Neuro-sliding Mode Controller Research Articles

Design of a neuro-sliding mode controller for interconnected quadrotor UAVs carrying a suspended payload

Design of a neuro-sliding mode controller for interconnected quadrotor UAVs carrying a suspended payload

Fixed-time neuro-sliding mode controller design for quadrotor UAV transporting a suspended payload

This study develops a fixed-time neuro-sliding mode controller to enhance the transient response and robustness of a quadrotor that is subjected to external disturbances and parameter uncertainties along with the dynamics of a suspended payload. A simple neural network with a fixed-time adaptive law is used to estimate unknown dynamics; therefore, no previous knowledge of dynamics is required to develop controllers. The neural network component will also help to eliminate the chattering effect of the sliding mode controller through its learning structure. Fixed-time convergence to desired trajectories is proven by detailed theoretical analysis based on the Lyapunov stability theorem, and to show the proposed controller’s effective performance, thorough numerical simulations are run.

Finite-Time Neuro-Sliding-Mode Controller Design for Quadrotor UAVs Carrying Suspended Payload

Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller.

Neuro sliding mode control of quadrotor UAVs carrying suspended payload

In this paper, a neuro-sliding mode controller (SMC) has been designed for a quadrotor transporting a suspended payload. SMCs are very efficient under uncertain conditions. However, if the uncertain dynamics change over time, SMC gains need to be updated to maintain the tracking performance. This study proposes a neuro-SMC to control a quadrotor UAV carrying a suspended payload in the existence of time-varying uncertain dynamics. Once the accuracy of the proposed controller is demonstrated theoretically using the Lyapunov stability criterion, the effectiveness of the proposed controller is shown in simulation, which confirms the theoretical claims.

Neuro Sliding Mode Control for Exoskeletons with 7 DoF

In this work, a novel neuro-sliding mode controller (NSMC) is developed for a 7 degree of freedom (DoF) upper limb exoskeleton. Even though the regular sliding mode controller (SMC) is very sufficient tool when the unknown dynamics of the system is time invariant, variation in the unknown dynamics cannot be handled by regular SMC. Therefore, two-layer neural network (NN) is used to approximate the exoskeleton dynamics in the structure of the SMC. Stability of the NSMC is developed by using Lyapunov stability criteria. To validate our theoretical claims and to compare NSMC with regular SMC, simulation results are provided at the end of the paper. In the simulation section, advantage of the NSMC over regular SMC is presented in the presence of time-varying unknown exoskeleton dynamics.

Design of adaptive neuro sliding mode controller for anesthesia drug delivery based on biogeography based optimization

Design of adaptive neuro sliding mode controller for anesthesia drug delivery based on biogeography based optimization

A Simple Flexible and Robust Control Strategy for Wind Energy Conversion Systems Connected to a Utility Grid

This paper presents a simple and robust control strategy for a variable speed wind turbine conversion system using a squirrel-cage induction generator and a three-phase voltage source (AC/DC/AC) Pulse Width Modulation (PWM) converter connected to the utility grid through an LCL filter. The control strategy integrates for the generator side an adaptive radial basis function (RBF) neurosliding mode controller associated with the rotor flux oriented vector control which is used to regulate the turbine rotation speed, rotor flux, and the DC bus voltage. For the grid side, the inverter current and voltage regulation as well as the current injected into the grid are regulated by PI controllers for two modes of operation, namely, the stand-alone mode and grid connected mode. The main contribution of this article is the introduction of a new and simple control algorithm allowing automatic mode switching method based on wind speed. The proposed scheme is very efficient and can be easily implemented in practice. Simulation results illustrate the effectiveness and feasibility of the proposed algorithm.

A Modified RBF Neuro-Sliding Mode Control Technique for a Grid Connected PMSG Based Variable Speed Wind Energy Conversion System

A modified control scheme based on the combination of online trained neural network and sliding mode techniques is proposed to enhance maximum power extraction for a grid connected permanent magnet synchronous generator (PMSG) wind turbine system. The proposed control method does not need the knowledge of the uncertainty bounds nor the exact model of the nonlinear system. Since the neural network is trained online, the time to estimate good weights can affect the dynamic performance of the process during the startup phase. Therefore an appropriate way to smoothly and explicitly accelerate the neural network rate of convergence during the startup phase is proposed. Furthermore, a flexible grid side voltage source converter control structure which can handle both grid connected and standalone modes based on conventional proportional integral (PI) control method is presented. Simulations are done in Matlab/Simulink environment to verify the effectiveness and assess the performance of the proposed controller. The results analysis shows the superiority of the proposed RBF neuro-sliding mode controller compared to a nonlinear controller based on sliding mode control method when the system undergoes parameter uncertainties.

Designing a Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control systems. Firstly, to determine the parameters of switching function used in the sliding mode control design, an improved genetic algorithm is applied. The proposed improved genetic algorithm provides a fast convergence rate and a proper dynamic performance in comparison with conventional genetic algorithms especially in online control applications. Then, an adaptive neural sliding mode control based on radial-basis function neural network approximation is proposed to eliminate chattering phenomenon in the sliding mode control. A numerical example is given to illustrate the effectiveness of the proposed controller in networked control systems. The results show that the proposed controller provides high-performance dynamic characteristics and robustness against plant parameter variations and external disturbances.

Chattering-Free Neuro-Sliding Mode Control of 2-DOF Planar Parallel Manipulators

This paper proposes a novel chattering free neuro-sliding mode controller for the trajectory tracking control of two degrees of freedom (DOF) parallel manipulators which have a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. A feedforward neural network (NN) is combined with an error estimator to completely compensate the large nonlinear uncertainties and external disturbances of the parallel manipulators. The online weight tuning algorithms of the NN and the structure of the error estimator are derived with the strict theoretical stability proof of the Lyapunov theorem. The upper bound of uncertainties and the upper bound of the approximation errors are not required to be known in advance in order to guarantee the stability of the closed-loop system. The example simulation results show the effectiveness of the proposed control strategy for the tracking control of a 2-DOF parallel manipulator. It results in its being chattering-free, very small tracking errors and its robustness against uncertainties and external disturbances.

A Neuro-Sliding Mode Control Scheme for Constrained Robots with Uncertain Jacobian

The joint robot control requires to map desired cartesian tasks into desired joint trajectories, by using the ill-posed inverse kinematics mapping. In order to avoid inverse kinematics, the control problem is formulated directly in task space to gives rise to cartesian robot control. In addition, when the robot is constrained due to its kinematic mappings yields a stiff system and an additional complexity arises to implement cartesian control for constrained robots. In this paper, an alternative approach is proposed to guarantee global convergence of force and position cartesian tracking errors under the assumption that the jacobian is not exactly known. A neuro-sliding mode controller is presented, where a small size adaptive neural network compensates approximately for the inverse dynamics and an inner control loop induces second order sliding modes to guarantee tracking. The sliding mode variable tunes the online adaptation of the weights. A passivity analysis yields the energy Lyapunov function to prove boundedness of all closed-loop signals and variable structure control theory is used to finally conclude convergence of position and force tracking errors. Experimental results are provided to visualize the expected performance.

Discrete time neuro sliding mode control with a task-specific output error

The problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task-specific output error is calculated, the neurocontroller parameters can be tuned so that the task is achieved. The paper postulates the strategy for discrete time representation of uncertain nonlinear systems belonging to a particular class. The performance of the proposed technique has been clarified on a third order nonlinear system, and the parameters of the controller are adjusted by using the error backpropagation algorithm. It is observed that the prescribed behavior can be achieved with a simple network configuration.