Fuzzy sliding mode control of a multi-DOF parallel robot in rehabilitation environment

Multi-degrees of freedom (DOF) parallel robot, due to its compact structure and high operation accuracy, is a promising candidate for medical rehabilitation devices. However, its controllability relating to the nonlinear characteristics challenges its interaction with human subjects during the rehabilitation process. In this paper, we investigated the control of a parallel robot system using fuzzy sliding mode control (FSMC) for constructing a simple controller in practical rehabilitation, where a fuzzy logic system was used as the additional compensator to the sliding mode controller (SMC) for performance enhancement and chattering elimination. The system stability is guaranteed by the Lyapunov stability theorem. Experiments were conducted on a lower limb rehabilitation robot, which was built based on kinematics and dynamics analysis of the 6-DOF Stewart platform. The experimental results showed that the position tracking precision of the proposed FSMC is sufficient in practical applications, while the velocity chattering had been effectively reduced in comparison with the conventional FSMC with parameters tuned by fuzzy systems

Relative Rate Observer Self-Tuning of Fuzzy PID Virtual Inertia Control for An Islanded microgrid

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids\u27 dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC\u27s success in showing remarkable improvement in the microgrid\u27s dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions

Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement

Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection

An improved artificial dendrite cell algorithm for abnormal signal detection

In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy

Direct force control of upper-limb exoskeleton based on fuzzy adaptive algorithm

In order to synchronize human and machine positions and minimize human-machine interaction forces in exoskeleton control, we present a two-degree-of-freedom (2-DOF) upper-limb exoskeleton model with power enhancement and direct force control strategy based on fuzzy adaptive algorithm. The conventional PD controller is widely used in exoskeleton control because it is model independent and its gains can be easily tuned. However, the speed of movement of the operator and the mass of external load are uncertain in practice; hence, the parameters of a conventional PD controller have to be adjusted according to the velocity of the motion and external loads to ensure the effectiveness of trajectory tracking. Additionally, there is a lag in the response time when the operator starts to move or changes direction suddenly. Therefore, this study proposes the use of an adaptive controller combining the fuzzy set techniques and PD controller to improve trajectory tracking. Robustness testing of the fuzzy PD controller for the external load uncertainty and motion velocity change are also investigated. The simulation results clearly indicate the superior performance of the fuzzy adaptive PD controller over the conventional one for tracking performance with external load uncertainty and motion velocity variance

Fuzzy-enhanced Dual-loop Control Strategy for Precise Nanopositioning

Postprin