control Input ◽  
Buck Converters ◽  
Tracking Accuracy ◽  
Control Approach ◽  
Efficient Suppression

This paper proposes a novel adaptive intelligent global sliding mode control for the tracking control of a DC-DC buck converter with time-varying uncertainties/disturbances. The proposed control law is formulated using a switching surface that eliminates the reaching phase and ensures the existence of the sliding action from the start. The control law is derived based on the Lyapunov stability theory. The effectiveness of the proposed approach is illustrated via high-fidelity simulations by means of Simscape simulation environment in MATLAB. Satisfactory tracking accuracy, efficient suppression of the chattering phenomenon in the control input, and high robustness against uncertainties/disturbances are among the attributes of the proposed control approach.

scholarly journals Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

2020 ◽  
Author(s):  
Jinghua Guo ◽  
Keqiang Li ◽  
Jinjin Fan ◽  
Yugong Luo ◽  
Jingyao Wang
Keyword(s):  
Electric Vehicles ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Law ◽  
Time Varying ◽  
Steering Control ◽  
Structure Control ◽  
Adaptive Sliding Mode ◽  
Automatic Steering ◽  
Neural Fuzzy

Abstract This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to surprise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, The results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

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