Adaptive Sliding Mode Control Research Articles

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.

A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor

A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor

Robust adaptive sliding mode control of a bidirectional DC-DC converter feeding a resistive and CPL based on PSO

A DC-DC converter functioning in bidirectional (two-way) mode is a crucial component of direct current (DC) microgrids since it allows electricity to flow in both directions. However, because of load changes and other factors, the DC-bus voltage might become unstable. This research proposes a robust adaptive controller for a half-bridge two-way DC-DC converter founded on particle swarm optimization (PSO). Using a DC-DC half-bridge bidirectional converter, the effectiveness of various conventional and proposed control techniques is investigated. In comparison to a conventional sliding mode controller (CSMC), it is found that a PSO-based sliding mode control with an adaptive law is the optimal control approach for a bidirectional half-bridge DC-DC converter. This is because minimal steady-state error and the shortest rising and settling times are guaranteed. The benefits of robustness, chattering reduction, and simple design are combined in the suggested controller, which is especially beneficial when dealing with load and input voltage changes. The controller ensures robustness and stability in the face of parameter changes. Numerical simulations conducted in a MATLAB-Simulink environment on a DC-DC half-bridge converter operating in bidirectional mode show the controller's improved performance over its existing counterpart.

Path following for Air Cushion Vehicle using Adaptive Sliding Mode Control with Virtual Control

Path following for Air Cushion Vehicle using Adaptive Sliding Mode Control with Virtual Control

Anti-Sway Adaptive Fast Terminal Sliding Mode Control Based on the Finite-Time State Observer for the Overhead Crane System

This work proposes an adaptive rapid terminal SMC (sliding mode control) approach based on the FFTSO (fast finite-time state observer) for overhead crane trajectory tracking and anti-swing control in the presence of external disturbances and parameter uncertainty. First, the system state observation under the constraint of unknown system parameters is accomplished by designing the FFTSO based on finite-time theory. Next, a parameter-adaptive fast terminal SMC is created for an overhead crane based on the model transformation. This technique can still monitor the intended trajectory and reduce payload swing even in cases when the payload mass and wire rope length are uncertain. Next, the Lyapunov theorem is used to demonstrate the stability of the overhead crane system’s positioning and anti-swing angle control mechanism. Lastly, the platform experiments confirm that the suggested closed-loop system control technique is successful.

Adaptive Sliding Mode Control of Quadrotor System with Elastic Load Connection of Unknown Mass

During quadrotor load transport, the cable’s elasticity exacerbates load fluctuations, which may result in platform instability or a potential crash. This paper introduced a model of the connecting cable as a spring-damper system and established the dynamic model of the suspension system based on Newton’s law. Nonsingular fast terminal sliding mode control (NFTSMC) was employed for attitude, position, and anti-swing controller design. Adaptive controllers were integrated into altitude control to address uncertainties related to load mass and cable length. The inclusion of an anti-swing controller into the position control loop effectively dampens load oscillations while ensuring accurate position tracking. Numerical simulations demonstrated that the proposed controller outperforms both the energy-based controller and the conventional linear sliding mode controller.

Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters

Metaheuristics have become popular tools for solving complex optimization problems; however, the overwhelming number of tools and the fact that many are based on metaphors rather than mathematical foundations make it difficult to choose and apply them to real engineering problems. This paper addresses this challenge by automatically designing optimization algorithms using hyper-heuristics as a master tool. Hyper-heuristics produce customized metaheuristics by combining simple heuristics, guiding a population of initially random individuals to a solution that satisfies the design criteria. As a case study, the obtained metaheuristic tunes an Adaptive Sliding Mode Controller to improve the dynamic response of a DC-DC Buck–Boost converter under various operating conditions (such as overshoot and settling time), including nonlinear disturbances. Specifically, our hyper-heuristic obtained a tailored metaheuristic composed of Genetic Crossover- and Swarm Dynamics-type operators. The goal is to build the metaheuristic solver that best fits the problem and thus find the control parameters that satisfy a predefined performance. The numerical results reveal the reliability and potential of the proposed methodology in finding suitable solutions for power converter control design. The system overshoot was reduced from 87.78% to 0.98%, and the settling time was reduced from 31.90 ms to 0.4508 ms. Furthermore, statistical analyses support our conclusions by comparing the custom metaheuristic with recognized methods such as MadDE, L-SHADE, and emerging metaheuristics. The results highlight the generated optimizer’s competitiveness, evidencing the potential of Automated Algorithm Design to develop high-performance solutions without manual intervention.

Adaptive arbitrary time synchronisation control for fractional order chaotic systems with external disturbances

In this paper, adaptive synchronisation of fractional order chaotic systems with external disturbances is studied. For this purpose, first, a new integer order system is introduced and its convergence to zero exponentially within an arbitrary time is proven. Using this system, a novel adaptive exponential arbitrary time sliding mode controller is established to synchronise the fractional order chaotic systems. Then, to estimate the disturbance terms of the fractional systems an exponential arbitrary time disturbance observer is developed. The use of the disturbance observer can lead to simple controller design, low chattering control input, and more accurate response. Finally, a disturbance observer based sliding mode control is presented, that by incorporating the disturbance estimation in controller design ensures synchronisation of chaotic systems. The exponential arbitrary time synchronisation and the stability of the closed loop control system are confirmed using the Lyapunov theory. Simulation results on synchronisation of different fractional order systems, validate the proposed control schemes. It is noteworthy that the introduced adaptive sliding mode controllers can be used for controlling a wide variety of fractional order systems.

Design of an Adaptive Super-Twisting Sliding Mode Controller to Adjust Voltage in DC Microgrids with Constant Power Load

Design of an Adaptive Super-Twisting Sliding Mode Controller to Adjust Voltage in DC Microgrids with Constant Power Load

Adaptive sliding mode H∞ control for discrete time-varying singular systems with deception attacks

This paper studies the problem of adaptive sliding mode control (SMC) for discrete time-varying singular systems (DTVSSs) with deception attacks. The deception attacks that make the system unstable are modeled as an unknown nonlinear function. Firstly, some sufficient conditions for ensuring the admissibility with H ∞ performance of the system are given. Secondly, a novel method is proposed to solve the controller gain. Thirdly, to estimate the unknown parameter in upper bound of deception attacks, an adaptive SMC law is established to assure that the reachability of sliding surfaces can be achieved and the effect of deception attacks on the system is weakened. Finally, the developed method is verified by two examples.

Review on recent control system strategies in Microgrid

Microgrids (MGs) are integral to the evolving global energy landscape, facilitating the integration of renewable energy sources such as solar and wind while enhancing grid stability and resilience. This review presents a comprehensive analysis of control strategies in MG systems, addressing both conventional and advanced methodologies. We explore traditional control methods, such as droop control and Proportional Integral Derivative (PID) controllers, for their simplicity and scalability, but acknowledge their limitations in handling non-linearities and real-time adaptation. Model Predictive Control (MPC), Adaptive Sliding Mode Control (ASMC), and Artificial Neural Networks (ANN) are some of the more advanced techniques that make systems more flexible, better at managing energy, and stable even when operations change quickly. The review further delves into the role of the Internet of Things (IoT), predictive analytics, and real-time monitoring technologies in MGs, emphasizing their importance in enhancing energy efficiency, ensuring real-time control, and improving system security. The review places emphasis on energy management systems (EMS), which optimize supply and demand balance, reduce uncertainty, and enable seamless integration of distributed energy resources (DERs). The paper also highlights emerging trends such as blockchain, AI-driven controls, and deep learning for MG optimization, security, and scalability. Concluding with future research directions, the paper underscores the need for more robust control frameworks, advanced storage technologies, and enhanced cybersecurity measures, ensuring that MGs continue to play a pivotal role in the transition to a decentralized, low-carbon energy future.

Mathematical modeling of an articulated flying vehicle for precise control analysis in grasping highly oscillatory objects

In this research work, a detailed mathematical modeling approach has been taken in order to develop a complete package for the investigation of phenomena associated with the flight dynamics and control of an articulated aerial robot in grasping high weight oscillatory objects such as living creatures. To this end, the origin of forces and moments generated by a moving target is analyzed and the nonlinear differential equations of the system are derived. Then, the model is verified and the effect of target oscillations on the whole system dynamics is investigated. In the next step, due to the nonlinear nature of this under-actuated system and the presence of the force-generating target with unknown dynamics and model parametric and non-parametric uncertainties, two versions of adaptive sliding mode control (A-SMC) are designed to enable the system follows the nominal trajectories in a desirable way without needing to know the upper bounds of the time-varying forces and uncertainties. In this regard, the stability of closed-loop systems is proved based on Lyapunov theory. The performance and robustness of the designed controllers are evaluated and compared through numerical and Monte Carlo simulations with some standard criteria. At the end, the conclusion of this work is presented.

Robustifying a reinforcement learning agent-based bionic reflex controller through an adaptive sliding mode control

Robustifying a reinforcement learning agent-based bionic reflex controller through an adaptive sliding mode control

Fixed-time adaptive sliding mode-based trajectory tracking control for Wheeled Mobile Robot: Theoretical development and real-time implementation

Wheeled mobile robots (WMR) are common autonomous systems requiring efficient control methods. This work investigates fixed-time adaptive sliding mode control (FxT-ASMC) for the trajectory tracking task of a WMR subject to disturbances/uncertainties. The developed control strategy seeks to avoid the chattering problem, cope with disturbances when the upper limit is undefined, and enhance the convergence of the system states. Indeed, a new adaptive technique has been developed to effectively handle disturbances, even in the absence of prior knowledge regarding their upper bound. This technique consists in estimating the switching gain for each channel separately. On the other hand, an appropriate design of the reaching law ensures the fixed-time convergence of the sliding variables to zero. Additionally, sliding variables are designed in a way that guarantees the tracking errors will stabilize within a set amount of time. This kind of convergence was demonstrated using Lyapunov stability theory. In order to realize the trajectory tracking control structure, the FxT-ASMC system is incorporated with a classical kinematic controller. The efficiency of the suggested control methodology is proven via simulations and practical implementation.

Adaptive Sliding Mode Control for AUV based on Backstepping and Neural Networks

Abstract Automatic Underwater Vehicle (AUV) inevitably suffers from various interference issues in the marine environment. Due to the limitations of underwater measurement methods and tools, as well as the complexity of AUV's control parameters in the underwater environment, dynamic measurement errors are easily cascaded and amplified, leading to the failure of the control system. In response to this phenomenon, this paper focuses on overcoming the influence of internal and external unknown factors in the tracking and control process of AUV trajectories. The internal factors are mainly the uncertainties generated by the model mismatch problem, and the external factors are mainly from the dynamic ocean currents. The AUV disturbances mainly affect the kinematics and dynamics levels directly or indirectly. In order to achieve the control of internal and external dynamic disturbances, we have designed a control system that employs backstepping (BS) and a sliding mode controller (SMC) with RBF neural networks to achieve the cascaded control of kinematics and dynamics. Comparison of multiple sets of simulations shows that our proposed algorithm has excellent anti-disturbance performance for dynamic conditions with low measurement accuracy.

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

Adaptive Reinforcement Learning Strategy-Based Sliding Mode Control of Uncertain Euler–Lagrange Systems With Prescribed Performance Guarantees: Autonomous Underwater Vehicles-Based Verification

Adaptive Reinforcement Learning Strategy-Based Sliding Mode Control of Uncertain Euler–Lagrange Systems With Prescribed Performance Guarantees: Autonomous Underwater Vehicles-Based Verification

Projective Synchronization for Uncertain Fractional Reaction-Diffusion Systems via Adaptive Sliding Mode Control Based on Finite-Time Scheme.

In this article, the projective synchronization of uncertain fractional-order (FO) reaction-diffusion systems is studied for the first time via the fractional adaptive sliding mode control (SMC) method. A FO integral type switching function is designed, and corresponding adaptive SMC laws are derived which ensure the FO sliding mode surface (SMS) is reachable after a finite time interval. The improved version of these control laws which have smaller oscillation and better control performance are also derived. A new lemma for proving the finite-time reachability of the FO SMS is developed. At last, numerical examples are provided to verify the effectiveness of our theories.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

Distributed Cooperative Control of Heterogeneous Multiagent Systems for Air‐to‐Ground Formation

ABSTRACTThis paper is concerned with a distributed cooperative formation problem for a heterogeneous multiagent system with a car‐like mobile robot, a quadrotor, and an omnidirectional wheeled mobile robot. In the heterogeneous multiagent system, a three‐layer distributed cooperative formation control strategy is constructed to achieve air‐to‐ground formation, the car‐like mobile robot tracks a virtual leader, the quadrotor, and the omnidirectional wheeled mobile robot track the car‐like mobile robot. Two nonlinear leader‐follower controllers based on exponential virtual control laws are designed to maintain formation for the quadrotor and the omnidirectional wheeled mobile robot. A kinematics controller with continuous functions, an improved adaptive sliding mode controller and a suitable back‐stepping controller are introduced in the heterogeneous multiagent system, respectively. Multiagent communication is one of the key steps to accomplish multiagent time synchronization. Experimental results are given to illustrate the applicability of the proposed distributed cooperative control strategy.