Heterogeneous Nonlinear Systems Synchronization Based on Adaptive Super Twisting

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Oscar Salas-Peña ◽  
Jesús De León-Morales
Keyword(s):  
Nonlinear Systems ◽  
Control Strategy ◽  
Heterogeneous System ◽  
Degrees Of Freedom ◽  
Heterogeneous Systems ◽  
Experimental Results ◽  
Uncertain Nonlinear Systems ◽  
Sliding Modes ◽  
Control Scheme ◽  
Twisting Algorithm

In this work, the synchronization of a group of heterogeneous uncertain nonlinear systems is addressed. A strategy based on adaptive super twisting algorithm is proposed, in order to synchronize the outputs of the heterogeneous systems. With the aim of implementing the proposed control strategy, unmeasurable states are estimated by means of high-order sliding modes differentiators. This control scheme increases robustness against unknown dynamics and disturbances, whose bounds are not required to be known. Finally, experimental results for synchronizing a heterogeneous system platform, constituted by an inertial stabilization platform (ISP) and a helicopter of two degrees-of-freedom (DOF), are used to illustrate the performance of the proposed control scheme.

scholarly journals Super-Twisting Algorithm Applied to Velocity Control of DC Motor without Mechanical Sensors Dependence

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6041
Author(s):  
Fredy A. Valenzuela ◽  
Reymundo Ramírez ◽  
Fermín Martínez ◽  
Onofre A. Morfín ◽  
Carlos E. Castañeda
Keyword(s):  
Controller Design ◽  
Sliding Mode ◽  
Dc Motor ◽  
Experimental Results ◽  
Resistive Load ◽  
Velocity Control ◽  
Velocity Sensor ◽  
Control Scheme ◽  
Mechanical Sensors ◽  
Twisting Algorithm

A DC motor velocity control in feedback systems usually requires a velocity sensor, which increases the controller cost. Additionally, the velocity sensor used in industrial applications presents several disadvantages such as maintenance requirements and signal conditioning. In this work, we propose a robust velocity control scheme applied to a DC motor based on estimation strategies using a sliding-mode observer. This means that measurements with mechanical sensors are not required in the controller design. The proposed observer estimates the rotational velocity and load torque of the motor. The controller design applies the exact-linearization technique combined with the super-twisting algorithm to achieve robust performance in the closed-loop system. The controller validation was carried out by experimental tests using a workbench, which is composed of a control and data acquisition Digital Signal Proccessor board, a DC-DC electronic converter, an interface board for signals conditioning, and a DC electric generator connected to an adjustable resistive load. The simulation and experimental results show a significant performance of the proposed control scheme. During tests, the accuracy, robustness, and speed response on the controller were evaluated and the experimental results were compared with a classic proportional-integral controller, which uses a conventional encoder.

scholarly journals Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances

2020 ◽  
Vol 10 (20) ◽  
pp. 7073
Author(s):  
Roxana Recio-Colmenares ◽  
Kelly Joel Gurubel-Tun ◽  
Virgilio Zúñiga-Grajeda
Keyword(s):  
Nonlinear Systems ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Control Law ◽  
Uncertain Nonlinear Systems ◽  
External Disturbances ◽  
Ant Lion Optimizer ◽  
Highly Nonlinear ◽  
Whale Optimization ◽  
Ant Lion

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

A new adaptive control scheme for uncertain nonlinear systems with quantized input signal

2015 ◽  
Vol 352 (12) ◽  
pp. 5599-5610 ◽  
Author(s):  
Lantao Xing ◽  
Changyun Wen ◽  
Hongye Su ◽  
Jianping Cai ◽  
Lei Wang
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Input Signal ◽  
Uncertain Nonlinear Systems ◽  
Control Scheme ◽  
Adaptive Control Scheme

scholarly journals A Robust Adaptive CMAC Neural Network-Based Multisliding Mode Control Method for Unmatched Uncertain Nonlinear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Honghui Wang ◽  
Xiaojun Yu ◽  
Shicheng Liang ◽  
Sheng Dong ◽  
Zeming Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Control Strategy ◽  
Control Method ◽  
Control Variable ◽  
Uncertain Nonlinear Systems ◽  
Cerebellar Model Articulation Controller ◽  
Mode Control ◽  
Cmac Neural Network ◽  
Robust Adaptive

This paper proposes a new robust adaptive cerebellar model articulation controller (CMAC) neural network-based multisliding mode control strategy for a class of unmatched uncertain nonlinear systems. Specifically, by employing a stepwise recursion-based multisliding mode method, such a proposed strategy is able to obtain the virtual variables and the actual control inputs of each order first, and then it reduces the conservativeness for controller parameter design by adopting the CMAC neural network to learn both system uncertainties and virtual control variable derivatives of each order online. Meanwhile, with the hyperbolic tangent function being chosen to replace the sign function in the variable structured control components, the proposed strategy is able to avoid the chattering effects caused by the discontinuous inputs. The stability analysis shows that the proposed control strategy ensures that both the system tracking errors and the sliding modes of each order could converge exponentially to any saturated layer being set. The control strategy was also applied onto a passive electrohydraulic servo loading system for verifications, and simulation results show that such a proposed control strategy is robust against all system nonlinearities and external disturbances with much higher control accuracy being achieved.

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