Nonsingular Terminal Sliding Mode Control Research Articles

A modified sliding mode controller based on Fuzzy logic to control the longitudinal dynamics of the autonomous vehicle

This article delves into the intricate world of controlling the longitudinal dynamics of autonomous vehicles. In the first part, we studied two distinct controllers: the Super Twisting Sliding Mode Control and its modified version enriched with the integration of fuzzy logic, applied to the longitudinal dynamics of the autonomous automobile to follow a desired speed longitudinal profile, the two controllers are compared with a Neural Network-Based Non-singular Terminal Sliding-Mode Control, the system takes the throttle and brake as its inputs and delivers speed and acceleration as outputs. The overarching objective is to ensure that the controlled vehicle maintains a close and precise alignment with the desired speed profile. The second part of our research is dedicated to the development of adaptive cruise control systems and cruise control according to the safety conditions. This controller consists of two blocks low and upper controller, in upper controller the inputs are the speeds of the automobile in front and of the autonomous automobile itself, the safety distance, the measured distance. The output is the desired acceleration. The objective is to maintain a distance between the front vehicles, greater than or equal to the safety distance. For this, to achieve this task, we have implemented a control system known as the Proportional Integral Derivative (PID) controller in the adaptive cruise control system to control this system. In the low controller block, the same controllers used in the first part: the Super Twisting Sliding Mode Control and its modified version based on fuzzy logic, are applied, the system inputs are throttle and brake, and the outputs are speed and acceleration. This system is processed by MATLAB code, we obtained a better result with our proposed controller such that the maximum absolute speed error is equal to 0.0144 m/s in the first case of speed tracking, and to 0.006 m/s in the second case of the using adaptive cruise control, the illustrations below show the efficiency and robustness of these controllers.

Adaptive backstepping sliding mode compensation control for electro‐hydraulic load simulator with backlash links

AbstractWhen a mechanical backlash between the loading piston rod and the steering gear piston rod connection of the electro‐hydraulic load simulator (EHLS), it will lead to poor loading accuracy. To solve this problem, an adaptive backstepping sliding mode controller based on the backlash feedforward compensation and state observer is proposed. First, the mechanism of surplus force generation in the EHLS is analyzed, and a mathematical model containing position disturbance and backlash nonlinearity is established based on the analysis results. Second, a state observer is designed to estimate the system state and the parameter adaptive law is designed to estimate the system parameters. Third, the adaptive backstepping method is combined with the non‐singular terminal sliding mode control (NTSMC) strategy to deal with the position disturbance and parameter uncertainty and to enhance the system's dynamic performance. Finally, a backlash feed‐forward compensator is engineered to restrain the detrimental effects caused by the backlash nonlinearity, and the simulation experimental results show that the designed new method can realize the precise loading of the EHLS under different working conditions, and the surplus force is suppressed better at the same time.

A novel adaptive fast sliding mode control method based on fuzzy algorithm for the air management system of fuel cell stack

Proton exchange membrane fuel cells (PEMFCs) offer advantages, such as, cleanliness, high efficiency, and environmental friendliness. To achieve optimal control of the PEMFC stack's air supply and management system under complex operating conditions, a novel non-singular terminal sliding mode controller based on fuzzy algorithm is proposed. Compared with the conventional sliding mode control, the terminal sliding mode control exhibits further performance improvements. However, it still has limitations. The proposed innovative sliding mode structure would ensure finite-time convergence and avoid the issue of singularity associated with terminal sliding mode control. Moreover, fuzzy logic makes sliding mode gain adapt to the system and further enhances the performance and stability, which is benefited from a stronger sliding surface. Results indicate that, compared with the other controllers, the designed controller could reduce delay further and provide faster response to sudden changes. The overshoot of sliding surface has decreased by 70% compared to its previous level. This novel control approach significantly enhances the efficiency and reliability of the PEMFC system.

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.

Adaptive fractional-order nonsingular terminal sliding mode control and sequential quadratic programming torque distribution for lateral stability of FWID-EVs with actuator constraints

This paper proposes a novel sliding mode control (SMC) algorithm for direct yaw moment control of four-wheel independent drive electric vehicles (FWID-EVs). The algorithm integrates adaptive law theory, fractional-order theory, and nonsingular terminal sliding mode reaching law theory to reduce chattering, handle uncertainty, and avoid singularities in the SMC system. A sequential quadratic programming (SQP) method is also proposed to optimize the yaw moment distribution under actuator constraints. The performance of the proposed algorithm is evaluated by a hardware-in-the-loop test with two driving maneuvers and compared with two existing SMC-based schemes together with the cases with the change of vehicle parameters and disturbances. The results demonstrate that the proposed algorithm can eliminate chattering and achieve the best lateral stability as compared with the existing schemes.

Ultralocal Model-Free Adaptive Supertwisting Nonsingular Terminal Sliding Mode Control for Magnetic Levitation System

The magnetic levitation technology has attracted blooming attention from all walks of life thanks to its superiorities such as non-contact and active control. While the intrinsic features of open-loop instability and strong nonlinearity, along with the uncertain external disturbance make the levitation control particularly significant and difficult. For speeding up the convergence and obtaining a better stability and disturbance robustness, this paper proposes an ultra-local model-free adaptive super-twisting nonsingular terminal sliding mode control (ULMF-ASTNTSMC) strategy for the magnetic levitation system. Different from the conventional model-based control, ultra-local model-free control breaks the constraint of a precise system model. Therefore, an improved second-order ultra-local model is established for the magnetic levitation system. For the benefit of finite time convergence and non-singularity, the nonsingular terminal sliding mode surface is chosen. As a second-order algorithm, super-twisting (ST) algorithm can fundamentally suppress the chattering effect of the sliding mode control (SMC). An adaptive super-twisting algorithm is designed for the further improvement of the system dynamic response. The stability of the proposed scheme is strictly verified based on the Lyapunov stability theory. Simulation and experimental comparisons with the existing control methods validate the exceeding performance of the proposed strategy under various operating conditions.

A Novel Faster Fixed-Time Adaptive Control for Robotic Systems With Input Saturation

In this paper, an adaptive anti-saturation fixed-time control method with a faster convergence rate is studied for uncertain robotic systems. Firstly, a new segmental sliding variable is constructed to solve the singularity problem brought by the terminal sliding mode control (TSMC) and achieve a faster convergence rate. Secondly, to approximate and compensate for the model uncertainty and the viscous friction parameter, an adaptive neural network (ANN) is employed. Then, a novel auxiliary system is constructed to mitigate the effects of input saturation. Based on this, a novel non-singular TSMC algorithm integrated with the ANN and the auxiliary system is designed, so that the trajectory tracking errors of the robotic system can converge within a faster fixed time with actuator saturation. Finally, the superiority and practicability of the present method are verified by comparative experiments.

Performance-optimisation-based repetitive trajectory tracking control for autonomous farming vehicle

The high precision trajectory tracking for the autonomous farming vehicle (AFV) is closely related to work quality and crop yield. Farmland operations are usually repetitive tillages, and the farmland soil is relatively soft, slippery, and uneven, which can easily lead to uncertain problems such as slippage, parameter perturbation, and external disturbance. This paper proposes a finite-time repetitive trajectory tracking control strategy integrated with particle swarm optimisation (PSO) and equivalent input disturbance (EID) for the AFV to achieve performance optimisation. The repetitive control framework with finite-time convergence technique can effectively realise the iterative convergence performance of the repetitive trajectory tracking. The PSO algorithm is integrated into determining the control gains to optimise the dynamic and steady-state performances of the trajectory tracking control system. The EID method enhances the tracking precision and robustness to internal and external disturbances. Under MATLAB/Simulink and CarSim co-simulation environment, the effectiveness and advantages of the designed control strategy are illustrated by comparing it with the traditional repetitive control, the EID-based PI control, the active-disturbance-rejection-control-based nonsingular terminal sliding mode control, as well as the sliding-mode-observer-based integral sliding mode control strategies for a farming vehicle.

Predefined-time trajectory tracking control of free-flying space manipulator subject to uncertainties and disturbances

This article addresses the predefined-time trajectory tracking control of free-flying space manipulator (FFSM) subject to uncertainties and disturbances. First, a predefined-time nonsingular terminal sliding mode (NTSM) controller is developed with the high insensitivity to uncertainties and strong robustness against disturbances. The generalized attitude and angular velocity tracking errors under the predefined-time NTSM controller can stabilize to zero in predefined time. Then, an adaptive version of the predefined-time NTSM controller is presented. The parametric adaptation mechanism is incorporated to identify the square of the upper bound of the lumped unknown item. Thus, the predefined-time adaptive NTSM (ANTSM) controller is smooth with no obvious chattering phenomenon and can maintain the high tracking accuracy simultaneously. The generalized attitude and angular velocity tracking errors under the predefined-time ANTSM controller can stabilize to the minor bounded regions around zero in predefined time. Simulations are provided to demonstrate the exploited controllers.

Research on switching control based sliding mode coordination strategy of hybrid electric vehicle

The transient mode transition from pure electric driving to hybrid driving in a hybrid electric vehicle (HEV) involves multiple stages, each characterized by different models and control objectives, demanding diverse levels of controller robustness and convergence performance. Addressing the complexities of multi-stage transitions in a parallel HEV, the paper proposes a dynamic coordination strategy based on the switched control system concept. Distinct sliding mode controllers (SMC) are designed for each transition stage, tailored to the specific characteristics of powertrain operation. At the engine starting and speed synchronization stages, the global integral sliding mode control (GISMC) is integrated with a novel anti-saturation reaching law to ensure the system resides in the sliding mode, enhancing global robustness. During the engine speed regulation stage, the nonsingular terminal sliding mode control (NTSMC) facilitates rapid engine speed tracking for reduced mode transition time. By combining GISMC for global robustness and NTSMC for accelerated convergence, the strategy is designed to enhance mode transition quality at different stages. Simulation results demonstrate a 7.3% reduction in mode transition time and a 60.7% decrease in jerk compared to the conventional SMC strategy. In hardware-in-the-loop tests, the proposed control strategy proves effective in improving mode transition quality.

Quadrotor UAV Trajectory Tracking Control Based on Adaptive Non-singular Terminal Sliding Mode

To address the issue of the quadrotor unmanned aerial vehicle (UAV) being susceptible to external disturbances and model uncertainties during flight, an adaptive non-singular terminal sliding mode controller with disturbance compensation is proposed. Firstly, the quadrotor UAV dynamic model considering system disturbances is constructed using the Newton-Euler method. Next, the trajectory tracking problem is transformed into a command tracking control problem for each channel. To enhance the adaptability and robustness of trajectory tracking for the quadrotor UAV, an adaptive law is designed to dynamically adjust the sliding mode surface gain. In addition, a disturbance observer is introduced to estimate and compensate for external disturbances and model uncertainties. Finally, the stability of the designed controller is proven through the Lyapunov function. Simulation results demonstrate that the proposed controller effectively enhances the disturbance resistance and trajectory tracking response speed of the quadrotor UAV.

Finite time disturbance observer based sliding mode control for PMSM with unknown disturbances

AbstractThis article proposes a finite time disturbance observer (FTDO) with modified non‐singular terminal sliding mode control (NTSMC) scheme for permanent magnet synchronous motor with unknown uncertainties such as system uncertainties and disturbances. A FTDO is used to estimate the unknown uncertainties and provides feed‐forward compensation in the control design. A novel fast sliding mode reaching law is developed, which can reduce the time to reach the sliding surface while the chattering phenomenon in the control signal. Then, by incorporating the FTDO into the modified sliding mode manifold, a finite time NTSMC scheme is presented. The developed control method can not only compensate the unknown uncertainties, but also achieve a finite time convergence. The finite time stability of the closed system under the presented control strategy is guaranteed via Lyapunov stability theory. The validity and feasibility of the presented control scheme are verified with simulations and experiments based on a motor driving system.

Anti-Offset Multicoil Underwater Wireless Power Transfer Based on a BP Neural Network

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs.

A fixed-time nonsingular terminal sliding mode control based on radial basis function neural network for vehicle active front steering system

In our work, a novel fixed-time nonsingular terminal sliding mode controller is developed to improve the control performance for the active front steering system of electric vehicles. First, the sliding mode surface and the sliding mode convergence law are constructed so that the sliding variable can be stabilized to zero at a fixed time. Moreover, the radial basis function neural network is introduced to estimate the systematic uncertainties, which can make the system have stronger anti-disturbance ability. The Lyapunov analysis also is given to verify the fixed-time stability of the whole system. The presented control scheme possesses two appealing advantages, including the strong robustness to perturbations and the fixed-time convergence to zero. Notably, the settling time is independent of the initial value while only related to the controller parameters. Through the joint simulation of CarSim and MATLAB, the given controller is compared with the conventional sliding mode controller and nonsingular terminal sliding mode controller to demonstrate the plausibility and validity of the presented control strategy.

Trajectory tracking of binocular vision system for picking robot based on fast non-singular terminal sliding mode control

This paper proposes a non-singular fast terminal sliding mode control method for a binocular active vision platform of a picking robot with unknown dynamics. The method uses radial basis function (RBF) neural networks to achieve trajectory tracking accuracy and enhance robustness against external interference. A non-singular fast terminal sliding mode controller is designed for the system’s convergence within a limited time. An adaptive neural network approximates the unknown nonlinear function of the dynamic model. Stability and finite-time convergence of the closed-loop system are established using Lyapunov theory. Experimental verification on the binocular vision platform demonstrates position and speed errors converging to the desired trajectory within 2 and 1 second, respectively. Moreover, when subjected to external interference, the position and velocity errors converge within 0.1 seconds. Simulation experiments confirm the method’s effectiveness in improving convergence speed, trajectory tracking accuracy, and robustness against external interference, while reducing system chattering.

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process.

Modeling and anti-sway control method for vertical lift-up/lay-down process of slender-beam payload

The existing crane control methods mainly aim at suppressing the oscillation of the payloads during the transportation process. However, during the vertical lift-up/lay-down process of the slender-beam payload (SBP), such as the installation of the wind turbine towers, rocket assembly, and pipeline laying, one end of the SBP is lifted up and the other end is in contact with the ground. In this situation, the external disturbance may cause the SBP to sway around the contact point. In this paper, a nonlinear three-dimensional dynamic model for the vertical lift-up/lay-down process of the SBP is established. On this basis, a control method for suppressing oscillation during the lift-up/lay-down process of the SBP is proposed by adopting the non-singular terminal sliding mode control (NTSMC). The stability of the NTSMC is proved based on the Lyapunov method. Simulations and experiments are carried out to verify the effectiveness of the proposed method.

Disturbance Observer-Based Fixed-Time Tracking Control for a Tilt Trirotor Unmanned Aerial Vehicle

This paper proposed a new fixed-time trajectory tracking control strategy based on the disturbance observer (DOB) for a tilt trirotor unmanned aerial vehicle (TTUAV) which is subject to unknown external disturbances. To facilitate the TTUAV's trajectory tracking controller design, the dynamic model of the TTUAV is divided into two subsystems, the position subsystem (outer-loop) and the attitude subsystem (inner-loop). For the dynamics of the TTUAV's outer-loop, the unknown external disturbances consist of the unknown wind disturbances, the unknown aerodynamic frictions forces, the lateral force generated by the rear servo and the states coupling between the outer-loop and the inner-loop. Then, a nonlinear DOB is designed to estimate the unknown external disturbances, and a nonsingular terminal sliding-mode controller (NTSMC) is further developed to drive the TTUAV's position tracking errors to zero in a fixed time. For the dynamics of the TTUAV's inner-loop, the unknown external disturbances are mainly introduced by the aerodynamic frictions torques. Then, a modified sliding mode controller is designed to reject the unknown external disturbances, and to ensure that the attitude tracking errors converge to zero exponentially. In addition, a composite stability analysis based on Lyapunov stability theory is provided to guarantee the fixed-time convergence of the closed-loop dynamics of the TTUAV. Finally, the results of the real-time experiments are presented to verify the effectiveness of the proposed control strategy.

Adaptive control of robotic manipulator with input deadzone and disturbances/uncertainties

This work address the problem of trajectory tracking for robotic manipulator while encountering external disturbances and internal uncertainties. Robotic manipulators have been widely used in various industrial and research applications due to their versatility and efficiency. The precise control of these manipulators is essential for their proper functioning. However, due to the presence of uncertainties and disturbances in the system, it is often challenging to achieve accurate control. The proposed controller is capable of handling the uncertainties and disturbances present in the system, making it robust and efficient for practical applications. The controller is designed based on the concept of second-order nonsingular terminal sliding mode control (SNTSMC), which ensures that the system tracks the desired trajectory accurately. The substitution of the sign function with the saturation function effectively reduces the chattering that is caused by the high-frequency switching element in the conventional SMC approach. By utilizing an adaptive technique, uncertainties can be effectively mitigated without relying on any prior knowledge of an upper limit. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The simulation results show the effectiveness of the proposed algorithm in terms of tracking accuracy and robustness.

A Robust Adaptive Non-Singular Terminal Sliding Mode Control: Application to an Upper-Limb Exoskeleton with Disturbances and Uncertain Dynamics

This paper presents a new control strategy for uncertain upper-limb exoskeleton systems, which are known to have high nonlinearities, unmodeled dynamics, and uncertainties. The proposed technique is based on the terminal sliding mode control algorithm and its nonsingular design method and incorporates an adaptive control approach to estimate the upper bounds of the unknown system uncertainties, which helps to improve the accuracy of the control and reduce the effects of disturbances. The stability of the proposed control strategy is confirmed using Lyapunov theory, and its effectiveness is tested on a two-degrees-of-freedom upper-limb exoskeleton. The results demonstrate that the proposed control scheme provides robust, fast, and finite-time convergence as well as an effective control approach capable of dealing with the disturbances and uncertainties that such systems are prone to.