Earticle

A tremor is an involuntary, somewhat rhythmic, muscle contraction and relaxation involving to and from movements (oscillations or switching) of one or more body parts. It is the most common of all involuntary movements and can affect the hands, arms, eyes, face, head, vocal folds, trunk, and legs. Most tremors occur in the hands. In some people, tremor is a symptom of another neurological disorder. This research focuses on the design and analysis of a model reference-based controller for highly nonlinear dynamic multi degrees of freedom, in presence of hand tremors. In order to provide a high performance nonlinear methodology, the inverse dynamical controller is selected. The inverse dynamic controller is the type of model reference controller. It is nonlinear and stable controller. To reduce the challenges of this controller hybrid fuzzy controller is used. However, this method has many advantages, but it has challenged in presence of uncertainties. To solve this challenge model reference-based direct adaptive methodology is used and applied to hybrid inverse dynamic fuzzy controller.

Refer to this research, an integral criterion-based hybrid fuzzy controller is proposed for vibration reduction in sensitive actuators. The first problem of the fuzzy logic controller was stability in certain and uncertain systems. This problem can be reduced in certain system by using hybrid fuzzy control law. However fuzzy hybrid controller based on PID methodology has advantage in certain system, but it has challenge in presence of uncertainty and motor vibration. To solve this challenge direct adaptive methodology applied to hybrid fuzzy controller. This type of adaptive technique is model-reference variable structure control. Regarding to proposed method, chattering eliminate as well improve the stability and robustness. The simulation results exhibit that the proposed method works well in certain system.

This paper presents the analysis and simulation of the control of double star induction motor, using direct torque control (DTC) based on model reference adaptive control algorithm (MRAC). The DTC is an excellent solution for general- purpose induction drives in very wide range the short sampling time required by the TC schemes makes them suited to a very fast torque and flux controlled drives as well the simplicity of the control algorithm. DTC is inherently a motion sensorless control method. The model reference regulator (MRAC) can improve the double star induction motor performance in terms of overshoot, rapidity, cancellation of disturbance, and capacity to maintain a high level of performance. Simulation results indicate that the proposed regulator has better performance responses. The implementation of the DTC applied to a double star induction motor based on model reference regulator is validated with simulated results.

This paper focuses on a sensorless control method for small wind turbines based on permanent magnet synchronous generators. An adaptive intelligent algorithm is proposed to estimate the rotor speed for designing sensorless controllers of small wind turbines. In the proposed algorithm, a model reference adaptive system has been formed by using the instantaneous and steady-state powers to estimate the rotor speed. Then a power speed feedback (PSF) control carries out maximum power point tracking by using the optimum relationship between speed and power, which is kept in a lookup table. The efficiency of the system is improved by vanishing the observation errors and tracking errors. The validity of the proposed algorithm is verified by the simulation results.

This paper aims to develop an adjustable gain-based model reference adaptive control (AG-MRAC) scheme for maximum power point tracking (MPPT) in photovoltaic (PV) systems. To ensure a fast convergence speed with guaranteed transient performance and overall system stability of the MPPT under rapidly changing environmental conditions, a 2-level control scheme has been proposed. The first level of control is the conventional ripple correlation control (RCC), which is used to obtain a ripple-free optimal duty cycle in the steady-state. This also serves as input for the second level of control, which is the proposed AG-MRAC controller. The conventional high-static adaptation gain MRAC provides guaranteed transient performance in MPPT. However, a high adaptation gain circumvents the adverse effects on the system stability and robustness. Moreover, in PV systems, when the environmental conditions are rapidly changing, the requirement gain depends on the magnitude of the error. Therefore, a fixed high gain controller does not provide a complete solution to the dynamic behavior of non-linear PV systems under rapidly changing environmental conditions. This paper attempts to overcome these issues using the proposed AG-MRAC architecture, where the adaptation gain is adjusted as a function of the tracking error, which is caused by variations in environmental conditions. A mathematical model of the proposed AG-MRAC has been developed and its stability has been verified using Lyapunov theory. To check the effectiveness of the proposed control scheme, simulation and experimental models have been developed for validation. In addition, a performance comparison has been done with recent similar works.

The control scheme using fuzzy modeling and Parallel Distributed Compensation (PDC) concept is proposed to provide asymptotic tracking of a reference signal for the flexible joint manipulators with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop multi-input/multi-output system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.