Model-free Adaptive Sliding Mode Control Research Articles

Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application

Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application

Under-Actuated Motion Control of Haidou-1 ARV Using Data-Driven, Model-Free Adaptive Sliding Mode Control Method.

We propose a data-driven, model-free adaptive sliding mode control (MFASMC) approach to address the Haidou-1 ARV under-actuated motion control problem with uncertainties, including external disturbances and parameter perturbations. Firstly, we analyzed the two main difficulties in the motion control of Haidou-1 ARV. Secondly, in order to address these problems, a MFASMC control method was introduced. It is combined by a model-free adaptive control (MFAC) method and a sliding mode control (SMC) method. The main advantage of the MFAC method is that it relies only on the real-time measurement data of an ARV instead of any mathematical modeling information, and the SMC method guarantees the MFAC method's fast convergence and low overshooting. The proposed MFASMC control method can maneuver Haidou-1 ARV cruising at the desired forward speed, heading, and depth, even when the dynamic parameters of the ARV vary widely and external disturbances exist. It also addresses the problem of under-actuated motion control for the Haidou-1 ARV. Finally, the simulation results, including comparisons with a PID method and the MFAC method, demonstrate the effectiveness of our proposed method.

Cascade control algorithm for slip rate in a brake-by-wire system based on direct drive valve

To achieve a rapid response and precise control of braking hydraulic pressure, a brake-by-wire electro-hydraulic braking system based on a direct drive valve was designed. This system employs an electromagnetic linear actuator to drive the valve core directly, achieving swift adjustment of brake wheel cylinder hydraulic pressure. Given the strong coupling and non-linearity of the electromagnetic linear actuator, solely using a single-loop controller to control the slip rate can easily lead to weakened system performance. Hence, we proposed a cascade control algorithm for the brake-by-wire system, with an outer loop for slip rate control and an inner loop for direct drive valve position. The outer loop adopted a fuzzy PID control, while the inner loop adopted a model-free adaptive sliding mode control. By combining model-free adaptive control with a novel discrete exponential approach, we addressed the system’s non-linearity and unknown disturbances. A braking system test platform was constructed to verify the superior hydraulic tracking performance of this brake-by-wire system and to perform slip rate control performance analysis under different road conditions. Results demonstrated that compared to the fuzzy PID-MFAC algorithm, the proposed fuzzy PID-MFASMC control enhanced car slip rate control precision, and reduced both braking time and distance.

Model-Free Adaptive Sliding Mode Control Method for Unmanned Surface Vehicle Course Control

A novel data-driven-based adaptive sliding-mode control scheme is proposed for unmanned surface vehicle course control in the presence of disturbances. The proposed method utilizes the model-free adaptive control (MFAC) theory. On account of the unknown dynamics of the USV course system, the control scheme is only established by online input and output information of the system. Based on a model-free adaptive control scheme, the system disturbance estimation technique is applied to compensate for the disturbances in the established compact form dynamic linearization data model. The controller is designed and combined with the sliding mode method, and a second-order switching surface with a fast terminal sliding function is employed to achieve finite-time convergence. Furthermore, an analysis of the stability of the control system is provided. Finally, MATLAB simulations are implemented to verify the validity and robustness of the proposed control scheme by comparing it with PID and typical model-free adaptive sliding mode control.

Design of quadcopter attitude controller based on data-driven model-free adaptive sliding mode control

Design of quadcopter attitude controller based on data-driven model-free adaptive sliding mode control

Performance-based model-free adaptive finite-time control for shape memory alloy actuated swing platform

Shape memory alloy (SMA), a kind of smart material, can be used as an actuator in many fields; however, its strong nonlinearity and parameter uncertainty hinders its application in high-tracking accuracy tasks. This paper addresses the tracking control problem of the SMA actuated swing platform suffering from completely unknown nonlinear model information and prescribed finite-time error constraints. First, the equivalent dynamic linearization model of the swing platform is established, and the unknown disturbance is estimated and compensated by the extended state observer. Meanwhile, a novel discrete-time performance function is proposed, and the prescribed finite-time tracking error constraints are transformed into a new equivalent unconstrained task. Second, the model-free adaptive sliding mode controller is designed using the unconstrained error, and the complete control law, including sliding mode control law and equivalent control law, is derived. Third, the stability of the closed-up swing platform is guaranteed by employing the Lyapunov method. Finally, experiments reveal that the proposed method is preferable.

Data-Driven Model-Free Adaptive Sliding Mode Control Based on FFDL for Electric Multiple Units

The electric multiple units (EMUs) have become a very convenient and powerful means of transportation in our daily life. Safe and punctual trajectory tracking control is the key to improve the performance of the EMUs system, but it is difficult to realize due to the influence of environmental uncertainty, coupling and nonlinearity. In this paper, a model-free adaptive sliding mode control (MFASMC) method is proposed for the EMUs. This method can solve the dependence of the model-based control method on the train model and eliminate the influence of external disturbances on the robust performance of the system. In this method, the running process of the EMUs is equivalent to a full format dynamic linearization (FFDL) data model, and a model-free adaptive controller (MFAC) is designed based on the data model. Then, to reduce the influence of measurement disturbance and improve the robustness of the system, a discrete sliding mode control (SMC) algorithm is introduced. Furthermore, to prevent the control input from being too large, the parameter estimation error is introduced as an additional correction term of the algorithm. In the end, the simulation experiment is carried out with CRH380A EMUs as the object. Compared with the traditional MFAC and the traditional SMC, the speed tracking effect of each power unit of the MFASMC algorithm is more effective, the change of control force is stable, the acceleration meets the requirements of driving, and has a strong inhibitory effect on external disturbances.

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Vehicle longitudinal dynamics system has the characteristics of being strongly non-linear, time-varying, and multiple-perturbed, so, it is difficult to build the mathematical model accurately. The control algorithms, based on accurate mathematical model, can hardly achieve the ideal effect, but control methods, which merely adopt input/output data (I/O) of a system, provides a solution. In this paper, by means of combing model-free adaptive control (MFAC) and sliding-mode control (SMC), the model-free adaptive sliding mode control (MFASMC) method is proposed. By comparison with feedback-feedforward control method, the MFASMC method can better improve the control effect and anti-disturbance performance. Meanwhile, the stability of MFASMC method was proven mathematically. Besides, the parameters of MFASMC method were optimized using genetic algorithm. Results of simulation and HiL test shows that the MFASMC method has fast response, strong robustness and smooth output. It would be better to apply it to the longitudinal dynamics control of intelligent vehicles.

Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

The unmanned ground vehicle (UGV) is developing towards miniaturization, lightweight and intelligence. Path tracking and energy-saving control strategies are of great significance. In this study, a hierarchical control strategy is proposed to realize the path tracking control of six-wheel independent drive UGV (6WID UGV) based on differential steering, where actuator fault and energy-saving are considered. Firstly, a model free adaptive sliding mode control (MFASMC) method is utilized in the upper controller to estimate the required yaw moment. Then, in the lower controller, the seeker optimization algorithm (SOA) is utilized to achieve the torque distribution, realize the required yaw moment and longitudinal force, and reduce the energy consumption in the process of path tracking. The fault-tolerant control is considered in the torque distribution strategy to ensure the stability of 6WID UGV in case of motor execution fault. Finally, the effectiveness of the proposed scheme is verified by simulation under various operating conditions. The results show that the proposed method has better control performance, and the characteristics of energy-saving are optimized while ensuring the vehicle stability.

Model-Free Adaptive Sliding Mode Control-Based Active Chatter Suppression by Spindle Speed Variation

Abstract This paper presents a novel model-free adaptive sliding mode control (MFA-SMC) algorithm, which is employed to actively adjust the amplitude and frequency of spindle speed variation (SSV) for chatter suppression in turning. The SSV technique is effective for mitigating regenerative chatter, which however is not widely applied due to its poor adaptability to time-varying characteristics of machining dynamics and cutting conditions. The online adjustment of SSV parameters has been reported in previous works, whereas the proportional integral differential (PID)-type controller used cannot compensate for the interactions when abruptly changing the reference input and the parameter tuning procedure consumes much time. The proposed method integrates the model-free adaptive control (MFAC) with the global sliding mode control (GSMC). The method is data-driven and only dependent on the measured input/output data of the cutting process in this paper, which are the normalized wavelet packet entropy and SSV parameters, respectively. The bounded-input bounded-output stability and the tracking error convergence of the proposed control algorithm are guaranteed by theoretical analysis. The effectiveness of the proposed chatter suppression method is investigated with numerical simulations. Finally, experimental results demonstrate that chatter vibration under different cutting conditions can be effectively mitigated by the proposed method.

Data-driven model-free adaptive sliding mode control for electromagnetic linear actuator

A data-driven model-free adaptive sliding mode control (MFASMC) method is proposed to tackle the problems of inaccurate model and time-varying parameters of a micro electro-magnetic linear actuator system, considering the dependence of existing model-based control methods on the system dynamics model and the impact of unmodeled dynamics on the control performance. Firstly, the pseudo-gradient concept in the model-free adaptive control (MFAC) framework is used to transform the electromagnetic linear actuator dynamics model, which is difficult to obtain parameters accurately, into a full-format dynamic linearized data model, the dependence of the controller on the electromagnetic linear actuator model is reduced. To compensate for the effects of unknown perturbations, an improved discrete sliding mode exponential convergence law is introduced to derive a new composite control algorithm based on the pseudo partial derivative estimator, which improves the robustness of the control system. Then, the convergence of the control error and the stability of the system are demonstrated by theoretical analysis. The results show that the proposed MFASMC reduces the 10 mm step response time of the electromagnetic linear actuator by 46.3% and 25.3% compared with proportional-integral-derivative control (PID) and MFAC. The phase lag time and root-mean-square error of MFASMC under sinusoidal conditions are 0.8 ms, 0.178 mm respectively, and the steady-state error does not exceed 0.05 mm. The experimental and simulation results keep the error within 5%, the proposed control algorithm has good trajectory tracking response speed and control accuracy, and has good robustness to system uncertainty and external force disturbance.

Data-driven model-free adaptive fractional-order sliding mode control for the SMA actuator with prescribed performance

In this paper, a novel data-driven model-free adaptive fractional-order sliding mode controller with prescribed performance is proposed for the shape memory alloy (SMA) actuator. Due to the strong asymmetric saturated hysteresis nonlinear characteristics of the SMA actuators, it is not easy to establish an accurate model and develop an effective controller. Therefore, we present a controller without using the model of the SMA actuators. In other words, the proposed controller depends merely on the input/output (I/O) data of the SMA actuators. To obtain the reasonable compensation for hysteresis, enhance the noise robustness of the controller, and reduce the chattering, a fractional-order sliding mode controller with memory characteristics is employed to improve the performance of the controller. In addition, the prescribed performance control (PPC) strategy is introduced in our work to guarantee the tracking errors converge to a sufficiently small boundary and the convergence rate is not less than a predetermined value which are significant and considerable in practical engineering applications of the SMA actuator. Finally, experiments are carried out, and results reveal the effectiveness and success of the proposed controller. Comparisons with the classical Proportional Integral Differential (PID), model-free adaptive control (MFAC), and model-free adaptive sliding mode control (MFAC-SMC) are also performed.

Prescribed Performance Control for the Upper-Limb Exoskeleton System in Passive Rehabilitation Training Tasks

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.

Design of a model-free adaptive sliding mode control to synchronize chaotic fractional-order systems with input saturation: An application in secure communications

In this work, a model-free adaptive sliding mode control (ASMC) methodology is proposed for synchronization of chaotic fractional-order systems (FOSs) with input saturation. Based on the frequency distributed model and the non-integer version of the Lyapunov stability theorem, a model-free ASMC method is designed to overcome the chaotic behavior of the FOSs. The control inputs are free from the nonlinear-linear dynamical terms of the system because of utilizing the boundedness feature of the states of chaotic FOSs. Moreover, a new medical image encryption scheme is tentatively proposed according to our synchronization method, and its effectiveness is verified by numerical simulations. Furthermore, the performance and security analyses are given to confirm the superiority of the proposed encryption scheme, including statistical analysis, key space analysis, differential attack analysis, and time performance analysis.

Model-free adaptive sliding mode control with adjustable funnel boundary for robot manipulators with uncertainties.

Considering that the nominal dynamics model or numerous parameters of robotics are usually unsuitable for real applications, a model-free adaptive sliding mode control with an adjustable funnel boundary is proposed for robot manipulators with uncertainties. First, time delay estimation (TDE) technique is utilized to estimate the unknown dynamics of the control system, which ensures an attractive model-free advantage. Furthermore, a modified funnel function is introduced to transform the trajectory tracking error fall within an adjustable funnel boundary strictly. Then, based on the transformed error variable, a novel funnel nonsingular fast terminal sliding mode control scheme is developed to enhance the transient and steady-state tracking performance of the closed-loop control system. To cope with the TDE error, an adaptive update method is designed with only one adaptive parameter, which is adaptively tuned according to the sliding surface. Finally, the simulation and experimental results are presented to illustrate the superiority and high-precision tracking performance of the proposed approach.

Prescribed performance model-free adaptive sliding mode control of a shape memory alloy actuated system

This paper concentrates on a simple and robust control method for the discrete time nonlinear systems to fulfill the requirement of predefined accuracy. A sliding mode control method is designed by introducing equivalent dynamic linearization technique according to the input/output (I/O) information merely. A square-root type error transformation method is presented for the tracking error to be restricted within a preassigned zone. The performance of presented control method is demonstrated through experiments on a nonlinear system. Experiment results show that the presented control method has a superior tracking accuracy compared with PID controller and model-free adaptive control (MFAC).

Linearized Bregman iteration based model‐free adaptive sliding mode control for a class of non‐linear systems

There is a growing demand for robust data-driven control methods particularly for industrial process control. This paper presents a new model-free adaptive sliding mode control approach for a class of discrete-time, multiple input and multiple output non-linear systems. The proposed methodology seeks to address issues with the computation of inverse matrices and problems with singularity in existing methods while at the same time seeking to enhance robustness. A Majorization–Minimization technique and the L1 norm are used within the proposed optimization and an online iterative approach is described for update of the control law. The closed-loop system response is proved to be stable. The effectiveness of the proposed control is validated by extensive simulation and also experimental results, with the performance obtained by the proposed approach being compared throughout with a well-known approach from the established literature.

Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

Model-free adaptive command-filtered-backstepping sliding mode control for discrete-time high-order nonlinear systems

A new model-free adaptive sliding mode control method is proposed to solve some control issues in a class of discrete-time high-order nonlinear systems whose dynamics are unknown in this paper. In order to simplify the nonlinear system, a new concept called pseudo-partial derivative matrix (PPDM) is introduced to facilitate the nonlinear system by using a compact form dynamic linearization approach. Therefore, to estimate the PPDM, the identified adaptive observer is constructed using the input/output data from each subsystem of the higher order discrete time system. Taking into account the amplitude and rate constraints of real-world input signals, an anti-windup dynamic compensator is developed. Furthermore, the problems of complexity explosion and non-causality are solved by using discrete-time command filters while designing the controller. It has been shown that all the control variables in closed-loop systems are uniformly-ultimately bounded. An example of a linear induction motor taking into account the end effects is used to verify the effectiveness of the presented control strategy through simulations and experiments.

Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes

In this paper, a new data-driven model-free adaptive sliding mode control is discussed for discrete-time nonlinear processes with tracking error constraint. Furthermore, a novel transformed error method together with a new sliding mode control framework is studied to ensure the tracking error converges to a predefined region all the time. Meanwhile, the proposed controller can guarantee that the convergence rate is not smaller than a pre-assigned constant and maximum steady-state error is less than an arbitrarily small prespecified value all the time, which is more effective in the complex industrial processes. The effectiveness of the considered strategy is validated by the simulation examples.