Visual servoing with hand-eye manipulator-optimal control approach

This paper presents how the control performance of the visual servo system is improved by utilizing the redundant features. We use the minimum singular value of the image Jacobian as a measure of the control accuracy (sensitivity) and we show that the sensitivity is strictly decreased by increasing the number of redundant features. Effectiveness of the control scheme with redundant features are verified by real time experiments on a PUMA 560 manipulator.

This research regards to a two-dimensional lateral pushing nanomanipulation using Atomic Force Microscope (AFM). Yet a reliable control of the AFM tip position during the AFM-based manipulation process is a chief issue since the tip can jump over the target nanoparticle and then the process can fail. However, a detailed Modeling and understanding of the interaction forces on the AFM tip is important for prosperous manipulation control and a nanometer resolution tip positioning. In the proposed model, Lund-Grenoble (LuGre) dynamic friction model is used as friction force on the contact surface between the nanoparticle and the substrate. This model leads to a stick-slip behavior of the nanoparticle that is so similar to the experimental behavior in nanoscale. Derjaguin interaction force is applied between the AFM tip and the nanoparticle which considers both attractive and repulsive interactions. The AFM is modeled by lumped-parameters model. A controller is designed based on the proposed dynamic model in order for positioning the AFM tip during a desired nanomanipulation task. Optimal sliding mode approach is used to design the controller. In this approach sliding surface which is used in the sliding mode approach is selected optimally based on the linear quadratic (LQ) method.

In this paper, an asymmetric cable-driven mechanism is proposed for accurate force control of exoskeleton systems with a compact structure. Inspired by the fact that the required forces in human motions are not symmetric in many cases, a spring-actuator type cable-drive mechanism is adopted, which enables a compact cable routing structure. The joint is connected with the exoskeleton frame through a rotary series elastic mechanism to transmit the desired force to the human user. High performance in force control is achieved by advanced control algorithms, which combine a proportional and differential (PD) controller optimized with a linear quadratic (LQ) method with a disturbance observer (DOB) and a zero phase error tracking (ZPET) feedforward filter. The proposed system was tested for the elbow joint. Experimental results confirmed that the proposed system was able to generate and deliver accurate force to the human user even with external disturbances and modeling uncertainties introduced by human motions.

Optimal LQ feedforward tracking with preview: Practical design for rigid body motion control

An optimal discrete controller is presented in this paper for contour error control using the standard linear quadratic (LQ) method, upon which the offset loss function and the control loss function were considered to attain a relatively good compromise. The weight coefficient for the control loss and the optimal discrete sampling period are garnered, then a zero phase error tracking controller (ZPETC) is designed with the disturbance observer (DOB) to improve the anti-interference capability and the robustness of the system. The proposed methods are capable of enhancing the stability and rapidity of the discrete controller and reducing the contour error of the system. The results may be valuable for various trajectory tracking control applications.

This article is concerned with decentralised output regulation of hierarchical systems subject to input and output disturbances. It is assumed that the disturbance can be represented as the output of an autonomous linear time invariant (LTI) system with an unknown initial state. The primary objective is to design a decentralised controller with the property that not only does it reject the degrading effect of the disturbance on the output (to achieve a satisfactory steady-state performance), it also results in a small linear quadratic (LQ) cost function (implying a good transient behaviour). To this end, the underlying problem is treated in two phases. In the first step, a number of modified systems are defined in terms of the original system. The problem of designing an LQ centralised controller which stabilises all the modified systems and rejects the disturbance in the original system is considered, and it is shown that this centralised controller can be found efficiently by solving a linear matrix inequality (LMI) problem. In the second step, a method recently presented in the literature is exploited to decentralise the designed centralised controller. It is shown that the obtained controller satisfies the prescribed design specifications including disturbance rejection. Finally, in a more pragmatic context, the system is assumed to be subject to input delay, and a robustness analysis is carried out accordingly. Simulation results elucidate the efficacy of the proposed control law.

The objective of visual servoing aims to control an object's motion with visual feedbacks and becomes popular recently. Problems of complex modeling and instability always exist in visual servoing methods. Moreover, there are few research works on selection of the servoing gain in image-based visual servoing (IBVS) methods. This paper proposes an IBVS method with Q -Learning, where the learning rate is adjusted by a fuzzy system. Meanwhile, a synthetic preprocess is introduced to perform feature extraction. The extraction method is actually a combination of a color-based recognition algorithm and an improved contour-based recognition algorithm. For dealing with underactuated dynamics of the unmanned aerial vehicles (UAVs), a decoupled controller is designed, where the velocity and attitude are decoupled through attenuating the effects of underactuation in roll and pitch and two independent servoing gains, for linear and angular motion servoing, respectively, are designed in place of single servoing gain in traditional methods. For further improvement in convergence and stability, a reinforcement learning method, Q -Learning, is taken for adaptive servoing gain adjustment. The Q -Learning is composed of two independent learning agents for adjusting two serving gains, respectively. In order to improve the performance of the Q -Learning, a fuzzy-based method is proposed for tuning the learning rate. The results of simulations and experiments on control of UAVs demonstrate that the proposed method has better properties in stability and convergence than the competing methods.

Günümüzde teknolojinin gelişimiyle birlikte simülatörlerin kullanım alanları çeşitlenerek artmaktadır. Simülatörler her sektörde kendilerine yer bulabildikleri gibi havacılık ve uzay çalışmalarında da büyük rol oynamaktadırlar. Bu çalışmada, yeni bir küresel uçuş simülatörü tasarlanmış, küresel uçuş simülatörünün doğrusal olmayan dinamiği Newton-Euler yöntemi kullanılarak matematiksel olarak modellenmiştir. Elde edilen matematiksel model küresel uçuş simülatörü için kontrolcü tasarımında kullanılmıştır. Kontrolcü tasarımında kutup yerleştirme yöntemi ve doğrusal karesel düzenleyici yöntemi (LQR) seçilmiş olup bu yöntemler kıyaslanmıştır. Sistemin matematiksel benzetimi SIMULINK ortamında yapılmıştır.

Flight control laws for commercial aircraft have traditionally been designed using classical methods. This is adequate for rigid statically stable airplanes controlled by mechanical and hydraulic means. The challenge increases as pitch stability is reduced and structural flexibility is increased. The practical application of modern linear quadratic methods to stability augmentation system synthesis for a large subsonic transport is demonstrated. Stability, robustness, handling qualities, and dynamic load requirements under discrete and random gusts are addressed. The approach evolves from the traditional linear quadratic regulator methods to the synthesis of robust low-order controllers of a given structure with parameter optimization. The design is accomplished with aeroelastic models at flight conditions ranging from a low-speed takeoff to a high-speed cruise with forward and aft center-of-gravity configurations .

A robust position controller with linear-quadratic (LQ) method and fuzzy neural-network (FNN) technique for permanent magnet synchronous motor (PMSM) is presented in this paper. An FNN controller is implemented to preserve a favorable model-following characteristics under various operating conditions. The performance of the proposed drive is investigated both in experiment and simulation at different condition.

Optimal discretionary monetary policy in a micro-founded model with a zero lower bound on nominal interest rate

On some attitude control syntheses for satellites with flexible appendages

Trade-Off Between Energy Capture and Dynamic Loads in Variable Speed Wind Turbines

For a gantry crane, optimal control of the crane motion requires that the speed of the cart be maximized, and the swing of the hanging payload be minimized. The problem lends itself naturally to optimal linear quadratic (LQ) controllers. This paper examines the performance of four different approaches to the design of an LQ controller, including two optimization approaches as based on: 1) minimal energy of cart and payload and 2) integrated absolute error of payload angle. Both simulation and experimental results are presented. A demonstration is also given as to how the results taken from laboratory scale gantry crane experiments must be treated with caution. Laboratory based studies have generally worked with systems where the moving cart mass is much larger than the suspended payload mass. In the case of industrial scale gantry cranes, the reverse can be true. This has implications with respective to the robustness of the controller. In the case where the cart mass is much greater than the payload mass, the effect of the payload on the cart is correctly neglected. However, there are stability implications if this is not the case. The implications with respect to the tuning of LQ controllers for this application are discussed.

An adaptive failure compensation control scheme is developed based on linear quadratic (LQ) control for discrete-time possibly nonminimum phase systems with unknown actuator failures with characterizations that some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants. The failure compensation control problem is formulated as a new LQ control problem. Based on some results on new controller structures and design conditions developed in [9], a stable adaptive law is proposed for the estimation of uncertain parameters from both the plant and actuator-failures. The adaptive failure compensation controller is implemented with adaptive parameter estimates, for achieving closed-loop stability and output regulation. Simulation results are presented to verify the desired adaptive control performance in the presence of uncertain actuator failures.