Model Reference Input Shaping Using Quantitative Feedforward-Feedback Controller

Input shaping convolutes the reference signal with a sequence of impulses, whose amplitudes and timings are designed to produce a shaped reference that avoids exciting lightly-damped modes to reduce residual vibration from a quick movement. The input shaper can be made robust to uncertain mode parameters by adding more impulses, which delays the reference signal, resulting in longer move time. Instead of using more impulses, in this paper, a feedforward-feedback control system, based on the quantitative feedback theory, is placed in the loop to match the closed-loop system, with uncertain plant, to a known reference model. The feedforward-feedback system handles the uncertainty, so the input shaper, placed outside the loop, needs not be robust. The closed-loop system emphasizes on selected frequencies and reduces the cost of feedback. It is shown that the proposed feedforward-feedback system is less conservative than the pure-feedback system. Other sources of vibration such as external disturbances and noise can be handled by the feedforward-feedback system as well. Simulation shows that the proposed technique can withstand large plant uncertainty with fast move time when compared to traditional robust input shaper

State of the art of control schemes for smart systems featuring magneto-rheological materials

This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

Nonlinear control for Two-Link flexible manipulator

Recently the use of robot manipulators has been increasing in many applications such as medical applications, automobile, construction, manufacturing, military, space, etc. However, current rigid manipulators have high inertia and use actuators with large energy consumption. Moreover, rigid manipulators are slow and have low payload-to arm-mass ratios because link deformation is not allowed. The main advantages of flexible manipulators over rigid manipulators are light in weight, higher speed of operation, larger workspace, smaller actuator, lower energy consumption and lower cost. However, there is no adequate closed-form solutions exist for flexible manipulators. This is mainly because flexible dynamics are modeled with partial differential equations, which give rise to infinite dimensional dynamical systems that are, in general, not possible to represent exactly or efficiently on a computer which makes modeling a challenging task. In addition, if flexibility nature wasn\u27t considered, there will be calculation errors in the calculated torque requirement for the motors and in the calculated position of the end-effecter. As for the control task, it is considered as a complex task since flexible manipulators are non-minimum phase system, under-actuated system and Multi-Input/Multi-Output (MIMO) nonlinear system. This thesis focuses on the development of dynamic formulation model and three control techniques aiming to achieve accurate position control and improving dynamic stability for Two-Link Flexible Manipulators (TLFMs). LQR controller is designed based on the linearized model of the TLFM; however, it is applied on both linearized and nonlinear models. In addition to LQR, Backstepping and Sliding mode controllers are designed as nonlinear control approaches and applied on both the nonlinear model of the TLFM and the physical system. The three developed control techniques are tested through simulation based on the developed dynamic formulation model using MATLAB/SIMULINK. Stability and performance analysis were conducted and tuned to obtain the best results. Then, the performance and stability results obtained through simulation are compared. Finally, the developed control techniques were implemented and analyzed on the 2-DOF Serial Flexible Link Robot experimental system from Quanser and the results are illustrated and compared with that obtained through simulation

Intelligent Backstepping System to Increase Input Shaping Performance in Suppressing Residual Vibration of a Flexible-Joint Robot Manipulator

Input shaping technique can be used to suppress residual vibration, occurring from moving rapidly a flexible system from one point to another point. An input shaping filter produces a shaped input signal that avoids exciting the flexible modes of the flexible system. The technique requires accurate knowledge of mode parameters. When the plant model is not accurate, performance of the input shaper degrades. Several robust input shapers were proposed to handle this inaccuracy at the expense of longer move time. The purpose of this paper is, for the first time, to present an application of an intelligent backstepping system to matching of the resulting closed-loop system with a reference model. The input shaper can then be designed from the mode parameters of the reference model. Because the reference model is accurate even when the plant model is not, the input shaper needs not be robust, resulting in shorter move time. The intelligent backstepping system consists of a three-layer neural network, a variable structure controller, and a backstepping controller. The neural network is used as a black-box model in case when the plant model is unknown, making the proposed system model-independent. The adaptive property of the neural network also makes the proposed system suitable for nonlinear, time-varying, or configuration-dependent systems. The variable structure controller handles the uncertainty arisen in the system. The backstepping controller, through its virtual controls, provides a means for the control authority to reach the unmatched uncertainty in the system. This study contains simulation and experimental results on a flexible-joint robot manipulator. The results showed that this proposed intelligent input shaping system outperformed previously proposed robust input shapers in terms of allowable uncertainty amount and move time. The proposed system is also relatively easy to apply because it does not require the plant model

Modelling and control of a two-link flexible manipulator using finite element modal analysis

This thesis focuses on Finite Element (FE) modeling and robust control of a two-link flexible manipulator based on a high resolution FE model and the system vibration modes. A new FE model is derived using Euler-Bernoulli beam elements, and the model is validated using commercial software Abaqus CAE. The frequency and time domain analysis reveal that the response of the FE model substantially varies with changing the number of elements, unless a high number of elements (100 elements in this work) is used. The gap between the complexity of the high order FE model capable of predicting dynamics of the multibody system, and suitability of the model for controller design is bridged by designing control schemes based on the reduced order models obtained using modal truncation/H8 techniques. Two reduced order multi-input multi-output modal control algorithms composed of a robust feedback controller along with a feed-forward compensator are designed. The first controller, Inversion-based Two Mode Controller (ITMC), is designed using a mixed-sensitivity H8 synthesis and a modal inversion-based compensator. The second controller, Shaping Two-Mode Controller (STMC), is designed with H8 loopshaping using the modal characteristics of the system. Stability robustness against unmodelled dynamics due to the model reduction is shown using the small gain theorem. Performance of the feedback controllers are compared with Linear Quadratic Gaussian designs and are shown to have better tracking characteristics. Effectiveness of the control schemes is shown by simulation of rest-to-rest maneuver of the manipulator to a set of desired points in the joint space. The ITMC is shown to have more precise tracking performance, while STMC has higher control over vibration of the tip, at the expense of more tracking errors

Online Deflection Compensation of a Flexible Hydraulic Loader Crane Using Neural Networks and Pressure Feedback

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Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency