Chaotic vibration control of an axially moving string of multidimensional nonlinear dynamic system with an improved FSMC.

A new control approach based on fuzzy sliding mode control (FSMC) is proposed to regulate the chaotic vibration of an axial string. Hamilton's principle is used to formulate the nonlinear equation of motion of the axial translation string, and the von Kármán equations are used to analyse the geometric nonlinearity. The governing equations are nondimensionalized as partial differential equations and transformed into a nonlinear 3-dimensional system via the third-order Galerkin approach. An active control technique based on the FSMC approach is suggested for the derived dynamic system. By using a recurrent neural network model, we can accurately predict and effectively apply a control strategy to suppress chaotic movements. The necessity of the suggested active control method in the regulation of the nonlinear axial translation string system is proven using different chaotic vibrations. The results show that the study of the chaotic vibrations of axially translating strings requires nonlinear multidimensional dynamic systems of axially moving strings; the validity of the proposed control strategy in controlling the chaotic vibration of axially moving strings in a multidimensional form is demonstrated.

Sliding mode control with self-adaptive parameters of a 5-DOF hybrid robot.

Due to the advantages of high stiffness, high precision, high load capacity and large workspace, hybrid robots are applicable to drilling and milling of complicated components with large sizes, for instance car panels. However, the difficulty in establishing an exact dynamic model and external disturbances affect the high accuracy control directly, which will decrease the machining accuracy and thereby affect the machining quality and efficiency of the system. Sliding mode control is an effective approach for high-order nonlinear dynamic systems since that it is very insensitive to disturbances and parameter variations. However, chattering may exist in traditional sliding mode control with fixed parameters, which results from a constant approaching speed. Besides, the approaching speed will affect the chattering strength directly. To solve these problems, a modified sliding mode controller with self-adaptive parameters is proposed to enhance the trajectory-tracking performance of a 5-degree-of-freedom hybrid robot. Firstly, the kinematic model of the robot is established. Then adopting the principle of virtual work, a rigid dynamic model of the robot is built. Based on the built dynamic model, a modified sliding mode control method is developed, of which the approaching speed is dependent on the system state. Finally, the sliding mode controller with self-adaptive parameters is created for a hybrid robot. The proposed sliding mode controller can achieve a rapid approaching speed and suppress chattering simultaneously. Simulation results demonstrate that the proposed modified sliding mode controller can achieve a comparatively accurate and smooth trajectory, which owns good robustness to external disturbances.

Quantized feedback integral sliding mode control for uncertain networked linear systems via event-triggered approach.

This paper investigates the design problem of quantized event-triggered (ET) integral sliding mode (ISM, SM) controller for uncertain networked linear systems. Firstly, this paper proposes a novel ISM surface constructed using only quantized ET states and applied to the design of the controller. The quantized ET ISM surface in this paper divides the integrals from t0 to t into the sum of adjacent ET intervals and replaces ET states with quantized ET states. Secondly, based on state error and quantized ET SM error, novel ET conditions are constructed to decide whether to update the current control signal or not. Thirdly, this paper proves the existence of minimum inter-event times under "zoom-out" and "zoom-in" phases respectively, avoiding Zeno phenomenon. Finally, to prove the validity of the proposed method, two comparative simulation results are given.

Adaptive fuzzy neural super-twisting control of micro gyroscope sensor.

To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of [Formula: see text] and [Formula: see text] in the x and y directions, respectively.

Performance study of dual heart assisted control system based on SL-SMC physiological combination controller.

The main challenges of Biventricular Assist Devices (BiVAD) as a treatment modality for patients with Bicardiac heart failure heart failure are the balance of systemic blood flow during changes in physiological activity and the prevention of ventricular suction. In this study, a model of the Biventricular Circulatory System (BCS) was constructed and a physiological combination controller based on Starling-Like controller and Sliding Mode Controller (SMC) was proposed. The effects of the physiological controller on the hemodynamics of the BCS were investigated by simulating two sets of physiological state change experiments: elevated pulmonary artery resistance and resting-exercise, with constant speed (CS) control and combined Starling-like and PI control (SL-PI) as controllers. Simulation and experimental results showed that the Starling-like and Sliding Mode Control (SL-SMC) physiological combination controller was effective in preventing the occurrence of ventricular suction, providing higher cardiac output, maintain balance of systemic blood flow, and have higher response speed and robustness in the face of physiological state changes.

Optimized fast non-singular integral terminal sliding mode control of immune response and HCMV infection of renal transplant recipient.

Kidneys are the most commonly transplanted organs, and renal transplant is the best treatment for patients with advanced stages of renal disease. Immunosuppressive drugs are used after renal transplant to prevent the body from rejecting the transplanted kidney and ensure its proper kidney functioning. However, suppression of the immune system increases the risk of viral infections and other complications. Therefore, careful monitoring and management of immunosuppressive and antiviral drugs are essential for the success of the transplants. This article presents a hybrid fast non-singular integral terminal sliding mode control technique to adjust the efficacies of these drugs in renal transplant recipients, ensuring successful transplants and preventing viral infections. The proposed strategy tracks system trajectories to reference values and adjusts the treatment plan accordingly. The Lyapunov stability theorem is used to prove the asymptotic stability of the closed-loop system. Several simulation studies are conducted in MATLAB/Simulink environment to evaluate the performance of the proposed control technique in maintaining a balance between over-suppression and under-suppression. Genetic Algorithm is used to optimize the gain values to further improve the performance of the proposed control technique. Its performance is compared with two other variants of terminal sliding mode controllers to demonstrate its effectiveness against them.

Super-twisting ADRC for maximum power point tracking control of photovoltaic power generation system based on non-linear extended state observer.

This paper proposed a new method for maximum power point tracking in photovoltaic power generation systems by combining super-twisting sliding mode control and active disturbance rejection method. An incremental guidance method is used to find the point of maximum power. The non-linear extended state observer is applied to estimate the unmodeled dynamics and external disturbance. The ADRC based on a super-twisting sliding mode is designed to bring the state variables to the desired state. In the next step, the stability of NESO and ADRC are theoretically proved. Finally, the simulation results have been compared with the results of the PI controller, classical sliding mode control, and terminal sliding mode control (TSMC) presented in other articles. The results show the effectiveness and superiority of the proposed method. Also, to check the performance of the proposal method in real-time, real-time results have been compared with non-real-time results. The results obtained from the real-time and non-real-time simulations exhibited a minimal difference. This fact indicates the high accuracy of the modeling and simulations performed. Indeed, the mathematical models and non-real-time simulations have been able to accurately mimic the actual behavior of the photovoltaic system under various operating conditions.

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller.

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes.

Disturbance Estimation and Predefined-Time Control Approach to Formation of Multi-Spacecraft Systems.

Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating a kinematics model for the relative motion of spacecraft, with the formation's communication topology represented by a directed graph for the formation system of the spacecraft. A disturbance observer is then developed to estimate the disturbances, and the estimation errors can be convergent in fixed time. Following this, a disturbance-estimation-based sliding mode control is proposed to guarantee the predefined-time convergence of the multi-spacecraft formation system, regardless of initial conditions. It allows each spacecraft to reach its desired position within a set time frame. The results of the analysis of the multi-spacecraft formation system are also provided. Finally, an example simulation of a five-spacecraft formation flying system is provided to demonstrate the presented formation control method.

Adaptive backstepping sliding mode control for single-inductor double-output boost converter.

To reduce the cross-regulation and improve the dynamic response performance of a single-inductor double-output Boost converter, an adaptive backstepping sliding mode control (ABSMC) strategy is proposed in this paper. The nonlinear mathematical model of the converter is established, an output function satisfying the exact feedback linearization (EFL) is constructed based on the inverse system theory, and the linearization and decoupling of the model are implemented. Meanwhile, the problem of EFL heavily relying on an exact model is solved by combining backstepping control with sliding mode control. Furthermore, an adaptive reaching law is proposed to adjust the gain of the switching function, and the chattering phenomenon of sliding mode control is reduced. The stability of the system is proven according to the Lyapunov stability theorem. Finally, compared with the existing control methods, both the simulation results and experimental results verify the effectiveness and superiority of the proposed ABSMC strategy.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy.

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.

Sliding mode tracking control of a class of fractional-order nonstrict-feedback nonlinear systems.

Since the Leibniz rule for integer-order derivatives of the product of functions, which includes a finite number of terms, is not true for fractional-order (FO) derivatives of that, all sliding mode control (SMC) methods introduced in the literature involved a very limited class of FO nonlinear systems. This article presents a solution for the unsolved problem of SMC of a class of FO nonstrict-feedback nonlinear systems with uncertainties. Using the Leibniz rule for the FO derivative of the product of two functions, which includes an infinite number of terms, it is shown that only one of these terms is needed to design a SMC law. Using this point, an algorithm is given to design the controller for reference tracking, that significantly reduces the number of design parameters, compared to the literature. Then, it is proved that the algorithm has a closed-form solution which presents a straightforward tool to the designer to obtain the controller. The solution is applicable to the systems with a mixture of integer-order and FO dynamics. Stability and finite-time convergence of the offered control law are also demonstrated. In the end, the availability of the suggested SMC is illustrated through a numerical example arising from a real system.

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm.

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

Nonlinear MPPT techniques to control hybrid power systems.

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC-DC boost converter (DC-DC-BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC-DC-BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Towards stable and efficient nitrogen removal in wastewater treatment processes via an adaptive neural network based sliding mode controller.

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration ( S N H ) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the S N H with nitrate nitrogen ( S N O ) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

Hydrodynamic analysis and manipulation control on a streamlined I-AUV.

Hydrodynamics analysis and control are very significant for the seabed operations, particularly for the intelligent manipulation process of streamlined intervention autonomous underwater vehicles (I-AUVs). The computation fluid dynamics simulations and verification were conducted in the consideration with water channel domain, mesh insensitivity, support straight bar connector, free surface and other boundary conditions. The variation trend of hydrodynamic coefficients in the process of manipulation is obtained, by simulations of streamlined I-AUV manipulation under dynamic manipulation state. To further realize underwater floating manipulation, a novel controller with an integral termed nonlinear sliding mode surface and disturbance observer has been developed. The disturbance observer can make quantity analysis on the interaction forces between I-AUV and the environment from hydrodynamic analysis. Simulations and experiments have verified the controller performance.

Dynamic look-ahead feedrate scheduling method based on sliding mode velocity control.

In the feedrate scheduling of complex curve direct interpolation, dynamic constraints such as axis acceleration and jerk are related to the actual state of the tool. Most existing methods convert dynamic constraints to velocity constraints at sampling points. However, it cannot guarantee the dynamic constraints are satisfied between sampling points. Addressing the issue, this paper proposes a dynamic look-ahead feedrate scheduling method based on sliding mode velocity control, which generates the motion command considering dynamic constraints in every interpolation cycle. To dynamically generate commands based on the current tool state, the acceleration and deceleration method based on sliding mode velocity control has been proposed, which can control tool state to transition to the command state with any initial state. To ensure sufficient distance for acceleration and deceleration, this paper uses braking distance to dynamically estimate the look-ahead distance. Then the minimum value within the look-ahead interval is selected as the command velocity for this scheduling cycle and the actual motion command is determined based on the dynamic constraints of each axis. Simulation and experiment results prove that compared with the existing method, this method effectively reduces the overshoot of dynamic constraints without significantly increasing the machining time. The analysis of real-time computation time has demonstrated the potential of the method proposed in this paper for real-time applications.

Improving Direct Yaw-Moment Control via Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Control for Electric Vehicles.

Given the increased significance of electric vehicles in recent years, this study aimed to develop a novel form of direct yaw-moment control (DYC) to enhance the driving stability of four-wheel independent drive (4WID) electric vehicles. Specifically, this study developed an innovative non-singular fast terminal sliding mode control (NFTSMC) method that integrates NFTSM and a fast-reaching control law. Moreover, this study employed a radial basis function neural network (RBFNN) to approximate both the entire system model and uncertain components, thereby reducing the computational load associated with a complex system model and augmenting the overall control performance. Using the aforementioned factors, the optimal additional yaw moment to ensure the lateral stability of a vehicle is determined. To generate the additional yaw moment, we introduce a real-time optimal torque distribution method based on the vertical load ratio. The stability of the proposed approach is comprehensively verified using the Lyapunov theory. Lastly, the validity of the proposed DYC system is confirmed by simulation tests involving step and sinusoidal inputs conducted using Matlab/Simulink and CarSim software. Compared to conventional sliding mode control (SMC) and NFTSMC methods, the proposed approach showed improvements in yaw rate tracking accuracy for all scenarios, along with a significant reduction in the chattering phenomenon in control torques.

Predefined-time sliding mode control of chaotic systems based on disturbance observer.

In this paper, in order to realize the predefined-time control of n-dimensional chaotic systems with disturbance and uncertainty, a disturbance observer and sliding mode control method were presented. A sliding manifold was designed for ensuring that when the error system runs on it, the tracking error was stable within a predefined time. A sliding mode controller was developed which enabled the dynamical system to reach the sliding surface within a predefined time. The total expected convergence time can be acquired through presetting two predefined-time parameters. The results demonstrated the feasibility of the proposed control method.

Robust Output Feedback Stabilization and Tracking for an Uncertain Nonholonomic Systems with Application to a Mobile Robot.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input-output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.