A novel control scheme utilizing fractional-order terminal sliding mode control with sliding perturbation observer (FOTSMCSPO) is proposed herein. It was used to solve the trajectory tracking problem on a seven-degree-of-freedom robot manipulator. The fractional-order terminal sliding mode in FOTSMCSPO provides a finite convergence time for reaching the sliding surface. In this article, a sliding perturbation observer (SPO) is used to estimate the disturbance from the environment and modeling uncertainties. The new control scheme exhibits high precision, fast convergence, and robust performance against the lumped uncertainties. These advantages are ensured through the newly designed fractional-order terminal sliding surface and the SPO. The stability is analyzed based on the Lyapunov functions for general and fractional-order systems. An implementation on a real robot manipulator demonstrated the effects and performance of the new control method.

Trajectory Tracking Control Using Fractional-Order Terminal Sliding Mode Control With Sliding Perturbation Observer for a 7-DOF Robot Manipulator

An adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high-performance six-phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field-oriented six-phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six-phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six-phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.

Adaptive Backstepping Control of Six-Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

As wind energy becomes one of the fastest growing renewable energy resources, the control of large‐scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable‐speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non‐affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter‐free controller. In addition, a high‐gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed‐loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

A fuzzy multiresolution wavelet neural network (FMWNN) controller with dynamic compensation (DC) is proposed to address the complexities of the electric load simulator (ELS). The FMWNN acts as a main torque tracking controller, which takes full advantage of the merits of an ideal sliding mode, fuzzy rules, and multiresolution WNN. The fuzzy algorithm is used to dynamically adjust the weights of the WNN and effectively accelerate the convergence rate. In addition, the DC controller is designed to greatly decrease the effect of the approximation error and guarantee the system stability in the sense of the Lyapunov theory. Finally, the proposed algorithms are carried out on the semiphysical simulation platform, the precision and superiority of which are comparatively verified based on the simulation results.

/pdf/control-of-the-electric-load-simulator-using-fuzzy-4qc4i8nczb.pdf

Control of the Electric Load Simulator Using Fuzzy Multiresolution Wavelet Neural Network with Dynamic Compensation

The inherent non-linear factors and the interference of external surplus torque of the electric load simulator make it difficult for the conventional control methods to achieve satisfactory control effect. The cerebellar model articulation controller (CMAC) is widely used because of its simple structure and quick learning. However, conventional CMAC utilizes binary 0 and 1 logic, which will lead to the divergence during the torque tracking. Inspired by the cognitive characteristics of the human brain, the fuzzy logic is implemented to CMAC. In this paper, we design a naive method of applying fuzzy logic to CMAC (NFCMAC), which can take both advantages of the local neural network and the global neural network, and present a new interpretation of the entire network. With parallel control of a PD controller, the NFCMAC–PD control strategy has been successfully applied to the electric load simulator. Dynamic simulation and experimental results have indicated that the NFCMAC–PD control strategy can ensure the ...

A naive method of applying fuzzy logic to CMAC in electric load simulator

This paper presents techniques for cooperation and task execution of an anthropomorphous two-armed robot with two seven degrees of freedom arms. An inverse kinematic solver is proposed to find all joint angles for given postures of two effectors on both arms. Any task can always be separated into many sub-tasks, in which some sub-tasks may have the same action. For executions of these sub-task can be realized by composition of the motion processes. In particular, this paper will emphasize on the description of the task regarding how to coffee making via two-armed cooperation, because this task requires various motion. Experimental results are conducted for illustration of effectiveness of the proposed methods.

Cooperation and task execution of an anthropomorphous two-armed robot: An application to coffee making

This paper presents two intelligent adaptive controllers, called self-balancing and speed controllers, for self-balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self-balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding-mode controller with online learning FBFN is proposed to accomplish self-balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory-built electric unicycle.

Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

This paper presents an adaptive steering control of an electric seatless unicycle using fuzzy cerebella model articulation controller (FCMAC), which are employed to approximate the frictions between the wheel and terrain surface. A backstepping controller with on-line parameter tuning rules is then presented to simultaneously achieve self-balancing and forward motion of the vehicle based on the rider's body inclination. The performance and merit of the proposed method are well exemplified by conducting one simulation on an electric seatless unicycle.

Adaptive steering control using fuzzy CMAC for electric seatless unicycles

This paper presents techniques for adaptive steering control of an electric unicycle with a steering turning mechanism, in order to achieve self-balancing and speed tracking simultaneously. A state feedback control method using the linear quadratic regulator approach is first presented to simultaneously achieve self-balancing and speed tracking, and then an adaptive friction compensating term is proposed to overcome frictions caused by different terrain surfaces. The performance and merit of the proposed control method are well exemplified by conducting two simulations and five experiments on a laboratory-built electric unicycle.

Adaptive steering control of an electric unicycle

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Cooperation and task execution of an anthropomorphous two-armed robot: An application to coffee making

Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

Adaptive steering control using fuzzy CMAC for electric seatless unicycles

Adaptive steering control of an electric unicycle