Sliding Mode Control Controller Research Articles

Performance improvement of manipulator actuated by pneumatic artificial muscles based on synergetic control and social spider optimisation algorithm

The manipulator actuated by pneumatic artificial muscles (PAM) is a widely used type of robotic arm in industrial automation. However, its performance can be limited by non-linear dynamics and uncertainties in the system. To overcome these limitations, this paper proposes a synergetic control strategy (SACT) to improve the performance of the SACT, a social spider optimisation algorithm (SSO) has been suggested for adjusting its parameters. To verify the performance of a PAM-actuated manipulator based on an optimal SACT controller, a computer simulation study was conducted using MATLAB software. Moreover, a comparison study between the optimal synergetic algorithm control theory and the optimal sliding mode controller (SMC) has been made in terms of robustness and transient behaviour characteristics. The provided simulation results have shown that the SACT controller exhibited quicker convergence towards the desired trajectory and maintained a lower steadystate error as compared to the SMC controller. Additionally, the SACT controller demonstrated more resilience to variations in parameters and showed more robust characteristics.

The adaptive sliding mode control based on U–K theory for foot trajectory following of hexapod robot

This paper addresses the control of a hexapod robot’s foot trajectory tracking using an adaptive sliding mode control (SMC) approach based on Udwadia–Kalaba theory. Unlike the traditional control approach, the Udwadia–Kalaba theory allows for the transformation of the hexapod robot foot trajectory tracking control problem into a system servo binding solution problem. This method eliminates the requirement to linearize the nonlinear system. The system may contain uncertainties, such as less-than-ideal initial circumstances and vibration disturbances during operation, which have an impact on the control precision due to mistakes in modeling, measurements, and changes in operational states. To deal with the uncertainty, the adaptive SMC controller was developed. The stability analysis is carried out using the second Lyapunov function method. By modeling the hexapod robot’s legs and running simulations to compare the simulated tracking route to the planned trajectory, the precision and stability of the control approach suggested in this study are finally demonstrated, and by comparing with the simulation results of adaptive robust control strategy, the advantages of RBF neural network adaptive SMC strategy are obtained.

Review of Deployment Controllers for Space Tethered System

This review article investigates the performance of newly designed controllers for Tethered Space Systems (TSS) deployment, comparing them to past and current research. Specifically, the study delves into the PD type controller, known for its simplicity and early development. While PD controllers adequately manage non-coplanar scenarios with external perturbations, they are limited to linearized cases. In contrast, advanced controllers like Sliding Mode Control (SMC) effectively handle TSS’s highly nonlinear dynamics due to their robust nature. The study introduces a sigma function, derived from Djebli’s literature, to regulate tether deployment rate and length across all controllers. Simulation results demonstrate the feasibility of controlling out-of-plane libration angles solely through the tether tension. Among the controllers tested, the advanced sigma-SMC exhibits superior accuracy during deployment, while the modified SMC deploys the tether fastest albeit with significant steady-state errors and deflection angles. Numerical results show that the original SMC controller performs well as the most fuel-efficient option. This comparison focuses solely on tether deployment with J2 and gravity-gradient perturbations, offering a foundation for further exploration into TSS missions, including factors like aerodynamic drag, solar radiation, third body perturbations, tether flexibility, and retrieval phase control, tailored to specific mission requirements.

Integral sliding-mode based interval type-2 fuzzy control for efficient networked nonlinear active suspension systems with input uncertainty

An event-triggered integral sliding mode control (ETISMC) approach for the networked nonlinear active suspension systems (ASSs) via interval type-2 (IT-2) T-S fuzzy logic is investigated in this paper. Firstly, in order to deal with the uncertainty in system masses, the nonlinearity of the suspension spring stiffness and damping coefficient, and the unknown actuator nonlinearity simultaneously, an ASSs model based on the IT-2 T-S fuzzy method is constructed. Secondly, a discrete event-triggered method is designed to process the sampled data and reduce the usage of vehicle CAN network communication resources. Thirdly, the H infinity robust method is used to handle the road disturbance and the sliding mode control (SMC) is adopted to deal with the actuator input uncertainty. To better integrate the SMC and event-triggered control (ETC), an ETISMC approach is explored for the ASSs. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed strategy in this work.

Research on the new control strategy of the three-phase VIENNA rectifier

Abstract The VIENNA rectifier has long been used in applications such as charging piles, and the theoretical research on its control strategy is enumerated. Compared with other circuit components, the VIENNA rectifier has fewer power components and a higher power factor, so it is applied to more application scenarios. The traditional PI control has a long start-up response time and a high overshoot, while the sliding mode control (SMC) has excellent control performance for nonlinear systems. In my article, the control mode of the VIENNA rectifier is analyzed mathematically, SMC takes controls of the current inner loop, and a new exponential reaching rate is put forward the at first improve the control process. Finally, a complete system simulation model is built to prove the superiority of the VIENNA rectifier based on an optimized SMC control strategy for each control target and the corresponding control strategy. The results show that the control effect is obviously optimized by using this method.

Distributed Improved RILOS Guidance-Based Formation Control of Underactuated ASVs for Cooperative Maritime Search

A distributed improved robust integral line-of-sight (RILOS) guidance-based sliding mode controller is designed for multiple underactuated autonomous surface vessels (ASVs) to perform cooperative maritime search operations. First, a parallel circle search pattern is designed based on the detection range of ASVs, which can provide the reference formation shape. Second, an improved RILOS method is presented by introducing an integral term into the improved robust LOS method, which can counteract the disadvantageous effect of the unknown sideslip angle and kinematic discrepancy simultaneously. Third, distributed improved RILOS guidance is presented by integrating the extended second-order consensus algorithm into the improved RILOS method; then, the desired heading angle and desired velocity are generated for the control system simultaneously. Finally, the fuzzy logic system is integrated into the sliding mode control (SMC) method to approximate the unknown nonlinear function; then, a distributed improved RILOS guidance-based SMC controller is presented for multiple ASVs. The close-loop signals are proved to be stable by the Lyapunov theory. The effectiveness of the presented method is verified by multiple simulations.

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

Robust Adaptive Sliding Mode Control Using Stochastic Gradient Descent for Robot Arm Manipulator Trajectory Tracking

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ1 and 0.13387 for θ2. In comparison, the STA controller yields RMSE values of 0.1953 for θ1 and 0.1953 for θ2, while the SMC controller results in RMSE values of 0.24505 for θ1 and 0.29112 for θ2. Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD’s effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators.

FPGA Implementation of Sliding Mode Control and Proportional-Integral-Derivative Controllers for a DC–DC Buck Converter

DC–DC buck converters have been designed by incorporating different control stages to drive the switches. Among the most commonly used controllers, the sliding mode control (SMC) and proportional-integral-derivative (PID) controller have shown advantages in accomplishing fast slew rate, reducing settling time and mitigating overshoot. The proposed work introduces the implementation of both SMC and PID controllers by using the field-programmable gate array (FPGA) device. The FPGA is chosen to exploit its main advantage for fast verification and prototyping of the controllers. In this manner, a DC–DC buck converter is emulated on an FPGA by applying an explicit multi-step numerical method. The SMC controller is synthesized into the FPGA by using a signum function, and the PID is synthesized by applying the difference quotient method to approximate the derivative action, and the second-order Adams–Bashforth method to approximate the integral action. The FPGA synthesis of the converter and controllers is performed by designing digital blocks using computer arithmetic of 32 and 64 bits, in fixed-point format. The experimental results are shown on an oscilloscope by using a digital-to-analog converter to observe the voltage regulation generated by the SMC and PID controllers on the DC–DC buck converter.

PENGEMBANGAN KENDALI TERBANG BERBASIS SLIDING MODE CONTROL DENGAN ELIMINASI CHATTERING PADA QUADROTOR UNTUK MENGATASI GANGGUAN ANGIN TAK TENTU DI KAWASAN JAWA BARAT

Quadrotors are intrinsically unstable. If there is a sudden wind disturbance, this wind disturbance will be amplified so that the quadrotor's attitude becomes uncontrollable. West Java is one of the regions in Indonesia that has quite high wind disturbances. The recorded daily wind disturbance reached 6.99 m/s with a maximum value of 9.95 m/s. In this research, a control system based on the sliding mode control (SMC) algorithm is used. The advantage of this control algorithm is its ability to compensate for uncertain disturbances. This research investigates the implementation of the SMC control into a quadrotor by incorporating random wind disturbance aspects into the model. The research was carried out numerically with simulations using MATLAB/Simulink, with the position variables being the main focus. Quadrotor equipped with SMC has a lower mean error value for the desired position relative to the real position. This shows that the SMC control algorithm that has been created is able to properly overcome random wind disturbances that occur in the West Java area.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

Performance analysis of PID and SMC for PEMFC-based grid integrated system using nine switch converter - A comparative study

Hydrogen fuel cells are gaining popularity as a reliable and efficient sustainable energy source. They have found uses in electric vehicles and microgrid applications. A grid-connected microgrid system with a 60 kW proton exchange membrane fuel cell (PEMFC) is proposed. A PEM fuel cell-based generating unit is studied, using a sliding mode controller (SMC) in the control scheme and a nine-switch converter (NSC) for the system integration. The NSC is used to integrate the FC system with the utility grid and controls the system's power flow. This study is focused on the performance evaluation of the proportional-integral-derivative (PID) and SMC controllers. The controllers' gains are tuned using an improved arithmetic optimization method (IAOA). The article also emphasizes improving system performance and stability by incorporating SMC in the control scheme. The suggested system is exposed to different source and load-side disturbances for performance evaluation. Matlab/Simulink is used to model the proposed system and validated by the real-time OPAL-RT 4510.

TRAJECTORY TRACKING CONTROL OF A TWO WHEELED SELF-BALANCING ROBOT BY USING SLIDING MODE CONTROL

Two-Wheeled Self-Balancing Robots are widely used in various fields today. These systems have a highly unstable nature due to their underactuated structures. On the other hand, parameter uncertainties and external disturbances significantly affect their control performance. The best way to deal with parameter uncertainties that can easily lead controllers to instability is to use robust control methods. Dealing with these uncertainties is particularly crucial in control of underactuated and unstable systems such as Two-Wheeled Self-Balancing Robots. In this study, trajectory tracking control of a two wheeled self-balancing robot by using Sliding Mode Control (SMC) was realized. The chattering problem inherent in the SMC method was eliminated by employing tangent hyperbolic (tanh) switching function instead of signum function. The performance of the SMC controller has been examined under five different cases including external disturbance and various parameter uncertainties and compared with PID and LQR methods. The results showed that the SMC method is much more insensitive to parameter changes than the PID and LQR methods. It has also been observed that all three controllers maintain their stability against disturbance inputs, but the SMC method offers a better control performance.

DTC-SVM strategy of double star induction motor using sliding mode controller enhanced by fuzzy logic

Extensive research and numerous articles on Direct Torque Control (DTC) systems have consistently highlighted various deficiencies associated with the employment of hysteresis control units. Specifically, these issues include significant torque and stator flux ripples, as well as distortions in stator current. Additionally, the sensitivity to parametric variations stemming from Proportional-Integral (PI) controllers poses a challenge due to the complexities involved in their adjustment. To address these drawbacks, this article explores the implementation of Space Vector Modulation DTC (SVM-DTC) technology integrated with a Sliding Mode Controller (SMC). The proposed control scheme for DTC amalgamates the benefits derived from SMC control and the SVM algorithm. The SMC controller is utilized to oversee PI controllers, employing this nonlinear technique ensures superior dynamic performance and robust resistance against external disturbances. Notwithstanding its advantages, one notable issue with SMC is chattering. To mitigate this phenomenon, we adopt an approach that integrates Fuzzy Logic with SMC. By replacing the saturation function (Sat) with a fuzzy inference system, we effectively minimize chattering. The simulation is conducted using MATLAB SIMULINK. Comparative analysis between DTC-SVM strategy and traditional methods including standard SMC and Fuzzy-enhanced Sliding Mode Controller (FSMC), demonstrates that our hybrid nonlinear controller exhibits high efficiency and remarkable robustness.

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

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.

Rotary inverted pendulum control using neuro-adaptive robust generalized dynamic inversion

The objective of this research is to design a robust control system for trajectory tracking of the under-actuated Rotary Inverted Pendulum (RIP). The focus is on achieving semi-global asymptotic stability in the absence of knowledge about the RIP geometric and inertia parameters. The control design commences by formulating differential servo constraint dynamics (VCD) that encapsulates the control objectives for the RIP. The baseline generalized dynamic inversion (GDI) control law is derived by inverting the VCD for the control voltage input using the Moore–Penrose generalized inverse. To enhance robustness against modeling uncertainties and exogenous disturbances, a sliding mode control (SMC) element is integrated within the GDI control system, resulting in the robust GDI (RGDI) control system. Furthermore, an adaptive estimator that is based on Radial Basis Function Neural Networks (RBF-NN) is adopted to eliminate the dependency of the RGDI control system on the RIP mathematical model. The weighting matrices of the RBF-NN are updated with the aid of a Lyapunov control function. The proposed control system offers robust solution and guarantees semi-global asymptotic stability to the unstable equilibria of the RIP dynamics in the absence of a mathematical model. The proposed control system’s performance is evaluated through computer simulations and experimental tests on a real RIP system. Comparative analyses with traditional SMC and linear quadratic control methodologies are conducted to show the effectiveness of the proposed approach.

Sliding mode controller with neural network compensation for tank

The control effectiveness of the all-electric tank stabilizers directly affects the firing accuracy of the tank gun. In order to improve the firing accuracy of the moving tank, this paper proposes a sliding mode control (SMC) strategy using neural network feed-forward compensation for the tank bidirectional stabilizers. SMC technology can quickly and effectively deal with uncertainties, unmodeled terms, and external disturbances in complex tank systems. Neural networks possess the advantage of approximating arbitrary continuous functions in finite time, realizing the estimation of SMC control errors with feed-forward compensation. The Lyapunov stability analysis proves that the designed controller can achieve asymptotic stabilization in finite time. Finally, both co-simulation and physical experiments are conducted. The results show that the proposed control strategy possesses better tracking speed and tracking accuracy compared to the conventional controller and exhibits strong robustness.

Intelligent Solar PV Grid Connected and Standalone UPQC for EV Charging Station Load

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.