Conventional Sliding Mode Control Research Articles

Particle swarm optimization based sliding mode control for maximum power point tracking in solar PV systems

One of the most significant renewable energies is photovoltaic (PV) energy, however it has a low efficiency due to its variable maximum power point that depends on weather conditions. In order to guarantee the system's best performance, intelligent algorithms can effectively track this point in real-time utilizing the maximum power point tracking (MPPT) method. Consequently, it is crucial to maximize the use of the solar energy that has been captured as well as the PV system's generated electricity. Variations in solar irradiance affects the amount of electric energy obtained from solar arrays. For efficient extraction of electricity from solar PV systems, MPPT algorithms are required. Sliding mode control (SMC) can be used in the control of nonlinear systems. However, the effectiveness of SMC can be improved by the choice of the sliding coefficients. In this paper, optimal search using particle swarm optimization (PSO) is used in the design of the sliding manifold. Results obtained via simulations showed that MPPT tracking efficiencies obtained for the PSO based SMC and the conventional SMC are 99.65% and 96.79% respectively. That means, PSO based SMC is 2.86% better than conventional SMC.

Super-twisting sliding mode controller for energy storage system of a novel multisource hybrid electric vehicle: Simulation and hardware validation

The rapid depletion of fossil fuels, oil, and natural gas due to their excessive use is a source of motivation for finding alternative solutions. Global warming is also an important issue caused by the emission of harmful gases. Consequently, extensive research has been conducted on Fuel Cell-based Hybrid Electric Vehicles (FCEVs) to address the twin challenges of resource depletion and harmful emissions. This study presents a novel model comprising of four sources i.e. Fuel Cell (FC), Photo Voltaic panel (PV), battery, and Supercapacitor (SC). The Proton exchange membrane fuel cell (PEMFC) in which hydrogen is used as fuel is the primary source whereas the other three sources act as secondary sources. These sources are interconnected via DC converters to a DC bus. To regulate this Hybrid Energy Storage System (HESS), a Super-Twisting Sliding Mode Controller (ST-SMC) has been developed, ensuring global stability through the application of Lyapunov criteria. In addition, the proposed controller is compared with a conventional Sliding Mode Controller (SMC) and Integral Sliding Mode Controller (ISMC). Both simulation (MATLAB/Simulink 2021b) and hardware tests (MicroLabBox dSPACE RTI1202) confirm the system's stability, resilience, and efficient functionality across various dynamic scenarios. Comparative analysis between the three demonstrates the superiority of ST-SMC over the SMC and ISMC, as verified by the results.

Terminal Synergetic Control for Plate Heat Exchanger

Terminal synergetic control (TSC) is proposed as a control strategy for the temperature management of a plate heat exchanger. The controller is designed by incorporating a selected macro variable with a time-varying sliding surface. The primary objective is to maintain precise control over the outlet temperature of the cold water. To assess the convergence characteristics of the newly proposed TSC approach, the simulation results achieved using TSC featuring a time-varying macro variable are compared to those obtained from the conventional synergetic control (SC) method. With an appropriate macro variable, the simulation results indicate a notable improvement in the convergence rate provided by our designed TSC method, compared to the conventional one. The desirable property of control input, the chattering-free condition, achieved by both TSC and SC approaches emphasizes the advantage of the synergetic control-based techniques over the conventional sliding mode controller. In conclusion, synergetic control-based techniques offer superior potential solutions for nonlinear feedback control problems.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

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.

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.

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.

Model reference sliding mode control for two-degree-of-freedom electro-hydraulic shaking table

The multi-axis electro-hydraulic shaking table (EHST) is capable of simulating complex vibration environments to assess product reliability under vibrational excitation. The random vibration control method plays a crucial role in simulating these environments. However, the unmolded disturbances and the nonlinearity of the system have a significant impact on the control accuracy of the EHST. Sliding mode control (SMC) can be used in single-input single-output electro-hydraulic servo systems (EHSS) to attenuate the influence of disturbance forces. The conventional SMC is not suitable for acceleration random vibration control of the multi-axis EHST because the coupling between the axes and the actuators and the null bias of the servo valve are not taken into account. A model reference sliding mode control (MRSMC) is presented in this paper based on the nonlinear dynamic model of the EHST. The MRSMC designed based on the backstepping method and Lyapunov theory is used to compensate for the disturbance forces and the null bias of the servo valve. Experimental acceleration random vibration responses show that the MRSMC enhances the accuracy of tracking the acceleration random signal.

Performance Comparison of a DC–DC Boost Converter With Conventional and Non-linear SMC Controller in Dual Loop Structure

Generally, the control system confronts various control techniques in the dual-loop control concept. Most of the studies deal with two different controllers in their loops, which increases the problem of designing two distinct procedures with lengthened calculations. In this article, different control techniques like conventional proportional integral (PI), proportional integral derivative (PID), and nonlinear sliding mode control (SMC) have been discussed, and the implementation of SMC in the Dual loop control of a DC–DC Boost converter is presented. The converter’s dynamic behavior is observed by driving their analytical equations. The effective controller for the proposed converter is ascertained by their comparative analysis. The proposed concept is validated with MATLAB/Simulink and a hardware prototype. The controller’s response is compared for various disturbances. Here, dual-loop SMC is put forward in both loops and its competence is confirmed with a minimal settling period of 0.005 sec and a percentage overshoot of 0.2% during the startup conditions, whereas during the line disturbances a minimum peak time of 0.0001 sec is achieved with a settling time of 0.025 sec. Additionally, a nil percentage overshoot and a settling time of 0.02 sec with a 0.001 sec peak time are attained during the load variations. Furthermore, a settling time of 0.01 sec and a percentage overshoot of 0.8% is reached during the reference voltage variations. From the numerical analysis, it is contemplated that an appropriate selection of SMC controllers in both the outer and inner loop results in optimal performance of the converter during the closed loop operation. Finally, the SMC + SMC controller exhibits superior controlling action compared to PI + PI and PID + PID controllers.

Trajectory tracking for non-holonomic mobile robots: A comparison of sliding mode control approaches

This paper implements and compares the performance of three controllers based on Sliding Mode Control (SMC) applied to the motion control of mobile robots. The controllers include a standard SMC controller and two variations, namely Dynamic Sliding Mode Control (D-SMC) and Dual Sliding Mode Control (DM-SMC). The three controllers differ from conventional SMC approaches since they are designed based on a generic reduced-order empirical model that can represent any system that exhibits behavior akin to a First-Order Plus Delay Time (FOPDT) system. In this study, the comparison of controllers focuses on the Trajectory Tracking Problem (TTP) of a Nonholonomic Mobile Robot (NMR). Performance indices are employed to quantify the controllers' results across three trajectory types. Simulation results evidence that if a smooth curvature trajectory is tracked, the best performance (in terms of minimizing trajectory tracking error and controller effort) can be obtained by using a DM-SMC. On the other hand, if there are discontinuities in the curvature, the results suggest that a better alternative is the D-SMC since it compensates for abrupt changes as in the case of tracking a square trajectory. Overall, the findings from this work demonstrated that the use of simplified empirical models to design SMC-based motion controllers can be successfully applied to solve the TTP of NMR.

Design of a sliding mode controller for suppressing coupled axial & torsional vibrations in horizontal drill strings using Extended Kalman Filter

Nowadays, drill string technologies are among the leading fields in the industries of oil and gas extraction and are widely used in the construction of subway tunnels. Due to their significant applications, horizontal drill strings have attracted the attention of researchers. Various vibrations that occur during the drilling process are also sources of system uncertainty. To solve the mentioned problems, in this paper, a coupled axial-torsional model is considered for a horizontal drill string, and all the forces and torques that are involved in the drilling process, are modeled. All the primary modes of the axial and torsional vibrations are calculated using a numerical solution. For solving the PDE equations of motion, the mode summation method is used, and in order to determine the minimum necessary modes, mode convergence analysis has been accomplished for this coupled axial-torsional plant for the first time. In the next step, a novel nonlinear sliding mode controller (SMC) is designed for this plant to eliminate the coupled axial and torsional vibrations, using only two control inputs. As the proposed controller requires the state variables, and the sensors are incapable of recording all the state variables, an Extended Kalman Filter (EKF) is designed to estimate all the 12 state variables, which is one of the novelties of this research. Eventually, a comparison analysis with a conventional SMC and a Pole-Placement controller is provided to ensure the performance of the proposed controller. Moreover, unmodelled dynamics and parametric uncertainties are considered to ensure the robustness of the proposed controller.

Perfect Tracking Control of Linear Sliders Using Sliding Mode Control with Uncertainty Estimation Mechanism

This paper aims to achieve precise position control of a stage used in semiconductor exposure apparatus. The demand for smart devices, such as smartphones, is rapidly expanding, and their performance is expected to continue to improve. To manufacture these devices, it is necessary to miniaturize semiconductor devices and improve productivity. The precise control of semiconductor exposure apparatus is important for the manufacture of ultra-small semiconductor devices. The stage of semiconductor exposure apparatus uses a linear motor, and this paper performs high-precision perfect tracking control of this stage. Perfect tracking control is a control method that always follows the command value while the command value changes moment by moment, and requires high accuracy. In high-precision positioning, uncertainty in the stage model has a significant impact. Therefore, this paper proposes a method to reduce tracking errors due to the influence of uncertainty by performing uncertainty compensation using sliding mode control with the estimated value of uncertainty. The estimation of uncertainty uses a method that combines Kernel LMS with an observer. Instead of the widely used Gaussian kernel, this paper uses a generalized Gaussian kernel that allows for finer parameter settings. Furthermore, this paper proposes a method to adaptively optimize the shape parameter of the generalized Gaussian kernel. Our simulations and experiments confirm that the proposed method improves tracking performance compared to conventional sliding mode control.

Online neural sliding mode control with guaranteed stability

Model-based control systems allow engineers to leverage known system information in controllers, often resulting in improved control accuracy or robustness. However, these system models can change over time, be difficult to obtain for complex systems, or fail to capture higher-order effects in the system. In this work, we use a highly robust model-based control methodology, sliding mode control, without the need for an a priori model. We use a neural network to learn an approximate system model fully online, with no pretraining. The neural network term is found to increase control accuracy while alleviating chatter, the primary drawback of conventional sliding mode control. The controller is verified by simulating a non-planar 3 degree-of-freedom robotic arm picking up a load, and the proposed controller is shown to outperform conventional model-based sliding mode control, especially when the built-in load margin on the end effector in the conventional model-based controller is exceeded.

Trajectory Tracking Control of Remotely Operated Vehicles via a Fast-Sliding Mode Controller with a Fixed-Time Disturbance Observer

Time-varying nonlinear external disturbances, as well as uncertainties in model and hydrodynamic parameters, make remotely operated vehicles (ROVs) trajectory tracking control complex and difficult. To solve this problem, this paper proposes a fast sliding mode controller with a fixed-time disturbance observer (FSMC-FDO), which consists of a sliding mode controller based on a fast reaching law and a novel fixed-time disturbance observer. The FSMC can solve the contradiction between system response time and chatter amplitude in sliding mode control. The FDO can compensate for time-varying external disturbances. The Lyapunov theory is used to prove the stability of the entire control scheme. Simulation results show that FSMC-FDO exhibits a good trajectory tracking performance with a better robustness than the conventional sliding mode control (CSMC) on the basis of exponential reaching law (ERL), while significantly reducing chatter.

Fixed-Time-Convergent Sliding Mode Control with Sliding Mode Observer for PMSM Speed Regulation.

This paper focuses on the speed control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. Conventional sliding mode control (CSMC) can only converge asymptotically in the infinite domain and will cause unacceptable sliding mode chattering. To improve the performance of the PMSM speed loop in terms of response speed, tracking accuracy, and robustness, a hybrid control strategy for a fixed-time-convergent sliding mode controller (FSMC) with a fixed-time-convergent sliding mode observer (FSMO) is proposed for PMSM speed regulation using the fixed-time control theory. Firstly, the FSMC is proposed to improve the convergence speed and robustness of the speed loop, which can converge to the origin within a fixed time independent of the initial conditions. Then, the FSMO is used as a compensator to further enhance the robustness of the speed loop and attenuate sliding mode chattering. Finally, simulation and experimental results show that the proposed method can effectively improve the dynamic performance and robustness of the PMSM speed control system.

Robust sliding mode control for robot manipulators with analysis on trade‐off between reaching time and L∞ gain

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach.

Performance analysis of vehicle magnetorheological semi-active air suspension based on S-QFSMC control

The performance of the suspension is a crucial criterion for evaluating both vehicle handling and passenger comfort. To enhance suspension performance, this study proposes the design of a Quantum Genetic Fuzzy Sliding Mode Controller (S-QFSMC) based on the Smith predictor estimator, building upon the foundation of the vehicle magneto-rheological semi-active air suspension. According to the physical model of the vehicle suspension, a mechanical model of a quarter-vehicle magneto-rheological semi-active air suspension with time delay is established. On this basis, a conventional sliding mode controller is designed, and quantum genetic algorithm and fuzzy control principles are employed to optimize the chattering issue associated with sliding mode control. The Smith predictor estimator is utilized to effectively compensate for the time delay in the suspension system. Subsequently, a simulation analysis of the vehicle suspension performance is conducted. The results indicate that, compared to passive suspension control, both the QFSMC controller and the S-QFSMC controller improve the suspension performance, with the S-QFSMC controller exhibiting superior comprehensive improvement. This validates the effectiveness of the designed controllers.

Robust trajectory tracking of a 3-DOF robotic arm using a Super-Twisting Fast finite time Non-singular Terminal Sliding Mode Control in the presence of perturbations

Extensive research has focused on enhancing the efficiency and stability of robotic arms. Sliding mode control (SMC) is commonly used in industrial robots due to its robustness and simplicity. However, SMC approaches have challenges such as chattering and slow convergence rates which can compromise tracking accuracy. To address these issues, this paper proposes a novel Super-Twisting Fast Non-singular Terminal Sliding Mode Control (ST-FNTSMC) strategy for a 3-DOF arm robot. The proposed approach significantly improves trajectory tracking accuracy, robustness, and convergence time and eliminates chattering. The proposed controller was tested in the presence of model mismatches and external disturbances. The super-twisting methodology avoided chattering effects and increased robustness against perturbations. Two Lyapunov functions ensure closed system stability and finite-time convergence. The designed ST-FNTSMC controller is implemented in real-time using a Smart Man Robot manipulator. Its performance is compared to other sliding mode controllers, such as conventional PID Sliding Mode Control (PID-SMC), Non-singular Terminal Sliding Mode Control (NTSMC), and Fast Non-singular Terminal Sliding Mode Control (FNTSMC). Experimental results demonstrate the superior performance of the proposed controller, highlighting its effectiveness in improving the efficiency and stability of industrial robots.

Trajectory Following Control of an Unmanned Vehicle for Marine Environment Sensing.

An autonomous surface vehicle is indispensable for sensing of marine environments owing to its challenging and dynamic conditions. To accomplish this task, the vehicle has to navigate through a desired trajectory. However, due to the complexity and dynamic nature of a marine environment affected by factors such as ocean currents, waves, and wind, a robust controller is of paramount importance for maintaining the vehicle along the desired trajectory by minimizing the trajectory error. To this end, in this study, we propose a robust discrete-time super-twisting second-order sliding mode controller (DSTA). Besides, this control method effectively suppresses the chattering effect. To start with, the vehicle's model is discretized using an integral approximation with nonlinear terms including environmental disturbances treated as perturbation terms. Then, the perturbation is estimated using a time delay estimator (TDE), which further enhances the robustness of the proposed method and allows us to choose smaller controller gains. Moreover, we employ a genetic algorithm (GA) to tune the controller gains based on a quadratic cost function that considers the tracking error and control energy. The stability of the proposed sliding mode controller (SMC) is rigorously demonstrated using a Lyapunov approach. The controller is implemented using the Simulink® software. Finally, a conventional discrete-time SMC based on the reaching law (DSMR) and a heuristically tuned DSTA controller are used as benchmarks to compare the tracking accuracy and chattering attenuation capability of the proposed GA based DSTA (GA-DSTA). Simulation results are presented both with or without external disturbances. The simulation results demonstrate that the proposed controller drives the vehicle along the desired trajectory successfully and outperforms the other two controllers.

Distributed Leader Follower Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter

This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.