Parameters Of Sliding Mode Controller Research Articles

Design and position-tracking control of a flexible joint based on the tendon-sheath mechanism

In this study, a flexible joint comprising active and passive driving modes was designed based on the tendon-sheath mechanism. Further, angle sensors and series elastomers (SE) were integrated inside the joint to produce a compact and flexible structure. First, a model of the flexible joint was established based on the involved stiffness relation, and this model was validated using the dSPACE system. Next, to respond to the low control accuracy and poor anti-interference ability of the flexible joints, a sliding-mode controller (SMC) was designed to facilitate the precise position control, as well as achieve improved robustness. However, the flexible joint was prone to considerable tremors when the overall system was subjected to a large load mass. Therefore, a fuzzy-modified adaptive SMC (FMASMC), which adaptively adjusts the SMC parameters, was designed based on the historical-state data of the system. This control method could suppress the trembling phenomenon of the system while improving its control accuracy. Finally, it was experimentally verified that the compliant joint could achieve the expected functions. Compared with the proportional–integral–derivative (PID) and SMC control algorithms, the FMASMC algorithm could effectively overcome the tremors of the compliant actuation system and improve its control accuracy; moreover, it is also suitable for controlling other compliant joints.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Concurrent Learning-Based Neuroadaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behaviour of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.

Taguchi Method for Analysing Sliding Surface Parameters of an Adaptive Terminal Sliding Mode Controller for an Articulated Robot

Taguchi Method for Analysing Sliding Surface Parameters of an Adaptive Terminal Sliding Mode Controller for an Articulated Robot

Sliding Mode Controller with Disturbance Rejection for Bidirectional Transformerless AC/DC Converter in EV Onboard Charger

Transformerless power converters are trending in automotive sector as the demand for onboard chargers rises. The high efficiency and leakage current mitigating property of highly efficient and reliable inverter concept (HERIC) converter make it an excellent choice among transformerless topologies. By using appropriate modulation technique, the HERIC converter can transmit bidirectional power. This article presents design of sliding mode controller (SMC) for bidirectional HERIC converter to enhance the tracking performance. The SMC parameters are selected utilizing Harris hawks optimization (HHO) algorithm. During vehicle-to-grid (V2G) operation, sliding mode controller is developed for reactive power regulation of the grid connected HERIC converter and for grid-to-vehicle (G2V) operation, the converter is operated with very low voltage ripples in the DC side. The stability analysis is done by considering model uncertainties to guarantee sinusoidal grid current, low THD, and unity power factor even in the presence of grid disturbances and parametric variations. The Typhoon Hardware in the Loop (HIL) 402 device is used to execute the real-time testing. The results confirmed the efficacy and robustness of the designed controller under different scenarios for both modes of operation.

Optimization Reinforced PID-Sliding Mode Controller for Rotary Inverted Pendulum

The control of a rotary inverted pendulum (RIP) is challenging because it is an underactuated, highly sensitive, and unsteady system. Sliding mode control (SMC) is a nonlinear control method with high-frequency switching control. Designing a proportional integral derivative (PID) controller for a RIP is challenging due to its nonlinearity and instability in open-loop characteristics. The primacy of the SMC over the PID is the stability of the closed-loop. Hybrid control of a PID-SMC controller can provide better performance because this technique demonstrates less chatter, higher precision, no oscillation, and adequate gain tuning. To achieve gain tuning, the PID and SMC parameters must be optimized. Thus, this paper proposed a congruently tuned control strategy (CTCS) to fine-tune the controller parameters. The proposed strategy uses an improved whale optimization algorithm (WOA), i.e., the modified Manhattan distance updated WOA (MMD-WOA) to identify effective coefficient values for the sliding surface to reduce tracking errors while reaching the desired position. The proposed CTCS for a RIP with the MMD-WOA was implemented, and the results are very promising.

Interval Fuzzy Type-2 Sliding Mode Control Design of Six-DOF Robotic Manipulator

The remarkable features of hybrid SMC assisted with fuzzy systems supplying parameters of the controller have led to significant success of these control approaches, especially in the control of multi-input and multi-output nonlinear systems. The development of type-1 fuzzy systems to type-2 fuzzy systems has improved the performance of fuzzy systems due to the ability to model uncertainties in the expression of expert knowledge. Accordingly, in this paper, the basic approach of designing and implementing the interval type-2 fuzzy sliding mode control was proposed. According to the introduced systematic design procedure, complete optimal design of a type-2 fuzzy system structure was presented in providing sliding mode control parameters by minimizing tracking error and control energy. Based on the proposed method, the need for expert knowledge as the main challenge in designing fuzzy systems was eliminated. In addition, the possibility to limit the control outputs to deal with actuators’ saturation was made available. The control method was implemented on a six-degree-of-freedom robot manipulator that was exposed to severe external disturbances, and its performance was compared to a type-1 fuzzy system as well as to the conventional SMC. The achievements revealed improved performance of the combined control system of fuzzy sliding mode type-2 in comparison with its control counterparts.

Study of chattering suppression for the sliding mode controller of an electromagnetic levitation system

Due to the inherent non-linearity and open-loop instability of maglev systems, their high-quality control performance is critical in the development stage. Sliding mode control has great potential in the field of maglev vehicle control because of its superior control performance, robustness and interference resistance. In practical applications of sliding mode control, however, the limitations of the hardware physical properties and time delay of the control units of maglev systems can cause chattering, thereby significantly reducing the stability of maglev vehicles. In order to suppress chattering, a modified sliding mode controller that combines the exponential reaching law and continuous control laws is proposed in this study. A single-point levitation experimental platform and corresponding co-simulation model were built, and a parameter influence analysis of the modified sliding mode controller was conducted. This paper presents an adaptive correction method for the sliding mode control parameters based on the aforementioned chattering study. The actual levitation experiments were used to validate the control performance of the proposed controllers. Overall, the conducted research revealed that the modified controllers could effectively suppress chattering and possess excellent robustness.

Optimized speed control with torque ripple reductions of BLDC motor based on SMC approach using LFD algorithm

Brushless DC motors (BLDCM) are utilized in various applications, including electric cars, medical and industrial equipment, where highefficiency speed control is necessary to meet load and tracking reference fluctuations. In this study, the optimal parameters of proportional-integral (PI) and sliding mode controller (SMC) for BLDC motor speed control are determined using Lévy flight distribution (LFD) technique. The integral time absolute error (ITAE) is used as a fitness function with the LFD algorithm for tuning the PI and SMC parameters. The optimization algorithms' performance is presented statistically and graphically. The simulation results show the SMC based on the LFD technique has superiority over SMC without optimization and PI controller in fast-tracking to the desired value with zero overshoot with rising time (6 ms) and low-speed ripple up to (± 9 RPM) under non-uniform conditions.

Sliding Mode Control of Networked Control Systems: An Auxiliary Matrices-Based Approach

A novel sliding mode control design method is developed to address the robust stabilization problem for uncertain time-varying networked control systems subject to state delay, which utilizes auxiliary matrices to avoid the difficulties of finding quadratic Lyapunov functions and solving the linear matrix inequalities. By embedding the original system into a higher dimensional system, simplified piecewise nonnegative functions can be used to analyze the system. Stability criteria are obtained which can be satisfied and verified easily. An optimization problem is defined based on these criteria, to obtain the sliding mode control parameters to ensure the exponential stability of the closed-loop system.

Optimization of the Performances of a Two-Joint Robotic Arm using Sliding Mode Control

In this paper, we worked on the control of the angular position of a two-joint robotic arm by the sliding mode technique, after having establishing the dynamic equations of the system by the Lagrange method, with the purpose of improving the performances of the system by acting on certain parameters related to the sliding mode technique. The simulation results show an optimization of the sliding mode controller parameters, generally, in the response of the controlled system, which consists on minimizing error and settling time, and eliminating the unwanted phenomenon of chattering, after finding the optimal values of these parameters. Verification by simulation of the robustness of the optimized robotic arm shows that its response is independent of the dimensions and masses of the bodies of this robotic arm, as well as of the applied load. this answer always corresponds to the best performances of speed, settling time and margin of error. the only quantity that varies according to the parameters of the robotic arm and the applied load is the torque required. This couple has a compensating effect to the change of these internal parameters and of this applied load, to keep the same optimal response on the condition of communicating these changes with the controller.

Manipulator trajectory tracking based on adaptive sliding mode control

AbstractA manipulator is a complex electromechanical system that is nonlinear, strongly coupled, and uncertain. Achieving its precise and high‐quality trajectory control is difficult. Sliding mode control (SMC) is one of the common control methods for manipulators. However, discontinuities in SMC can cause jitter and vibration in the manipulator system, leading to a reduction in the performance of the control system. For the self‐adaptive capability jitter vibration problem of SMC, the Dobot magician manipulator is treated as the research object in this article. The dynamics equations of the manipulator are established by Lagrange method, and a simplified model of the manipulator dynamics is constructed. The method of self‐adaptive sliding mode control is proposed. Self‐adaptive parameters are added to the SMC to achieve self‐adaptive adjustment of the SMC parameters. In the MATLAB/Simulink simulation environment analysis show that the self‐adaptive SMC method has better self‐tuning ability and trajectory tracking ability than the traditional SMC, and weakens the jitter phenomenon existing in the traditional SMC.

A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems

This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance.

Sliding mode heading control for AUV based on continuous hybrid model-free and model-based reinforcement learning

For autonomous underwater vehicles (AUVs), control over AUV heading is of key importance to enable high-performance locomotion control. In this study, the heading control is achieved by using the robust sliding mode control (SMC) method. The performance of the controller can be seriously affected by its parameters. However, it is time-consuming and labor-intensive to manually adjust the parameters. Most of the existing methods rely on the accurate AUV model or prior knowledge, which are difficult to obtain. Therefore, this study is concerned with the problem of automatically tuning the SMC parameters through reinforcement learning (RL). First, an AUV dynamic model with and without current influence was successfully established. Second, a continuous hybrid Model-based Model-free (MbMf) RL method based on the deterministic policy gradient was introduced and explained. Then, the framework for tuning the parameters of SMC by the RL method was described. Finally, to demonstrate the robustness and effectiveness of our approach, extensive numerical simulations were conducted on the established AUV model. The results show that our method can automatically tune the SMC parameters. The performance is more effective than SMC with fixed parameters or SMC with a purely model-free learner.

Optimal Tuning of the SMC Parameters for a Two-Link Manipulator Co-Simulation Control

This paper presents the trajectory tracking control of a two-link planar robot manipulator using MSC Adams and MATLAB co-simulation which enables the innovative virtual prototyping of the systems without any mathematical expressions. Firstly, the tracking control performance of the planar manipulator is investigated using the Sliding Mode Control (SMC) controller and the Proportional Integral Derivative (PID) controller in terms of the performance analysis. As a result, the SMC demonstrates effective control performances compared to the PID controller according to the required trajectory, settling time, and end position of the system. Then, the SMC controller parameters are determined using the different optimization methods offered as open source by MATLAB/Response Optimization Toolbox and compared to each other. In the virtual co-simulation, the trajectory tracking control performance is observed to be improved by optimizing the parameters of the SMC controller using Simplex Search (SS) method. All control results are examined and presented with graphics and international error standards.

Particle swarm optimization based sliding mode controllers for electric vehicle onboard charger

High-performance onboard chargers are needed to proliferate the popularity of electric vehicles considering the charging convenience and flexibility. In this study, a sliding mode controller (SMC) for power converters is presented to enhance the performance of the onboard battery charger. The SMC parameters are selected using the particle swarm optimization (PSO) algorithm. A two-stage charging topology is adopted with independent controllers. The grid side AC–DC converter mitigates the harmonic content in the grid current and smoothens the voltage and current in the dc bus. The bidirectional DC–DC converter at the battery side regulates the charging characteristics of the Li-ion battery with a constant current–constant voltage (CC–CV) profile. The steady-state and dynamic behavior of the charging system is presented. Hardware in the loop real-time emulator is utilized to confirm the validity of this study. Experiments show that optimization-based SMC contributes to the overall dynamic performance of the onboard battery charger.

Optimized Sliding Mode Control of Three-Phase Four-Switch Inverter BLDC Motor Drive Using LFD Algorithm

This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions.

Global optimization of second-order sliding mode controller parameters using a new sliding surface: An experimental verification to process control system

The chattering effect, robustness and stability are the major issues in a typical Sliding Mode Control (SMC). The selection of sliding manifold parameters are complicated, time-consuming and tedious. The parameter convergence is also a challenging work. Hence, the paper accords a global optimization of Second-Order Sliding Mode Controller (SOSMC) parameters with new sliding surface using the Jaya algorithm for non-linear uncertain process tank systems. The controller (SOSMC) role is to direct the sliding surface and it’s first order time derivative to zero in a finite time.The Jaya algorithm uses ’optimal features’ by updating the solutions in populations. The effectiveness of proposed strategy is evaluated for five objective functions and compared with Artificial Bee Colony (ABC) based SOSMC, classical SOSMC, typical SMC, Non-dominated Sorting Genetic Algorithm based First-order SMC (NSGA-II FOSMC) and Proportional+Integral+Derivative (PID) controller which is validated through real-time experimentation. The asymptotic stability is guaranteed through direct Lyapunov candidate function.The statistical significance of algorithms has been verified by using Friedman, Dunn, critical time efficiency and p-value tests. The Jaya algorithm shows a better performance over reported methods concerning process speed, settling time, rise time, reaching time, overshoot, response time and parameter convergence as explored from simulation, statistical, and experimental results. Furthermore, the elevated dead-time and oscillatory processes have been presented to show the efficacy of proposed strategy.

Path following method for AUV based on Q-Learning and RBF neural network

In the underwater docking process, the oscillation on AUV velocity brings extra challenge on AUV path following. A Q-learning based Sliding Mode Control (SMC) method to increase the path following performances is proposed. Firstly, AUV guidance law is designed to reduce the path following error. Heading and depth sliding mode controllers are designed to track the guidance law. Then, according to AUV velocity, tracking error and the first derivative, the control parameters of SMC are optimized via Q-learning network. RBF neural network is built to accelerate the offline learning rate. Finally, numerical simulations are made to investigate the characteristics of the present method. Comparisons are made between the trained Q-learning based SMC and the traditional SMC. The results show the effectiveness of the present method.

Control of negative gain nonlinear processes using sliding mode controllers with modified Nelder-Mead tuning equations

Abstract This paper proposes a sliding mode controller (SMC) with modified Nelder-Mead tuning, for the control of nonlinear chemical processes, which are represented as first order plus dead time process with negative gain (FOPDT-NG). In the controller design, the SMC controller parameter in continuous part is obtained based on the time constant and dead time of the process, and controller parameters in the discontinuous part is obtained using Nelder-Mead tuning equations. Even though the controller parameters of conventional SMC are tuned using Nelder-Mead tuning, zero dynamics are noticed in the closed loop response of few FOPDT-NG processes and, with few other FOPDT-NG processes tracking of set-point is unachievable. This work proposes modification in the Nelder-Mead tuning equations using Nelder-Mead optimization to overcome the above disadvantages. Four different types of FOPDT-NG processes are considered in this work, and for every type the Nelder-Mead tuning equations are modified, for the design of proposed controllers. The performances of proposed controllers are evaluated for FOPDT-NG processes and also for three different chemical processes taken from literature. A simulation results demonstrate that, the proposed controller prevailed the performance of the conventional SMC in tracking the set-point and the elimination of zero dynamic behavior of FOPDT-NG processes. Hence, the proposed controllers provide improved closed loop performances as compared to the conventional SMC.