Robust Sliding Mode Control Research Articles

Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem

The ultra-supercritical once-through boiler (OTB) unit is an advanced power generation technology with high plant efficiency and low emissions. However, it is difficult to realize a coordinate control for the ultra-supercritical OTB unit to achieve the fast and stable dynamic response during the load tracking and grid frequency disturbances and in the presence of unavoidable uncertainties. In this paper, an accurate grey box multivariable coupled nonlinear model of an ultra-supercritical boiler-turbine unit is considered. Steam pressure at throttle valve, specific enthalpy in separator and active power are adjusted at desired values by manipulation of the fuel rate command, feedwater rate command and throttle valve opening. In addition to the mentioned accurate model, limitations of operational parameters and uncertainty sources are considered in the controller design. This makes this research unique from previous works. According to affine nonlinear control theory, a nonlinear robust sliding mode controller is developed. Then using MATLAB μ - DK iteration syntax, a linear H-infinity (H∞) robust control is synthesized with selecting proper weight functions. This method is suitable for all types of power plants with a similar mathematical modeling approach. Simulations in MATLAB Simulink toolbox reveals that both designed controllers achieve appropriate performance in the presence of uncertainties and parameters’ limitations, but nonlinear controller has better performance. Finally, deficiencies of a linear controller with respect to the proposed designed robust controllers are denoted.

Robust sliding mode control for a class of nonlinear systems through dual-layer sliding mode scheme

This article investigates the topic of robust sliding mode control for a class of nonlinear systems in the presence of actuator faults and disturbances. By virtue of a novel integral sliding function combined with an auxiliary controller ‘ν(t)’, a dual-layer sliding mode control framework is established. For system responses, the first layer is an integral sliding mode generated by the actuator output, the second layer is a nonlinear sliding mode generated by ‘ν(t)’. Under the proposal, the nonlinear sliding mode is nested in the integral sliding mode. Compared with the state-of-the-art integral sliding mode control (ISMC) results, not only the inherent robustness is retained, but also the time derivative of integral sliding function can be resisted by ‘ν(t)’. Accordingly, the designed integral sliding mode controller provides more enhancive robustness. With the nested nonlinear sliding mode generated, the matched time derivative of integral sliding function in integral sliding motion dynamics is decoupled from the mismatched disturbances. This decoupling contributes to design a sliding mode controller for ensuring that the system states are uniformly bounded with time-varying performance boundaries. Further, by employing a class of vector-value functions, a novel continuous sliding mode controller is presented such that the sliding modes of the considered systems cannot be lost regardless of actuator faults and disturbances. At last, an illustrative example verifies the effectiveness of the theoretical findings.

Robust sliding mode control for a class of nonlinear systems with actuator failures and disturbances: A dual‐layer sliding mode strategy

AbstractThis paper studies the problem of robust sliding mode control (SMC) for a class of nonlinear systems subject to actuator failures and disturbances. By employing a novel integral sliding function constructed with an nested sliding mode controller “,” a dual‐layer sliding mode control is established. The first layer is an integral sliding mode generated by the actuator output, the second one is a linear sliding mode generated by “.” Indeed, the linear sliding mode is nested to the integral sliding mode for the system under consideration. Compared with the state‐of‐the‐art integral sliding mode controls (ISMCs), not only the inherent robustness of ISMC is retained, but also the time derivative of integral sliding function in integral sliding motion dynamics can be compensated by “,” so that the robustness of integral sliding motion is further enhanced. Furthermore, due to the generation of the nested linear sliding mode, it decouples the matched integral sliding function time derivative of integral sliding motion dynamics from the mismatched disturbance. This decoupling facilitates the design of “” through control theory, then, the closed‐loop systems are ensured to be asymptotically stable with performance. Lastly, with two classes of barrier functions exploited for the design of sliding mode controllers, both sliding modes cannot be lost for system responses even in the presence of unforeseen actuator failures and disturbances. In simulation, an IEEE 6 bus power system is offered to demonstrate the superiorities of the theoretical results.

All-in-One Control Framework for Distributed Drive Electric Buses Path Tracking Subject to Uncertain Crosswind and Varied Passenger Mass

This paper proposes an all-in-one control framework for distributed drive electric buses (DDEBs) that can effectively attenuate the effects of uncertain crosswind and varied passenger mass in path tracking, while minimizing the electricity consumption of battery. To track the desired path and ensure driving stability, a robust tube-based model predictive control method is proposed considering lateral, yaw and roll motions. Simultaneously, for the longitudinal motion, a robust sliding mode control method is designed to achieve accurate tracking of longitudinal velocity. Meanwhile, the uncertain crosswind and varied passenger mass faced by the DDEBs are decoupled into external disturbances and their effects on the system are eliminated in both controllers. Then, to cope with energy optimization problem in path tracking, a torque distribution controller is constructed by formulating the vehicle stability margin, tracking dynamic requirements and energy efficiency as constrained weighted least squares problem. Finally, a Trucksim-Simulink co-simulation is performed, where the simulation results demonstrated that the proposed control framework is robust and effective, and can achieve better energy saving effect compared to the original control methods.

Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system

The non-linear power-current characteristics of a photovoltaic generator (GPV) present a challenge in maximizing power production. To address this issue, Maximum Power Point Tracking (MPPT) methods, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), are commonly used due to their quick response and minimal oscillation. As well, sliding mode control (SMC) is a popular method for controlling linear and nonlinear systems because of its high robustness. In this study, the principal purpose is to develop a new approach based on the combination of ANFIS and Terminal Robust Sliding Mode Control (ANFIS-TRSMC) to resist the PV system against uncertain conditions and track the optimal power point. The simulation results show that the ANFIS-TRSMC controller has an accurate, fast, and robust response in comparison with other algorithms such as perturb and observe and the maximum power voltage–based TRSMC controller.

Robust FOSMC of quadrotor in the presence of slung load

This study aims to develop a robust control for the quadrotor slung-load system that efficiently follows a reference trajectory. A fractional-order robust sliding mode control has been chosen to control the quadrotor’s altitude, position, and attitude. An anti-swing controller was also installed to limit the swing angle of the suspended load. It was created via delayed feedback, in which the quadrotor’s position reference trajectory is changed by the difference of the load angles within a specific delayed value. Designing an adaptive FOSMC would control the system when the system uncertainties do not know the bounds. Moreover, the control parameters and the anti-swing controller for the FOSMC can be obtained using some optimization techniques to increase the controllers’ accuracy.

Artificial neural network assisted robust droop control of autonomous microgrid

AbstractElectric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

A Tube Model Predictive Control Method for Autonomous Lateral Vehicle Control Based on Sliding Mode Control.

This paper aims to enhance the lateral path tracking control of autonomous vehicles (AV) in the presence of external disturbances. While AV technology has made significant strides, real-world driving scenarios often pose challenges such as slippery or uneven roads, which can adversely affect the lateral path tracking control and reduce driving safety and efficiency. Conventional control algorithms struggle to address this issue due to their inability to account for unmodeled uncertainties and external disturbances. To tackle this problem, this paper proposes a novel algorithm that combines robust sliding mode control (SMC) and tube model predictive control (MPC). The proposed algorithm leverages the strengths of both MPC and SMC. Specifically, MPC is used to derive the control law for the nominal system to track the desired trajectory. The error system is then employed to minimize the difference between the actual state and the nominal state. Finally, the sliding surface and reaching law of SMC are utilized to derive an auxiliary tube SMC control law, which helps the actual system keep up with the nominal system and achieve robustness. Experimental results demonstrate that the proposed method outperforms conventional tube MPC, linear quadratic regulator (LQR) algorithms, and MPC in terms of robustness and tracking accuracy, especially in the presence of unmodeled uncertainties and external disturbances.

Chassis Coordinated Control for Full X-by-Wire Four-Wheel-Independent-Drive Electric Vehicles

In this paper, a full X-by-wire chassis coordination control scheme is proposed by synthetically utilizing the Direct Yaw-moment Control, Active Front Steering, Anti-Slip Regulation and Active Roll Control to improve the longitudinal, yaw and roll stability. First, a vehicle state prediction module is established to predict vehicle yaw and roll stability and to generate three reference vehicle states, i.e., the desired roll angle, yaw rate and longitudinal velocity. Then, a decentralized event-triggered discrete sliding mode control scheme is developed to track these reference states by coordinating the X-by-wire subsystems based on their respective effective working areas. For yaw rate control, dynamic torque regulation factors are introduced to prevent wheel slip and lock-up based on real-time slip ratio feedback. Under longitudinal driving conditions, a robust sliding mode control and a hybrid control-based method are used under emergency acceleration or braking conditions. The hardware-in-the-loop (HIL) tests under the slalom, double lane change and fish hook tests show that the proposed chassis coordinated control scheme can comprehensively improve vehicle ride comfort, handling performance, longitudinal and lateral stability, and rollover prevention ability.

Discrete Multi-Objective Switching Topology Sliding Mode Control of Connected Autonomous Vehicles With Packet Loss

Information flow topology is crucial in the control of connected autonomous vehicles. It has a substantial influence on the platoon's performance, especially in scenarios with imperfect communication. To address this issue, this study provides a real-time switching topology technique for improving the platoon's performance under various types of imperfect communication situations and constant packet dropout rates. First, a discrete sliding mode controller with a double power reaching law is designed for a nonlinear heterogeneous vehicle dynamic model with packet loss. Then, Lyapunov analysis is applied to ensure the platoon's stability and string stability. Finally, a two-step switching topology framework is introduced. The first step is to design an offline Pareto optimal topology search with some predicted imperfect communication scenarios, where the platoon's tracking ability, fuel consumption, and driving comfort are optimised using a multi-objective evolutionary algorithm based on decomposition with two opposing adaptive mechanisms. In the second step, the optimal topology is switched and selected in real-time from among the previously acquired Pareto optimal topology candidates, to reduce the control cost. To validate the proposed approach, numerical simulations are conducted. According to the results, compared to a standard robust sliding mode controller, the suggested technique enhances platoon tracking ability by 97.73 percent, fuel efficiency by 9.96 percent, and driving comfort by 20.18 percent, respectively.

Parasitic Component Small-Signal Modeling and Control of a Practical CLLC Resonant Converter

This article presents a comprehensive characterization of an asymmetric resonant CLLC dc/dc converter plant transfer function obtained using a generalized harmonic approximation (GHA)-based small-signal modeling approach. The effect of circuit parasitic components is comprehensively considered while deriving this model, and a quantified comparison of the resultant plant frequency response with a conventional first harmonic approximation (FHA)-based model is presented, which provides the designer insightful findings to design a robust and noise immune closed-loop compensator. Furthermore, a thorough explanation of a sliding mode control (SMC) along with detailed parameterization of the controller coefficients is provided by analyzing the system’s dynamic behavior and comparing the response with a conventional proportional–integral (PI)-based controller. In addition to the objective of designing a robust SMC controller, a phase shift-based secondary side modulation is introduced, which facilitates a significant reduction in the secondary side switching losses, thus enhancing the steady-state efficiency of the overall system. To validate and benchmark the open-loop plant response and controller dynamics, detailed steady-state results are elucidated for a 400–28- and 400–24-V voltage conversion at a rated load of 1 kW, with a resonant frequency of 500 kHz. Furthermore, a comprehensive experimental comparison between the proposed hybrid control scheme and the conventional PI controller is shown for two dynamic load changes corresponding to 10%–90% load step-up and 90%–10% load step-down. Quantification of dynamic response portrays a settling time reduction of 46.4% and an over/undershoot reduction of 33%, thus validating the robustness of the proposed control scheme.

A robust autonomous sliding-mode control of renewable DC microgrids for decentralized power sharing considering large-signal stability

DC microgrids (MGs) are providing a pathway toward a zero-carbon-based future. The intermittent renewable energy sources (RESs) and non-linear constant power loads (CPLs) thrive in DC MGs, craving for effective coordinative control solutions to ensure the stability of DC MGs. In this paper, a robust autonomous sliding mode control (SMC) scheme is proposed for achieving a globally stable and decentralized power sharing operation of multiple dispatchable units (DUs) in CPL-integrated DC MGs. Firstly, by using the high-order finite-time observer (HOFTO) technique, the disturbances, such as the power coupling and parameter uncertainties between different DU-interfaced converters, are self-eliminated within a finite time without any output current sensor and communication link. Secondly, a decentralized control scheme synthesizing the robust SMC and droop control algorithm is proposed to achieve proportional power sharing among paralleled DUs and precise DC bus voltage regulation. The proposed control guarantees the global system’s large-signal stability by ensuring the local stability of an individual converter, offering a simple yet effective stable coordinative solution. Finally, simulation and experimental results verify the effectiveness of the proposed strategy.

Research on anti-rollover warning control of heavy dump truck lifting based on sliding mode-robust control

In order to improve the rollover stability of heavy dump trucks during lifting and unloading under special circumstances and prevent heavy dump trucks from overturning, a sliding mode-robust control based on anti-rollover early warning control method for heavy dump trucks lifting and unloading was proposed. A four-degree-freedom heavy dump truck lifting and unloading roll dynamic model was established, and the accuracy of the model was verified in the MATLAB/Simulink simulation environment. The controller was designed by combining sliding mode robust control based on HJI theory and active suspension control. The ratio of the gravity carried by the wheel with the lighter bearing capacity to the total gravity of the heavy dump truck was used as the evaluation index for rollover warning. Under two different simulation conditions, compared with conventional sliding mode control and no control, the rollover evaluation index and roll angles were analyzed under three control methods. The results showed that the anti-rollover early warning control method for lifting and unloading of heavy dump trucks proposed in this paper had better anti-rollover capability and better stability.

Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties

Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.

Five-degree-of-freedom robust control of a magnetic endoscopic capsule using an electromagnetic system

Capsule endoscopy is a minimally invasive gastrointestinal diagnostic procedure. The active endoscopic capsule enables the physician to steer the device to any desired position and direction, resulting in a better diagnosis of the disease. Due to the model uncertainties and the existence of disturbances in the gastrointestinal system, traditional control methods cannot achieve robust performance and stability. To overcome this issue, in this paper, a robust sliding mode controller is proposed for 5 Degrees Of Freedom (DOF) control (3-DOF closed-loop control for position and 2-DOF open-loop control for orientation) of the capsule endoscope using an electromagnetic actuation system. The efficiency of the proposed control method is compared with a Proportional–Integral–Derivative (PID) controller considering the model uncertainties and external disturbances. The results of simulations considering 5% of uncertainty in the model parameters show that the suggested approach can precisely and efficiently actuate the capsule to follow the desired trajectory, with position and orientation accuracy of 0.53 mm and 0.69 degree, respectively. Experiments demonstrate the efficiency of the proposed controller, revealing a tracking error of about 0.73 mm in the capsule position. Furthermore, it has been proven by experiments that even in the presence of external disturbances, the proposed control system performs very well and keeps the capsule on the desired trajectory.

Modeling and Control of Quadcopter Based on Sliding Mode Robust Control in the Wind Field

Due to the low flying height, quadrotors are easily affected by external wind disturbances. In this paper, the dynamic model of the quadrotor flying in the wind field is established according to the Newton-Euler equation. The model introduces a weight function due to the phenomenon that different rotors are affected differently by the wind field due to their different positions relative to the wind field, and a sliding mode robust controller based on inner and outer loops is designed. The verification of the control system is carried out by means of simulation in a Gazebo and actual flight experiments in the wind field. The dynamic of the system and the controller performance under the wind field are also tested. The results show that the designed controller has the performance of stable flight in the wind field environment.

Design of hybrid fault-tolerant control system for air-fuel ratio control of internal combustion engines using artificial neural network and sliding mode control against sensor faults

This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.

Certainty equivalence-based robust sliding mode control strategy and its application to uncertain PMSG-WECS.

This work focuses on maximum power extraction via certainty equivalence-based robust sliding mode control protocols for an uncertain Permanent Magnet Synchronous Generator-based Wind Energy Conversion System (PMSG-WECS). The considered system is subjected to both structured and unstructured disturbances, which may occur through the input channel. Initially, the PMSG-WECS system is transformed into a Bronwsky form, i.e., controllable canonical form, which is composed of both internal and visible dynamics. The internal dynamics are proved stable, i.e., the system is in the minimum phase. However, the control of visible dynamics, to track the desired trajectory, is the main concern. To carry out this task, the certainty equivalence-based control strategies, i.e., conventional sliding mode control, terminal sliding mode control and integral sliding mode control are designed. Consequently, a chattering phenomenon is suppressed by the employment of equivalent estimated disturbances, which also enhance the robustness of the proposed control strategies. Eventually, a comprehensive stability analysis of the proposed control techniques is presented. All the theoretical claims are verified via computer simulations, which are performed in MATLAB/Simulink.

Robust Motion Planning in Robot-Assisted Surgery for Nonlinear Incision Trajectory

In the era of digital OTs (operating theatres), the developments in robot-assisted surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses robotic articulations to assist in complicated surgeries. Its implementation improves the ability of the specialized doctor to perform surgery to a great extent. The paper addresses the dynamics and control of the highly non-linear 3DOF surgical robot manipulator in the event of external disturbances and uncertainties. The integration of non-linear robust SMC (sliding mode control) with a smoothing mechanism, a FOPID (fractional-order proportional integral derivative) controller, and a fuzzy controller provides a high degree of robustness and minimal chatter. The addition of fuzzy logic to the controller, named intelligent fuzzy-SFOSMC (smoothing fractional order sliding mode controller) improves the system’s performance by ruling out the disturbances and uncertainties. The prototype model is developed in a laboratory and its outcomes are validated on OP5600, a real-time digital simulator. Simulation and experimental results of the proposed fuzzy-SFOSMC are compared with conventional controllers, which illustrates the efficacy and superiority of the proposed controller’s performance during the typical surgical situations. The proposed fuzzy-SFOSMC outperforms conventional controllers by providing greater precision and robustness to time-varying nonlinear multi-incision trajectories.

Intelligent Fractional Order Sliding Mode Based Control for Surgical Robot Manipulator

In the era of digital OT (Operating Theatre), the developments in Robot-Assisted Surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses surgical robots to assist complicated surgeries. Its implementation improves the ability of the specialised doctors to perform surgery to a great extent. This paper addresses the dynamics and control of the highly non-linear 3DOF surgical robot manipulator in the occurrence of external disturbances and uncertainties. The integration of non-linear robust SMC (Sliding Mode Control) with smoothing mechanism, FOPID (Fractional-Order Proportional Integral Derivative) controller and fuzzy controller provides a high degree of robustness and minimal chatter. The addition of type-2 fuzzy logic to the controller, named intelligent T2F-SFOSMC (Type-2 Fuzzy-Smoothing Fractional Order Sliding Mode Controller), improves the system’s performance by ruling out the disturbances and uncertainties. The prototype model is developed in a laboratory and its outcomes are validated on OP5600: a real-time digital simulator. The simulation results and experimental results of the proposed T2F-SFOSMC are compared with conventional controllers, which illustrates the efficacy and superiority of the proposed controller’s performance during the typical situation of surgery. The proposed T2F-SFOSMC outperforms conventional controllers by providing greater precision, stability and robustness to time-varying nonlinear multi-incision trajectory.