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In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

In this study, an intelligent adaptive interaction torque control for a pneumatically actuated forearm rehabilitation robot has been proposed. The main objective is to provide a haptic environment that ensures stable interaction torque fields at changing levels. To achieve this goal, a cascade fuzzy adaptive controller, that is specifically tailored to handle varying levels of interaction torque and ensure stability throughout the rehabilitation process, has been designed. To improve the efficiency of the controller, non-linear friction torque identification of the pneumatic actuator based on changing operating conditions has been conducted. Parallel to this, a user motion intention detection algorithm has been designed to provide compliant, safe and suitable human-robot interactions. The disturbance cases have been considered to make the system robust to unknown conditions. Stability analysis has been performed, specifically focusing on the boundary-input boundary-output (BIBO) stability conditions. In order to demonstrate the superior performance of the proposed cascade fuzzy adaptive algorithm, a cascade PID algorithm has also been meticulously designed for comparison. Numerous experimental validation tests involving a healthy user were conducted in a Hardware-in-the-Loop environment, focusing on torque trajectory tracking performance. The proposed control technique exhibited improved convergence dynamics compared to the cascade PID algorithm, yielding mean absolute error levels of 0.0218 Nm and 0.099 Nm for target interaction torque under disturbance-free and disturbed conditions, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the dynamic window approach (DWA) has low planning efficiency and it is difficult to avoid fast-moving obstacles, an improved adaptive DWA based on risk degree function is proposed in this paper. The risk function is designed and introduced into the evaluation function of the traditional DWA to evaluate the risk of collision between dynamic obstacles and the robot, so that the robot can effectively avoid faster obstacles. Then, according to the fuzzy control principle, the adaptive weight coefficient is designed to improve the evaluation function, so that the mobile robot can move to the target point more efficiently. The simulation results show that compared with the traditional DWA, the adaptive DWA based on risk degree function reduces the time of completing the planning task by about 8%, and the path length after the completion of the planning by about 8%, it indicates that the improved algorithm has higher efficiency. After 50 repeated experiments, using the adaptive DWA based on risk degree function successfully avoids all obstacles to complete the planning task, which shows that this algorithm has higher security. [ABSTRACT FROM AUTHOR]

An adaptive sliding mode control method based on the fuzzy exponential convergence law is proposed to solve the jitter problem caused by the sliding mode control of the servo-feed system and to improve the tracking performance of the system. The design of fuzzy rules for adaptive adjustment of convergence law parameters in sliding mode control improves the convergence speed of the sliding mode function, eliminates unknown disturbances in the system, and weakens the chattering of the system. The proposed method is simulated and experimentally verified by a parallel mobile platform. The results show that the sliding mode control method based on the fuzzy convergence law has strong disturbance suppression capability, high position tracking accuracy, and effective chattering suppression, and the maximum tracking error is reduced by 43.1% and 31.5%, respectively, compared with PID control and exponential sliding mode control. [ABSTRACT FROM AUTHOR]

For lower limb rehabilitation robots, different patients or patients in different rehabilitation stages have different motion abilities, and the parameters of the traditional impedance control model are fixed and cannot achieve the best active suppleness training effect. In this paper, an active training control method based on the spring damping model (SDM) and the fuzzy adaptive adjustment of its parameters is proposed. The SDM offsets the target trajectory according to the patient interaction force to obtain a new desired trajectory, creating a controllable impedance environment for the patient. Fuzzy rules are established using coefficients reflecting the patient's motion ability to adaptively adjust the stiffness and damping coefficients of the SDM. The virtual human–machine force interaction environment is changed to achieve the adaptive adjustment of the resistance training difficulty on the motion ability. The adaptive impedance control method proposed in this paper has achieved the expected goal through experimental verification, which can greatly mobilize the active participation of patients and help improve the rehabilitation effect of patients. [ABSTRACT FROM AUTHOR]

In order to solve the problem of load variation and nonlinear strong coupling of the quadrotor load unmanned aerial vehicle (UAV), this paper proposes a fuzzy adaptive linear active disturbance rejection control algorithm based on the Kalman filter (KFFA-LADRC). Firstly, according to the established dynamics model of the quadrotor load UAV, the linear extended state observer (LESO) and the controller based on the bandwidth method are designed. Secondly, to enhance the system’s adaptability and robustness, a real-time fuzzy adaptive controller is introduced to dynamically adjust the controller parameters. Furthermore, to tackle uncertainties disturbances arising from sensor noise and unknown external disturbances, the Kalman filter is utilized to predict the output state, thereby providing the optimal estimation input for the LESO. The approach not only achieves stable control of internal interference, but also reduces the dependence of the Kalman filter on the mathematical model. Finally, simulation results substantiate the efficacy of the proposed KFFA-LADRC, highlighting its robustness and adaptability when accommodating variations in load mass, sensor noise, and external interferences within unfamiliar environments.

To resolve the difficult control issue of intelligent new energy automobile in the process of vehicle control, the study starts from the vehicle motion mode and splits the vehicle control into two parts, transverse control and longitudinal control. Among them, the longitudinal speed control is controlled by an improved adaptive PID model, while the lateral angle control is dominated by an indistinct adaptive PID model, afterwards longitudinal speed control is combined with the lateral angle control for joint control. Finally, the study uses the actual lane simulation to verify the three perspectives of transverse control, longitudinal control and joint control respectively. It shows that the longitudinal control and lateral control are well realized with the improved adaptive PID model control effect at 8km, 15km and 20km per hour. Meanwhile, maximum trajectory error of the combined direction and transverse dominate is always lower than 0.5m at vehicle speeds of 8km/h, 16km/h and 20km/h in the integrated road environment and the combined longitudinal speed and transverse angle control performance is good in the curves. This shows that the model designed by the research can realize adaptive real-time precision control. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to accurately control the bending angle of a finger rehabilitation robot driven by a shape memory alloy ( SMA ) wire, this paper proposes a fuzzy adaptive PID control algorithm. Based on the kinematics model and the alloy wire drive model of a finger rehabilitation robot, this method utilizes the bending angle feedback of resistance mapping and adopts fuzzy adaptive parameters to realize position control. An experimental test platform for finger rehabilitation robots is built, the traditional PID and fuzzy adaptive PID control methods are used to conduct position control experiments on the index finger, and position control effects of each index finger joint after stability are analyzed. The research results show that, compared with the traditional PID control algorithm, the fuzzy adaptive control algorithm takes 2 ~3 s longer to reach the angle steady state time, the adjustment time is shorter, the position error is 2°, and the adaptive adjustment can be completed within 3 s under external disturbance conditions. The proposed method has strong robustness and can better compensate the hysteresis in the SMA phase changing process, which proves its effectiveness. [ABSTRACT FROM AUTHOR]

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC. [ABSTRACT FROM AUTHOR]

For lower limb rehabilitation robots, different patients or patients in different rehabilitation stages have different motion abilities, and the parameters of the traditional impedance control model are fixed and cannot achieve the best active suppleness training effect. In this paper, an active training control method based on the spring damping model (SDM) and the fuzzy adaptive adjustment of its parameters is proposed. The SDM offsets the target trajectory according to the patient interaction force to obtain a new desired trajectory, creating a controllable impedance environment for the patient. Fuzzy rules are established using coefficients reflecting the patient’s motion ability to adaptively adjust the stiffness and damping coefficients of the SDM. The virtual human–machine force interaction environment is changed to achieve the adaptive adjustment of the resistance training difficulty on the motion ability. The adaptive impedance control method proposed in this paper has achieved the expected goal through experimental verification, which can greatly mobilize the active participation of patients and help improve the rehabilitation effect of patients.

An adaptive sliding mode control method based on the fuzzy exponential convergence law is proposed to solve the jitter problem caused by the sliding mode control of the servo-feed system and to improve the tracking performance of the system. The design of fuzzy rules for adaptive adjustment of convergence law parameters in sliding mode control improves the convergence speed of the sliding mode function, eliminates unknown disturbances in the system, and weakens the chattering of the system. The proposed method is simulated and experimentally verified by a parallel mobile platform. The results show that the sliding mode control method based on the fuzzy convergence law has strong disturbance suppression capability, high position tracking accuracy, and effective chattering suppression, and the maximum tracking error is reduced by 43.1% and 31.5%, respectively, compared with PID control and exponential sliding mode control.

Based on fuzzy adaptive and dynamic surface (FADS), an integrated guidance and control (IGC) approach was proposed for large caliber naval gun guided projectile, which was robust to target maneuver, canard dynamic characteristics, and multiple constraints, such as impact angle, limited measurement of line of sight (LOS) angle rate and nonlinear saturation of canard deflection. Initially, a strict feedback cascade model of IGC in longitudinal plane was established, and extended state observer (ESO) was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system, including aerodynamic parameters perturbation, target maneuver and model errors. Secondly, aiming at zeroing LOS angle tracking error and LOS angle rate in finite time, a nonsingular terminal sliding mode (NTSM) was designed with adaptive exponential reaching law. Furthermore, combining with dynamic surface, which prevented the complex differential of virtual control laws, the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law. Finally, the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection. The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded, rigorously proven by Lyapunov stability theory. Hardware-in-the-loop simulation (HILS) and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms.

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately.

In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.

Multi--robot cooperation is widely used in cooperative handling, stamping, welding and other fields, with higher precision and more flexibility. The interpolation algorithm and the force/position control performance determine the smoothness of the trajectories and the stability of the cooperative operation, and also directly affect the bearing capacity and the operation quality. In this paper, the force/position control technology in the process of two robots cooperative handling was studied. First, the clamping characteristics and stress state of two robots cooperative handling were analyzed, and the ideal mathematical model of cooperative handling system was established. Next, according to the mathematical model of the second--order damping system of the master--slave robot, the master--slave cooperative control mode was adopted to design the force/position control strategies respectively. The optimized fuzzy self--tuning position control based on the ideal position was presented for the master robot, and the damping control with the optimized bacterial foraging algorithm based on the force optimization of position deviation of the master robot was presented for the slave robot, and the design of the controller was carried out. Then, the main body of the algorithm and its cost function were programed in MATLAB, and the control system was established in Simulink. Finally, the whole controller was simulated. The simulation results were as follows. The maximum position error of the master was 1.53 mm, and the maximum force error of the slave was 0.038 N. The position error of tracking down between the slave and the master was within 0.18 mm, and the slave could quickly correct the master--slave speed deviation within 0.2 s. The simulation results showed that compared with the conventional PD control method, the proposed controller could reduce the maximum tracking error by 40% and effectively eliminate the zero--crossing oscillations of the force tracking. This method could significantly improve the dynamic performance of the system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on the observer-based fuzzy adaptive fault-tolerant tracking control problem for uncertain nonlinear systems subject to unmeasured states and unmatched external disturbances. By designing a high gain state observer and a disturbance observer, unmeasured states and unmatched external disturbances are estimated and robust tracking performance is improved. Moreover, the barrier-type functions are introduced to the backstepping design procedure to address the problem that all states do not violate their constraint bounds. Finally, a novel fault-tolerant control scheme for output feedback is proposed by combining with the projection technique. By designing appropriate Lyapunov functions, it is concluded that all signals of the plant are bounded and the desired tracking error can be regulated to a small neighborhood around the origin. The simulation results show the effectiveness of the designed control scheme. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the design and the performance comparison of a classic PID controller, a feedforward PID controller, and a fuzzy PID controller. These controllers have been implemented to control the outlet temperature of a shell and tube heat exchanger. The controller with feedforward is proposed to provide assistance in the closed-loop control, in order to reduce the disturbances that affect the system. This method is compared with the fuzzy one, which is based on the concepts of artificial intelligence, genetic algorithm, neural networks, and fuzzy logic. For this controller, Gaussian membership functions have been established, a universe of discourse ranging from -6 to 6 for the error and its derivative, and a universe of discourse ranging from -0.5 to 0.5 for the control variables. From the results of this study, it has been concluded that the tracking error in the output temperature under the variation of the set-point decreases in a faster way in the PID with feedforward respect to the classical PID. However, the fuzzy adaptive PID control is based on the optimized parameters. Copyright © 2020 Praise Worthy Prize S.r.l. - All rights reserved. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Engineering Science / Gongcheng Kexue Yu Jishu is the property of Advanced Engineering Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Optimum: Journal of Economics & Management Sciences / Ekonomi ve Yönetim Bilimleri Dergisi is the property of Optimum: Journal of Economics & Management Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The underwater glider is a kind of novel invention that has been proven to be perfect for long-duration, wide-range marine environmental monitoring tasks. It is controlled by changing the buoyancy and adjusting the posture. For precise control of the underwater glider’s trajectory, a fuzzy adaptive linear active disturbance rejection control (LADRC) is designed in this paper. This controller allows the glider to dive to a predetermined depth precisely and float at a specific depth. In addition, the controller takes some important factors into account, such as model uncertainty, environmental disturbances, and the limited dynamic output of the actual mechanical actuator. Finally, simulation results show the superiority of this fuzzy adaptive LADRC control method. Particularly, when the underwater glider was controlled to dive 100 m at a predetermined attitude angle θ = −1 rad, the maximum overshoot of FLADRC is reduced by 75.1%, 56.6% relative to PID, LADRC, respectively.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract A fuzzy adaptive non-affine attitude tracking control method is proposed for a generic hypersonic flight vehicle (HFV). Due to the complexities of the hypersonic flows and nonlinear dynamics, the aerodynamic coefficients of the HFV are dependent not only on the attack of angle, sideslip angle, angular rates, and Mach number, but also on the deflection angles of the control surfaces. This cause the attitude tracking control problem becomes a non-affine multi-input–multi-output (MIMO) one. By analyzing the characteristics of the aerodynamic coefficients, it can be found that the non-affine terms have a great influence on the attitude dynamics and should not be ignored. Then, a non-affine MIMO attitude tracking controller is designed using fuzzy sliding mode adaptive techniques with consideration of the non-affine nonlinear aerodynamic coefficients. The proposed controller has good practicability because it does not need to know the exact bound values of the uncertainties, unmodeled dynamics, and external disturbances. Finally, simulation results show that the attitude angles can track the desired signals robustly and asymptotically under the control. [ABSTRACT FROM AUTHOR]

In the magnetically suspended momentum wheel, the orientation of the rotor shaft can be actively changed to generate 2-D torques. An adaptive back-stepping sliding mode control (SMC) method is proposed to precisely control the deflection angles. First, the magnetic torques generated by the magnetic bearings are analyzed. According to the magnetic torques, coupled torques exist and influence the tracking control of the deflection angles. It is difficult to model the coupled torque disturbances because their relationship with translations, deflection angles, currents, and other parameters is complex. By analyzing the dynamics and the magnetic torques, the tracking error dynamic is modeled as a system with unmodeled disturbances and parameter uncertainties. A back-stepping integral sliding mode controller whose sliding surface considers the integrals of the angle error and the tracking error is designed for this system. The switching gain should be relatively large to ensure system stability. This will induce severe chattering, which hinders the precision of the deflection angle. A fuzzy algorithm whose inputs include the sliding value and deflection angle is designed to adaptively tune the switching gain of the sliding mode method. According to the simulation and experimental results, the proposed method improves tracking performances and reduces chattering. [ABSTRACT FROM AUTHOR]

Recently, various rehabilitation robots have been developed for therapeutic exercises. Additionally, several control methods have been proposed to control the rehabilitation robots based on user's motion intention. One of the common control methods used is torque-based impedance control. This paper presents an electromyogram-based robust impedance control for a lower-limb rehabilitation robot using a voltage-based strategy. The proposed control strategy uses surface electromyogram (sEMG) signals in place of force sensors to estimate the exerted force. In addition, the control is based on the voltage control strategy, which differs from the common torque control strategies. For example, unlike the torque-based impedance control, the controller is not dependent on the dynamical models of the patient and the robot. This is particularly important as the dynamic of the patient is both difficult to model precisely and changes during the rehabilitation period. These simplifications results in a significant reduction in calculation time. To illustrate the effectiveness of the control approach, a 1-DOF lower-limb rehabilitation robot is designed. Experimental sEMG-force data are collected and used to train an artificial neural network. Simulation results show that compared with a torque-based control approach, the voltage-based is simpler, less computational and more efficient while it considers the presence of actuators. Finally, we design an adaptive fuzzy system to estimate and compensate the uncertainty in performing the impedance rule. The adaptive fuzzy system has an advantage that does not need new feedback to estimate the uncertainty. The control approach is further verified by stability analysis. Simulation results show the efficiency of the control approach in performing some therapeutic exercises. [ABSTRACT FROM AUTHOR]

Stability is a challenging problem for control system of the hypersonic vehicle when partial loss of effectiveness fault and stuck fault happen on elevators and engine. In this paper, a fuzzy adaptive nonlinear fault-tolerant control (FTC) method based on Nussbaum gain technique is proposed for hypersonic vehicles with diverse faults. The cases that one of elevators is lock-in-place and another elevator or the engine is partial loss of effectiveness are addressed. The longitudinal model is transformed into the strict feedback formation. The baseline controllers for altitude and velocity commands tracking are designed using dynamic surface control (DSC) and dynamic inversion. The partial loss of effectiveness faults of elevators and engine are combined in the control gain functions, and Nussbaum approach is introduced to avoid singularity of controllers. The unknown nonlinear functions involving the stuck fault and original uncertain nonlinear items are approximated by fuzzy logic systems. The norm estimation technique is utilized to reduce the number of fuzzy adaptive parameters. This greatly alleviates the calculation burden. It is guaranteed that all signals of the closed-loop FTC system are semi-global uniformly ultimately bounded. Finally, the numerical simulations involving diverse faults demonstrate the effectiveness of the proposed FTC approach. [ABSTRACT FROM AUTHOR]