Neuro-sliding Mode Control Research Articles

Adaptive Fast-Terminal Neuro-Sliding Mode Control for Robot Manipulators with Unknown Dynamics and Disturbances

This paper presents a novel adaptive fast-terminal neuro-sliding mode control (AFTN-SMC) for a two-link robot manipulator with unknown dynamics and external disturbances. The proposed controller is chattering-free and adaptive to the time-varying system uncertainties. Furthermore, the radial base function neural network (RBFNN) is employed to approximate the unknown state dynamics. The simulations have been completed in MATLAB, which illustrates the successful implementation of the proposed controller. The results showcased the effectiveness of the AFTN-SMC in achieving accurate tracking and stability, even in the presence of uncertainties and parameter variations. The incorporation of the RBFNN in the controller proved to be a valuable tool for approximating the unknown dynamics, enabling accurate estimation and control of the manipulator’s behavior. The research presented in this paper contributes to the advancement in control techniques for robot manipulators in diverse industrial and automation applications.

A New Self-Tuning Deep Neuro-Sliding Mode Control for Multi-Machine Power System Stabilizer

In order to increase the accuracy and improve the performance of the power system stabilizer (PSS) controller compared to the methods presented in other studies, this paper presents a new method for tuning sliding mode control (SMC) parameters for a PSS using a deep neural network. This controller requires fast switching which can create unwanted signals. To solve this problem, a boundary layer is used. First, the equations of a multi-machine power system are converted into the standard form of sliding mode control, and then the sliding surfaces are determined with three unknown parameters. Calculating and determining the optimal values (at any moment) for these parameters are fundamental challenges. A deep neural network can overcome this challenge and adjust the control system regularly. In the simulation, a power system with 4 machines and 11 buses is implemented and both phase-to-ground and three-phase errors are applied. The simulation results clearly show the good performance of the proposed method and especially the importance of the deep neural network in the SMC structure compared to other methods.

Adaptive Neuro Sliding Mode Control of Superconducting Magnetic Energy Storage System

ABSTRACT In this paper, an adaptive RBF neural network-based sliding mode controller is developed for superconducting magnetic energy storage (SMES) installed in a wind–diesel power system. Due to sudden load and wind power variations in a wind–diesel power system, power imbalances may force the system frequency to deviate from its nominal value and drive the system to operate in an unstable mode. Therefore, in a wind–diesel power system using a converter interface, a fast-acting and high power density SMES device is interconnected to carry the required power exchange whenever power imbalances occur. Based on switching manifold design, a sliding mode controller is developed to control the charging and discharging operations of SMES coil as per the power requirements, and the same is achieved by controlling the converter. A neural network using a radial basis function (RBF) is developed to estimate the unknown function of the system. Lyapunov stability analysis is conducted to guarantee the asymptotic stability of the system. MATLAB simulations are carried out and are presented to show the improved performance with the system exposed to disturbances in load and wind power.

Event-triggered neuro sliding mode control for an uncertain Euler–Lagrange system: An impulsive dynamical system approach

This paper investigates the design of a neural network-based sliding mode control (SMC) with event-based sampling for an uncertain Euler–Lagrange (EL) system. The EL system considered here is connected to the controller over a shared communication network. The event-based SMC is designed to ensure the reduced usage of the communication and computational resources with robust tracking performance. The controller is designed under the assumption that the EL system dynamics are unknown; thus, a radial basis function neural network (RBFNN) is used to estimate it. The discrete weight update dynamics of RBFNN is formulated for a continuous-time EL system which makes the closed-loop system hybrid. Hence, the stability of the closed-loop system is investigated using the theory of impulsive dynamical systems with the admissible inter sampling time. Finally, a numerical example of an EL system is simulated to establish the effectiveness of the proposed control strategy.

A Discrete Terminal Neuro-Sliding Mode Control with Adaptive Switching Gain for an Uncertain Nonlinear System

A Discrete Terminal Neuro-Sliding Mode Control with Adaptive Switching Gain for an Uncertain Nonlinear System

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.

Nonsingular Fast Terminal Adaptive Neuro-sliding Mode Control for Spacecraft Formation Flying Systems

In this paper, a nonsingular fast terminal adaptive neurosliding mode control for spacecraft formation flying systems is investigated. First, a supertwisting disturbance observer is employed to estimate external disturbances in the system. Second, a fast nonsingular terminal sliding mode control law is proposed to guarantee the tracking errors of the spacecraft formation converge to zero in finite time. Third, for the unknown parts in the spacecraft formation flying dynamics, we proposed an adaptive neurosliding mode control law to compensate them. The proposed sliding mode control laws not only achieve the formation but also alleviate the effect of the chattering. Finally, simulations are used to demonstrate the effectiveness of the proposed control laws.

A novel matlab/simulink model of DFIG drive using NSMC method with NSVM strategy

In this article, we present a comparative study between pulse width modulation (PWM) and neural space vector modulation (NSVM) strategy associated with a neuro-sliding mode control (NSMC) of stator reactive and stator active power command of a doubly fed induction generator (DFIG). The obtained results showed that, the proposed NSMC with NSVM strategy have rotor current with low harmonic distortion and low powers ripples than PWM strategy.

A comparison study between fuzzy PWM and SVM inverter in NSMC control of stator active and reactive power control of a DFIG based wind turbine systems

In this work, we present a comparative study between space vector modulation (SVM) and fuzzy pulse width modulation (FPWM) technique in neuro-sliding mode control (NSMC) of stator reactive and stator active power control of the doubly fed induction generator (DFIG) for wind turbine system (WTS). Two controls approach using NSMC-SVM and NSMC-FPWM control scheme are proposed and compared. The validity of the proposed control techniques is verified by simulation tests of a DFIG. The reactive power, rotor current and stator active power is determined and compared in the above strategies. The obtained results showed that the proposed NSMC with FPWM strategy has stator reactive and active power with low powers ripples and low rotor current harmonic distortion than SVM technique.

A New Adaptive Neuro-sliding Mode Control for Gantry Crane

This paper presents a new adaptive neuro-sliding mode control for gantry crane as varying rope length. This control method derived from combining the sliding surfaces of three subsystem of the gantry crane (trolley position, rope length, anti-swing) to draw out two system sliding surfaces: the trolley position with the anti-swing and the rope length and the anti-swing. On the based of the sliding mode control principle, drawn out the equivalent controller and the switching controller for gantry crane. But due to the uncertain parameters-nonlinear model of gantry crane with the bound disturbances, combining the neural approximate method, defined the neural controller and the compensation controller for the difference between the equivalent controller and the neural controller for two system control inputs: trolley position and rope length. The adaptive control laws for these controllers were deduced from Lyapunov’s stable criteria to asymptotically stabilize the sliding surfaces. Simulation studies are performed to illustrate the effectiveness of the proposed control.

A New Static Synchronous Series Compensator Control Strategy Based on RBF Neuro-Sliding Mode Technique for Power Flow Control and DC Voltage Regulation

This paper presents a new hybrid approach which combines Radial Basis Function neural network with the sliding mode technique to design a Static Synchronous Series Compensator controller for power flow control and DC voltage regulation of an electric power transmission system. The proposed neuro-sliding mode control technique requires reduced number of hidden units and the weights are updated through on-line learning. The proposed technique is robust and does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system (as opposed to existing methods based on Radial Basis Function neural networks). The performance of the proposed controller is validated on an IEEE 9 bus test system. Comparative results with another neural networks control and PI regulators show the effectiveness and satisfactory performance of the proposed controller in dealing with the perturbations considered.

Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems

In this paper, three neural control strategies are addressed to a class of single input-single output (SISO) discrete-time nonlinear systems affected by parametric variations. According to the control scheme, in a first step, a direct neural model (DNM) is developed to emulate the behavior of the system, then an inverse neural model (INM) is synthesized using specialized learning technique and cascaded to the system as a controller. The sliding mode backpropagation algorithm (SM-BP), which presents in a previous study robustness and high speed learning, is adopted for the training of the neural models. However, in the presence of strong parametric variations, the synthesized (INM) shows limitations to present satisfactory tracking performances. In fact, in order to improve the control results, two neural control strategies such as hybrid control and neuro-sliding mode control are proposed in this work. A simulation example is treated to show the effectiveness of the proposed control strategies

Discrete-time dynamic output feedback input shaping control of vibration in uncertain time-delay flexible structures

This paper proposes an observer-based discrete-time neuro-sliding mode control (NSMC) scheme combined with input shaping control for uncertain time-delay flexible structures. Only partial information of system states is known. It is shown that the proposed scheme not only guarantees closed-loop system stability, but also it effectively suppresses residual vibration and yields good robustness in the presence of state delays, input delays, parameter uncertainties, external disturbances and inaccurate impulse application instants simultaneously. The knowledge of upper bound of uncertainties is not required. Furthermore, it is shown that increasing the robustness to parameter uncertainties does not lengthen the duration of the impulse sequence. Simulation results demonstrate the efficacy of the proposed control scheme.

Robust tracking and model following based on discrete-time neuro-sliding mode control for uncertain time-delay systems

This paper proposes a discrete-time neuro-sliding mode control (NSMC) scheme to realize the problem of robust tracking and model following for a class of uncertain time-delay systems. It is shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of state delays, input delays, parameter uncertainties, and external disturbances. The selection of sliding surface and the existence of sliding mode are two important issues, which have been addressed. This scheme not only assures robustness against time-delays, system uncertainties and disturbances, but also avoids chattering phenomenon and reaching phase. Moreover, the knowledge of upper bound of uncertainties is not required. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme.

NONLINEAR TORQUE AND AIR-TO-FUEL RATIO CONTROL OF SPARK IGNITION ENGINES USING NEURO-SLIDING MODE TECHNIQUES

This paper presents a new approach for the calibration and control of spark ignition engines using a combination of neural networks and sliding mode control technique. Two parallel neural networks are utilized to realize a neuro-sliding mode control (NSLMC) for self-learning control of automotive engines. The equivalent control and the corrective control terms are the outputs of the neural networks. Instead of using error backpropagation algorithm, the network weights of equivalent control are updated using the Levenberg-Marquardt algorithm. Moreover, a new approach is utilized to update the gain of corrective control. Both modifications of the NSLMC are aimed at improving the transient performance and speed of convergence. Using the data from a test vehicle with a V8 engine, we built neural network models for the engine torque (TRQ) and the air-to-fuel ratio (AFR) dynamics and developed NSLMC controllers to achieve tracking control. The goal of TRQ control and AFR control is to track the commanded values under various operating conditions. From simulation studies, the feasibility and efficiency of the approach are illustrated. For both control problems, excellent tracking performance has been achieved.

Robust Neuro-Sliding Mode Multivariable Control Strategy for Powered Wheelchairs

This paper proposes an advanced robust multivariable control strategy for a powered wheelchair system. The new control strategy is based on a combination of the systematic triangularization technique and the robust neuro-sliding mode control approach. This strategy effectively copes with parameter uncertainties and external disturbances in real-time in order to achieve robustness and optimal performance of a multivariable system. This novel strategy reduces coupling effects on a multivariable system, eliminates chattering phenomena, and avoids the plant Jacobian calculation problem. Furthermore, the strategy can also achieve fast and global convergence using less computation. The effectiveness of the new multivariable control strategy is verified in real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty and external disturbance effects.

Observer based adaptive neuro-sliding mode control for MIMO nonlinear systems

In this paper, a stable adaptive neural sliding mode controller is developed for a class of multivariable uncertain nonlinear systems. For these systems not all state variables are available for measurements. By designing a state observer, adaptive neural systems, which are used to model unknown functions, can be constructed using the state estimations. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed loop system and obtain good tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models. Simulation results illustrate the design procedure and demonstrate the tracking performances of the proposed controller.

Neuro-Sliding-Mode Control of Flexible-Link Manipulators Based on Singularly Perturbed Model

A neuro-sliding-mode control (NSMC) strategy was developed to handle the complex nonlinear dynamics and model uncertainties of flexible-link manipulators. A composite controller was designed based on a singularly perturbed model of flexible-link manipulators when the rigid motion and flexible motion are decoupled. The NSMC is employed to control the slow subsystem to track a desired trajectory with a traditional sliding mode controller to stabilize the fast subsystem which represents the link vibrations. A stability analysis of the flexible modes is also given. Simulations confirm that the NSMC performs better than the traditional sliding-mode control for controlling flexible-link manipulators. The control strategy not only gives good tracking performance for the joint angle, but also effectively suppresses endpoint vibrations. The simulations also show that the control strategy has a strong self-adaptive ability for controlling manipulators with different parameters.

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.