Robust Adaptive Fuzzy Control Research Articles

Robust Image-Based Adaptive Fuzzy Controller for Guarantee Field of View With Uncertain Dynamics

Robust Image-Based Adaptive Fuzzy Controller for Guarantee Field of View With Uncertain Dynamics

Fuzzy adaptive control of coal gangue sorting parallel robot with variable load

ABSTRACT Delta parallel robot is widely used in the high-speed sorting industry with a light load and constant load. Its motion control is based on kinematic planning, without considering the influence of load and disturbance. The gangue sorting robot has the characteristics of a large load and random mass change, so traditional kinematics planning can not meet the control requirements. A fuzzy adaptive robust control algorithm is proposed to solve the problems of unstable motion and jitter in the coal gangue sorting robot. By establishing the mathematical model of the fuzzy system, the uncertain terms of the dynamic parameters caused by the random changes in the load are approximated. At the same time, to reduce the number of fuzzy rules and the amount of fuzzy calculation, the uncertain terms are decomposed into velocity terms and acceleration terms and then approximated, respectively. The Lyapunov analysis of the system is carried out, and the convergence of the system is proved. The simulation results show that the fuzzy adaptive robust control algorithm has a smaller error and faster convergence compared with the computational torque control algorithm when the system is sorting gangue within 8 kg, which improves the stability of gangue grasping.

Fixed‐time fuzzy adaptive robust H∞ control for nonstrict‐feedback nonlinear systems with input saturation and output constraints

SummaryThis paper focuses on the design of fixed‐time fuzzy adaptive robust control of nonlinear systems in a nonstrict‐feedback form that includes output constraints, input saturation, and external disturbance. Fuzzy systems have been utilized to deal with uncertainties and non‐linearity in the system dynamic model. By introducing novel control and adaptive rules, there is no need to approximate the input saturation function with the hyperbolic tangent function. A Barrier Lyapunov Function (BLF) ensures that systems outputs do not violate the defined constraints. By combining the backstepping and BLF method, asymptotic convergence of tracking error signal to zero in fixed‐time is proved. At the same time, index is also met in dealing with the disturbance effects. Finally, the simulation results of the proposed control method in two examples confirm its effectiveness.

Robust Adaptive Fuzzy Fault Tolerant Control of Robot Manipulators With Unknown Parameters

This paper resolves the safety tracking problem of the robot manipulator with the actuator faults, process faults, uncertain dynamics, and external disturbances. A novel robust adaptive fuzzy fault tolerant control (FTC) framework is presented. In this approach, a constraint mechanism of the zero overshoot error is proposed to keep the filter variable within an adjustable range, and the compact set can be available. An adaptive fuzzy logic system (FLS) is, then, employed to approximate the time-varying, nonlinear, and unknown robot dynamics. Furthermore, a robust adaptive term is designed to compensate the actuator faults and approximation errors, and ensure the convergence and stability of the whole robot control system. The uniform ultimate boundedness of the closed-loop dynamics for the robot manipulator is proved by the Lyapunov function. Afterwards, the simulation results demonstrate that the proposed robust adaptive fuzzy FTC (RAFFTC) approach of the robot manipulator has good control performance. Finally, the effectiveness of the proposed RAFFTC approach is verified by experiments.

Autonomous Trajectory Tracking and Collision Avoidance Design for Unmanned Surface Vessels: A Nonlinear Fuzzy Approach

An intelligent fuzzy-based control system that consists of several subsystems—a fuzzy collision evaluator, a fuzzy collision avoidance acting timing indicator, a collision-free trajectory generator, and a nonlinear adaptive fuzzy robust control law—is proposed for the collision-free condition and trajectory tracking of unmanned surface vessels (USVs). For the purpose of ensuring that controlled USVs are capable of executing tasks in an actual ocean environment that is full of randomly encountered ships under collision-free conditions, the real-time decision making and the desired trajectory arrangements of this proposed control system were developed by following the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs). From the simulation results, several promising properties were demonstrated: (1) robustness with respect to modeling uncertainties and ocean environmental disturbances, (2) a precise trajectory tracking ability, and (3) sailing collision avoidance was shown by this proposed system for controlled USVs.

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

The control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.

Robust adaptive dynamic memory‐event‐triggered attitude control for nonlinear multi‐UAVs resist actuator hysteresis

SummaryThis article studies the memory‐based dynamic event‐triggered (MBDET) robust adaptive fuzzy control problem for a class of uncertain nonlinear MASs subject to Backlash‐Like Hysteresis. The considered MASs are modeled as a class of nonlinear leader‐following multiagent systems with multiple inputs for each subsystem, which can be used to model multiple multi‐rotor unmanned aerial vehicle (Multi‐UAV) attitude systems. In the framework of backstepping recursive design, the fuzzy approximation technology, the parameter estimation technology, and the first‐order filtering technology are integrated, and an effective fuzzy adaptive control method is proposed, which solves the problems of Backlash‐Like Hysteresis and external disturbances in the system while avoiding the “explosion of complexity”. In order to relieve the communication pressure of Multi‐UAV attitude systems, a novel dynamic memory‐event‐triggered mechanism (DMETM) based on user‐set parameters is developed. Compared with the traditional event‐triggered mechanism, this DMETM has larger triggering time intervals, effectively reduces communication frequency, and has greater flexibility in balancing system performance and communication bandwidth resource constraints. The closed‐loop stability analysis scheme for Multi‐UAV attitude systems is presented by combining the backstepping recursive design technique and Lyapunov stability theory, which proves that closed‐loop stability and tracking performance can be ensured. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the dynamic memory‐event‐triggered attitude control problem for nonlinear Multi‐UAVs.

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

AbstractIn this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.

Observer-Based Robust Adaptive TS Fuzzy Control of Uncertain Systems with High-Order Input Derivatives and Nonlinear Input–Output Relationships

Observer-Based Robust Adaptive TS Fuzzy Control of Uncertain Systems with High-Order Input Derivatives and Nonlinear Input–Output Relationships

Pick–and–Place Trajectory Planning and Robust Adaptive Fuzzy Tracking Control for Cable–Based Gangue–Sorting Robots with Model Uncertainties and External Disturbances

A suspended cable–based parallel robot (CBPR) composed of four cables and an end–grab is employed in a pick–and–place operation of moving target gangues (MTGs) with different shapes, sizes, and masses. This paper focuses on two special problems of pick–and–place trajectory planning and trajectory tracking control of the cable–based gangue–sorting robot in the operation space. First, the kinematic and dynamic models for the cable–based gangue–sorting robots are presented in the presence of model uncertainties and unknown external disturbances. Second, to improve the sorting accuracy and efficiency of sorting system with cable–based gangue–sorting robot, a four-phase pick–and–place trajectory planning scheme based on S-shaped acceleration/deceleration algorithm and quintic polynomial trajectory planning method is proposed, and moreover, a robust adaptive fuzzy tracking control strategy is presented against inevitable uncertainties and unknown external disturbances for trajectory tracking control of the cable–based gangue–sorting robot, where the stability of a closed-loop control scheme is proved with Lyapunov stability theory. Finally, the performances of pick–and–place trajectory planning scheme and robust adaptive tracking control strategy are evaluated through different numerical simulations within Matlab software. The simulation results show smoothness and continuity of pick–and–place trajectory for the end–grab as well as the effectiveness and efficiency to guarantee a stable and accurate pick–and–place trajectory tracking process even in the presence of various uncertainties and external disturbances. The pick–and–place trajectory generation scheme and robust adaptive tracking control strategy proposed in this paper lay the foundation for accurate sorting of MTGs with the robot.

A path planning control for a vessel dynamic positioning system based on robust adaptive fuzzy strategy

The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability.

An adaptive robust fuzzy PI controller for maximum power point tracking of photovoltaic system

The transduction of solar energy to electrical energy requires Solar Photovoltaic (SPV) systems, which must be operated at Maximum Power Point (MPP) to extract maximum possible power. Being dependent on environmental factors such as irradiation and temperature, the MPP and, therefore, the SPV system’s performance is nonlinear. A Maximum Power Point Tracking (MPPT) controller is usually employed, which guides the SPV systems to work at MPP. For this task, in this paper, an Adaptive Robust Fuzzy Proportional-Integral (ARFPI) controller for MPPT of an SPV system is proposed. The proposed ARFPI controller parameters have been tuned using Particle Swarm Optimization by minimizing an equal-weighted combination of Integral of the Time-Weighted Absolute Error (ITAE) and Integral of the Absolute Error (IAE). In this combination, ITAE penalized long-term errors offering a faster response, while IAE penalized aggregate errors offering lower ripples. The MPPT performance of the proposed controller has been assessed using undershoot and steady-state ripples for several varying irradiance and temperature profiles with real-world data. Further, to assess its relative performance, it has also been benchmarked against traditional MPPT techniques, i.e., perturb and observe, incremental conductance, and PID controller. The presented investigations revealed clear superiority (reduced ripples and undershoot) of ARFPI controller, and therefore it is concluded to be a potential MPPT controller for the SPV system.

Lyapunov-based Robust Adaptive Configuration of the UAS-S4 Flight Dynamics Fuzzy Controller

AbstractIn tandem with the fast-growing demand for Unmanned Aerial Vehicles (UAVs) for surveillance and reconnaissance, advanced controllers for these critical systems are needed. This paper proposes a flight dynamics controller design that considers various uncertainties for the Hydra Technologies UAS-S4 Ehécatl. In order to be realistic, in addition to flight dynamics nonlinearities, three main sources of uncertainties are considered, as those caused by unknown controller’s parameters, modeling errors, and external disturbances. A Robust adaptive fuzzy logic controller is designed, in charge of nonlinear flight dynamics in presence of a variety of uncertainties. The nonlinear flight dynamics is modeled based on the Takagi-Sugeno method relying on the soft association of local linear models. Since this controller is model-based, an optimal reference model is defined, which is stabilised by the Linear Quadratic Regulator procedure. A fuzzy logic controller is then designed for the nonlinear model. Lastly, with the aim to handle the uncertainties, the gains of the fuzzy controller are reconfigured, and are continuously adjusted by Lyapunov-based robust adaptive laws. The performance of the UAS-S4 Robust adaptive fuzzy logic controller is evaluated in terms of lateral and longitudinal flight dynamics stabilisation, and the reference model state variables tracking under various uncertainties.

Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to direct its movement. The concurrent use of EMG with the elastic joint arm provides a suitable framework for human-robot interaction. EMG signals represent human's ‘intention’ on motion, i.e., they move between 50–100 ms before the mechanical motion begins. Hence this strategy potentially builds better synchronization between the robot and human intention. Furthermore, the adaptive fuzzy strategy eliminates the system chattering while also providing robustness against parametric uncertainties and time delay. Lyapunov analysis also shows bounded-input bounded-output stability of the closed-loop system. Finally, the proposed approach is experimentally implemented in the Sport Science Research Institute. Comparisons with a competing strategy, as well as a torque mode controller, shows that the proposed approach leads to a computationally faster and more accurate controller.

Robust Control Design Based on Perturbation Cancellation for Micro-Positioning Design with Hysteresis

Based on the superiority of the piezoelectric elements, including lightweight, high electric mechanical transformation efficiency and a quick response time, a piezoelectric-based micro-positioning actuator is developed in this investigation. For eliminating the effects of hysteresis and modeling uncertainties that appeared in this micro-positioning actuator, a nonlinear adaptive fuzzy robust control design with a perturbation cancellation ability is proposed for this micro-positioning design to achieve a positioning resolution of 1 μm. Structurally, this proposed robust control methodology contains two particular parts: a universal fuzzy approximator and a robust compensator, which are employed to cancel the modeling uncertainties caused by the perturbed parts of the micro-positioning actuator and mitigate the approximation error between the modeling uncertainties and the universal fuzzy approximator, respectively. From both the numerical simulations and real validations, this proposed micro-positioning design performs a promising positioning performance in the micrometer level.

Optimal robust adaptive fuzzy backstepping control of electro-hydraulic servo position system

In this paper, an optimal robust adaptive fuzzy backstepping control is presented to the position control of the electro-hydraulic servo (EHS) system in the presence of structured and unstructured uncertainties. Initially, the robust control using the backstepping technique is presented to overcome the existing uncertainties in the dynamic equations. Mathematical proof demonstrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability. Then, to overcome the chattering problem, a very simple fuzzy approximator is presented where it approximates the bounds of the uncertainties. Although the proposed robust fuzzy backstepping control has a desirable performance, it has no mathematical analysis to prove the stability of the closed-loop system. Therefore, to solve this problem, the proposed fuzzy approximator has been transformed into a one-law adaptive fuzzy approximator with a single-input single-output fuzzy rule. Mathematical analysis illustrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability under the proposed robust adaptive fuzzy backstepping control. Furthermore, a novel modified harmony search algorithm (MHSA) has been developed, by using the original harmony search algorithm (OHSA) as an optimization technique, to achieve the optimal values of the membership functions and the control coefficients. Finally, a comparative study has been conducted between the proposed control scheme under the MHSA and the OHSA, and other existing advanced control approaches to verify the effectiveness of the proposed control. Results show that the proposed control scheme under the MHSA can suppress the chattering problem and reduce the disturbances effectively while ensuring that the performance is tracked.

Robust Adaptive Fuzzy Control via State-Dependent Function for Nonlinear Stochastic Large-Scale Systems Subject to Dead Zones

This paper concentrates on the adaptive fuzzy control problem for stochastic nonlinear large-scale systems with constraints and unknown dead zones. By introducing the state-dependent function, the constrained closed-loop system is transformed into a brand-new system without constraints, which can realize the same control objective. Then, fuzzy logic systems (FLSs) are used to identify the unknown nonlinear functions, the dead zone inverse technique is utilized to compensate for the dead zone effect, and a robust adaptive fuzzy control scheme is developed under the backstepping frame. Based on the Lyapunov stability theory, it is proved ultimately that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. Finally, an example based on an actual system is given to verify the effectiveness of the proposed control scheme.

Robust adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems with unknown control directions

In this paper, the adaptive fuzzy backstepping control problem is considered for a class of single-input single-output (SISO) unknown uncertain nonaffine nonlinear systems in strict-feedback form. Within this approach, Nussbaum gain functions are introduced to solve the problem of unknown control directions. The unknown nonlinear functions are approximated by employing adaptive fuzzy systems. The stability analysis of the closed-loop system in the sense of Lyapunov guarantees the global boundedness property for all the signals and states, and at the same time, steers the tracking error to a small neighborhood of the origin. The feasibility of the developed control approach is illustrated by numerical simulation.

Parameter tuning of robust adaptive fuzzy controller for 3D elliptical vibration-assisted cutting

Abstract. Elliptical vibration cutting (EVC), as a precision machining technology, is used in many applications. In precision machining, control accuracy plays an essential role in improving the machinability of difficult-to-machine materials. To improve the control accuracy, dynamic and static characteristics of the system need to be tuned to obtain the optimal parameters. In this paper, we use a glowworm algorithm with an improved adaptive step size to tune the parameters of a robust adaptive fuzzy controller. We then obtain the optimal controller parameters through simulation. The optimal solution of the controller parameters is then applied to a 3D EVC system model for simulation and closed-loop testing experiments. The results indicate that a good agreement between the ideal curve and the tracking signal curve verifies the optimality of the controller parameters. Finally, under certain cutting conditions, the workpieces of three different materials are cut with two different cutting methods. The study revealed that the surface roughness value is reduced by 20 %–32 %, which further verifies the effectiveness of the optimal controller's parameters.

Adaptive Fuzzy Robust Control of a Bionic Mechanical Leg With a High Gain Observer

In this paper, an observer-based adaptive fuzzy robust controller is proposed for trajectory tracking control of a bionic mechanical leg (BML) with unmeasured system states, dynamic uncertainties and external disturbances. A high gain observer (HGO) is constructed to estimate the unavailable joint velocities using the joint position feedback signals, while an adaptive fuzzy logic system (AFLS) is employed to address the lumped uncertainties. The nonlinear robust controller is then synthesized via backstepping method to improve the position tracking performance. The stability of the closed loop system is mathematically demonstrated via the Lyapunov’s stability theory. It is proven that under the proposed controller all the closed-loop signals are bounded and the trajectory tracking errors converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme is illustrated by simulation studies.