Adaptive Neural Sliding Mode Control Research Articles

Adaptive neural sliding mode control of an uncertain permanent magnet linear motor system with unknown input backlash in laser processing

In this article, a novel robust and high-precision control scheme is developed for a permanent magnet linear motor system in large-range laser processing subject to unknown system uncertainties and input backlash. First, to enhance the robustness of the controller, an adaptive neural sliding mode control (SMC) scheme is proposed. Besides, to address the system uncertainties, a novel simplified radial basis neural network (RBFNN) is applied in the adaptive SMC scheme. Subsequently, considering that the unexpected chattering introduced by SMC strategy, another RBFNN is employed to approximate the optimal switching gain. In addition, considering that the nonlinear input backlash caused by actuator can also cause the fluctuation of the input signal, an inverse backlash model with the method of reverse compensation is proposed to further minimize the effect of the input backlash on control accuracy. Finally, simulation results, validation experiments, and laser processing experiments are provided to demonstrate that the proposed control scheme significantly reduces the impact of chattering and input backlash. Additionally, it exhibits outstanding control accuracy.

Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics.

The problem of neural adaptive distributed formation control is investigated for quadrotor multiple unmanned aerial vehicles (UAVs) subject to unmodeled dynamics and disturbance. The quadrotor UAV system is divided into two parts: the position subsystem and the attitude subsystem. A virtual position controller based on backstepping is designed to address the coupling constraints and generate two command signals for the attitude subsystem. By establishing the communication mechanism between the UAVs and the virtual leader, a distributed formation scheme, which uses the UAVs' local information and makes each UAV update its position and velocity according to the information of neighboring UAVs, is proposed to form the required formation flight. By designing a neural adaptive sliding mode controller (SMC) for multi-UAVs, the compound uncertainties (including nonlinearities, unmodeled dynamics, and external disturbances) are compensated for to guarantee good tracking performance. The Lyapunov theory is used to prove that the tracking error of each UAV converges to an adjustable neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the proposed scheme.

Advanced adaptive neural sliding mode control applied in PMSM driving system

Advanced adaptive neural sliding mode control applied in PMSM driving system

Adaptive neural sliding mode control for heterogeneous ship formation keeping considering uncertain dynamics and disturbances

This paper investigates the formation keeping problem of heterogeneous ships with underactuated inputs, uncertain dynamics, and environmental disturbances. The control objective is to make the heterogeneous followers keep the desired formation while tracking a leader. To solve the problem effectively, a novel virtual leader–follower formation scheme considering the ship heterogeneity is proposed by utilizing the backstepping method, adaptive neural network, and adaptive control law. The stability of the formation control system is proved based on Lyapunov’s direct method where all tracking errors are guaranteed to be uniformly ultimately bounded. Finally, simulations and comparisons are conducted to verify the effectiveness of the proposed control law.

Adaptive neural sliding mode control of uncertain robotic manipulators with predefined time convergence

AbstractIn this article, a predefined time convergence adaptive tracking control scheme is designed for a class of uncertain robotic manipulators with input saturation. First, a novel auxiliary dynamic system is proposed to handle the influence of input saturation. Radial basis function neural networks are used to approximate the uncertainty of the closed‐loop system and the neural adaptive law is designed by using the given time constant so that the neural networks have a fast convergence rate. The adaptive tracking controller is constructed by utilizing a nonsingular terminal sliding mode surface. Different from the finite‐time and the fixed‐time sliding mode control methods where the upper bound of the convergence time is related to system parameters, the convergence time upper bound of the proposed sliding mode controller is a given constant. Finally, numerical simulations are performed to illustrate that the proposed control scheme possesses the advantages of fast convergence rate and input saturation elimination.

Optimization-based adaptive neural sliding mode control for nonlinear systems with fast and accurate response under state and input constraints

The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.

Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems

In this article, a sliding mode control (SMC) is proposed on the basis of an adaptive neural network (NN) for a class of Single-Input–Single-Output (SISO) nonlinear systems containing unknown dynamic functions. Since the control objective is to steer the system states to track the given reference signals, the SMC method is considered by employing the adaptive neural network (NN) strategy for dealing with the unknown dynamic problem. In order to compress the impaction coming from chattering phenomenon (which inherently exists in most SMC methods because of the discontinuous switching term), the boundary layer technique is considered. The basic design idea is to introduce a continuous proportional function to replace the discontinuous switching control term inside the boundary layer so that the chattering can be effectively alleviated. Finally, both Lyapunov theoretical analysis and computer numerical simulation are used to verify the effectiveness of the proposed SMC method.

An Adaptive Neural Sliding Mode Controller Design for Autonomous Underwater Vehicle based on Improved Grey Wolf Optimization Algorithm

An Adaptive Neural Sliding Mode Controller Design for Autonomous Underwater Vehicle based on Improved Grey Wolf Optimization Algorithm

Adaptive sliding mode control with hysteresis compensation-based neuroevolution for motion tracking of piezoelectric actuator

In this paper, an adaptive sliding mode control with hysteresis compensation-based neuroevolution (ACNE) is proposed for precise motion tracking of the piezoelectric actuator (PEA) in the presence of uncertainties, disturbances, and nonlinearity hysteresis characteristics. Firstly, a new memetic differential evolution (MeDE) algorithm is proposed to optimize the weights of a 3-layer neural network (called neuroevolution or NE). In MeDE, a differential evolution algorithm is used as a global search scheme and the Jaya algorithm is used as local search exploitation. Secondly, an inverse hysteresis model of PEA is identified by the neuroevolution model to provide a feed-forward NE control signal to compensate for the hysteresis behavior of PEA system. Thirdly, an adaptive neural sliding mode control plus feedforward NE (ACNE) control is designed to enhance the quality control and guarantee asymptotical stability for PEA system. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the experimental Thorlabs piezoelectric actuator (PEA) is set up to verify the robustness and effectiveness of the proposed approach. Results show that the identified MeDE-Neuroevolution model has successfully applied to model the inverse hysteretic of PEA system and the performance of MeDE has better than Jaya, DE, and PSO in terms of best, worst, average, and standard deviation. Furthermore, in motion tracking control, the performance of proposed ACNE control has more accurate than a classical PID control, a feedforward control, a hybrid feedback–feedforward control, and an adaptive neural sliding mode control without a compensator.

Adaptive neural sliding mode control with prescribed performance of robotic manipulators subject to backlash hysteresis

Robust and precise control of robot systems are still challenging problems due to the existence of uncertainties and backlash hysteresis. To deal with the problems, an adaptive neural sliding mode control with prescribed performance is proposed for robotic manipulators. A finite-time nonsingular terminal sliding mode control combined with a new prescribed performance function (PPF) is developed to guarantee the transient and steady-state performance of the closed-loop system. Based on the sliding mode variable, an adaptive law is presented to effectively estimate the bound of system uncertainties where the prior knowledge of uncertainties is not needed. To approximate nonlinear function and unknown dynamics, the Gaussian radial basis function neural networks(RBFNNs) is introduced to compensate the lumped nonlinearities. All signals of the closed-loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. Finally, comparative simulations are conducted to illustrate superiority and reliability of the proposed control strategy.

Adaptive Neural Control for Gait Coordination of a Lower Limb Prosthesis

Because of distinct differences in structure and drive, the lower limb amputee that walks with prosthesis forms a heterogeneous coupled dynamic system. The strongly coupled nonlinearity makes it difficult for the lower limb prosthesis (LLP) to adapt to complex tasks, such as variable-speed walking and obstacle crossing. As a result, the typical behavior can be seen as gait incoordination or even gait instability. This paper proposes a new gait-coordination-oriented adaptive neural sliding mode control (GC-ANSMC) for the heterogeneous coupled dynamic system. At the high level, the controller adopts the homotopy algorithm, which inherits the intelligence of the healthy lower limb (HLL), to create the GC-oriented desired trajectory for the LLP. The embedding parameter of the homotopy algorithm is updated online based on the mean difference between the lab-based target trajectory and the HLL's delayed motion, resulting in better GC performance. In addition, the new GC-planning strategy has sufficient environmental adaptability with a limited lab-based target trajectory for complex tasks. At the low level, radial basis function neural network (RBFNN) is employed to model the human-prosthesis heterogeneous coupled system uncertainties online and generate the controlled torques for simultaneous uncertainty compensation and gait driving. According to Lyapunov's theory, the sliding mode gains and the cubic order evolution rules of the network's weight are carried out. As a result, the global convergence of the proposed control approach can be ensured, and the dynamic motion could be quickly tracked. Applications for the variable-speed walking and the obstacle crossing show that the present GC-ANSMC could achieve better control accuracy, faster convergence speed, lower controlled torques, and higher GC performance than traditional methods. These advantages, as a result, indicate a convincing potential for the adaptive control for the nonlinear human-prosthesis heterogeneous coupled dynamics in complex tasks.

Adaptive neural sliding mode control for two wheel self balancing robot

Adaptive neural sliding mode control for two wheel self balancing robot

Adaptive neural network-based robust <i>H</i><SUB align="right">∞ tracking control of a quadrotor UAV under wind disturbances

The paper deals with the stabilisation and trajectory tracking control of an autonomous quadrotor helicopter system in the presence of wind disturbances. The proposed adaptive tracking controller uses radial basis function neural networks (RBF NNs) to approximate unknown nonlinear functions in the system. Two controllers are proposed in this paper to handle the modelling errors and external disturbances: H∞ adaptive neural controller H∞-ANC and H∞-based adaptive neural sliding mode controller H∞-ANSMC. The design approach combines the robustness of sliding mode control (SMC) with the ability of H∞ to deal with parameter uncertainties and bounded disturbances. Furthermore, the RBF models are derived using Lyapunov stability analysis. The simulation results show that H∞-ANSMC is able to eliminate the chattering phenomenon, reject perturbation mismatch and leads to a better performance than H∞-ANC. A comparative simulation study between the proposed controllers is presented and the results are discussed.

Adaptive neural network-based robust <i>H</i><SUB align="right">∞ tracking control of a quadrotor UAV under wind disturbances

The paper deals with the stabilisation and trajectory tracking control of an autonomous quadrotor helicopter system in the presence of wind disturbances. The proposed adaptive tracking controller uses radial basis function neural networks (RBF NNs) to approximate unknown nonlinear functions in the system. Two controllers are proposed in this paper to handle the modelling errors and external disturbances: H∞ adaptive neural controller H∞-ANC and H∞-based adaptive neural sliding mode controller H∞-ANSMC. The design approach combines the robustness of sliding mode control (SMC) with the ability of H∞ to deal with parameter uncertainties and bounded disturbances. Furthermore, the RBF models are derived using Lyapunov stability analysis. The simulation results show that H∞-ANSMC is able to eliminate the chattering phenomenon, reject perturbation mismatch and leads to a better performance than H∞-ANC. A comparative simulation study between the proposed controllers is presented and the results are discussed.

An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems

An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability.

Adaptive Neural Sliding Mode Control of Markov Jump Systems Subject to Malicious Attacks

This article investigates the problem of adaptive neural sliding mode control for Markov jump systems. The transition probabilities of the Markov process are partly unknown. The cyber layer, which is vulnerable to the adversary, is deployed to broadcast the control signal to the actuator. The attacker can inject malicious information to the control signal to deteriorate the system performance. A sliding mode controller is designed to stabilize the system. Then, sufficient conditions that ensure the stability of the closed-loop system are given in the framework of the Lyapunov theory. In the end, two simulation examples are applied to illustrate the effectiveness and feasibility of the proposed methodology.

Model predictive and adaptive neural sliding mode control for three-dimensional path following of autonomous underwater vehicle with input saturation

With model uncertainties and input saturation, a novel control method is developed to steer an underactuated autonomous underwater vehicle that realizes the following of the planned path in three-dimensional (3D) space. Firstly, Serret–Frenet frame is applied as virtual target, and the path following errors model in 3D is built. Secondly, the control method which includes kinematic controller and dynamic controller was presented based on cascade control strategy. The kinematic controller, which is responsible for generating a series of constrained velocity signals, is designed based on model predictive control. The adaptive radial basis function neural network is used to estimate the model uncertainty caused by hydrodynamic parameters. Moreover, sliding mode control technology is applied in the design of dynamic controller to improve its robustness. Then, the control effect is compared with that of LOS guidance law and PID controller by simulation experiment. The comparison results show that the proposed algorithm can improve path following effect and reduce input saturation.

Neural Adaptive Sliding-Mode Control of a Bidirectional Vehicle Platoon with Velocity Constraints and Input Saturation

This paper investigates the vehicle platoon control problems with both velocity constraints and input saturation. Firstly, radial basis function neural networks (RBF NNs) are employed to approximate the unknown driving resistance of a vehicle’s dynamic model. Then, a bidirectional topology, where vehicles can only communicate with their direct preceding and following neighbors, is used to depict the relationship among the vehicles in the platoon. On this basis, a neural adaptive sliding-mode control algorithm with an anti-windup compensation technique is proposed to maintain the vehicle platoon with desired distance. Moreover, the string stability and the strong string stability of the whole vehicle platoon are proven through the stability theorem. Finally, numerical simulations verify the feasibility and effectiveness of the proposed control method.

Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.