Modelling and control of mechatronic and robotic systems

3noopenopenGasparetto A.; Seriani S.; Scalera L.Gasparetto, A.; Seriani, S.; Scalera, L

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

An Improved DC Motor Position Control Using Differential Evolution Based Structure Specified H∞ Robust Controller

Traditional synthesis of an H∞ controller usually results in a very high order of controller that is not practical for a low-cost embedded system such as a microcontroller. This paper presents a synthesis method of a low-order H∞ robust controller to control the position of a dc motor. The synthesis employed Differential Evolution optimization to find a controller that guarantees robust stability performance and robust stability against system perturbation. A second-order PID structure was chosen for the synthesized controller because this structure is simple and very famous. The proposed controller performance under uncertainties was compared to some other controllers. The first was compared with a conventional PID controller that had been finely tuned using the trial and error method in the nominal transfer function of the plant. Secondly, the proposed controller was compared with a full-order H∞ robust controller generated from a traditional synthesis method. Thirdly, the proposed controller was compared with another structure specified H∞ robust controller generated differently from the proposed method. All of the controllers result in a stable response. However, the proposed controller gives a better response in terms of overshoot and response time

機械システムの振れ抑制のための正確なモデル予測制御

The thesis proposes a control scheme based on an explicit model predictive control (EMPC) for suppressing fluctuation in time responses of mechanical systems. The control objective is not only to achieve robust tracking performance and rejecting disturbances, but also to suppress the fluctuation of the mechanism. Two kinds of fluctuation systems are considered in this thesis, i.e., torsional fluctuation system which a measured output contains an outlier and active magnetic micromanipulator which the operating point changes, or holding angle changes. The torsional fluctuation system can be represented by two-mass system which typically consists of a driving motor and load, both of which connect through a flexible shaft. Consequently, there is the difference between the motor and load speed, which results in the torsional fluctuation inevitably. In addition, the measured output, the motor speed, contains an outlier. The requirement of the high speed servo operation, tracking the desired motor speed, has to carefully design. The robust EMPC is proposed in order to achieve not only good tracking performance and load-change effect rejection, but also low torsional fluctuation whereas the measurement noise contains outliers. The control structure is based fundamentally on the combination of EMPC and an estimator where the well-known Kalman filter is replaced by the estimator to deal with the outlier phenomena. The effectiveness of the proposed method is compared with a PID control scheme by means of the simulation validations. The active magnetic micromanipulator having two dimensional degree of freedom that is able to move along x and y-axis in micro scale. The proposed micromanipulator\u27s structure consists of two decoupling links, namely the top and bottom link are able to move along the x and y-axis, independently. Each link has identically parallel leaf spring mechanisms. For a steering force, the combination of permanent magnets and electric coils is utilized as double driving. The hybrid control scheme is proposed which is a combination between EMPC and PID controllers. The PID controller is suitable for handling the holding angle changes, or the initial displacements. The EMPC controller provides an excellent tracking performance. The control objectives are to achieve the robust tracking performance and to suppress the fluctuation of the flexible structure. Root mean square error is less than 4μm whereas the active magnetic micromanipulator held by the user\u27s hand. The experimental results obtained indicate the effectiveness of the hybrid control. The results in this thesis reveal the effectiveness of both control schemes, robust EMPC and hybrid control, for suppressing the fluctuation in mechanical systems. For the torsional fluctuation system which the measured output contains an outlier, the results also show combining different norms. For the active magnetic micromanipulator, the results also show combining two controllers to handle the nonlinear system collectively. The contributions of the hybrid control enable a user to accomplish tracking reference tasks beyond human dexterity with the active magnetic micromanipulator.電気通信大学201

Development of New Adaptive Control Strategies for a Two-Link Flexible Manipulator

Manipulators with thin and light weight arms or links are called as Flexible-Link Manipulators (FLMs). FLMs offer several advantages over rigid-link manipulators such as achieving highspeed operation, lower energy consumption, and increase in payload carrying capacity and find applications where manipulators are to be operated in large workspace like assembly of freeflying space structures, hazardous material management from safer distance, detection of flaws in large structure like airplane and submarines. However, designing a feedback control system for a flexible-link manipulator is challenging due the system being non-minimum phase, underactuated and non-collocated. Further difficulties are encountered when such manipulators handle unknown payloads. Overall deflection of the flexible manipulator are governed by the different vibrating modes (excited at different frequencies) present along the length of the link. Due to change in payload, the flexible modes (at higher frequencies) are excited giving rise to uncertainties in the dynamics of the FLM. To achieve effective tip trajectory tracking whilst quickly suppressing tip deflections when the FLM carries varying payloads adaptive control is necessary instead of fixed gain controller to cope up with the changing dynamics of the manipulator. Considerable research has been directed in the past to design adaptive controllers based on either linear identified model of a FLM or error signal driven intelligent supervised learning e.g. neural network, fuzzy logic and hybrid neuro-fuzzy. However, the dynamics of the FLM being nonlinear there is a scope of exploiting nonlinear modeling approach to design adaptive controllers. The objective of the thesis is to design advanced adaptive control strategies for a two-link flexible manipulator (TLFM) to control the tip trajectory tracking and its deflections while handling unknown payloads. To achieve tip trajectory control and simultaneously suppressing the tip deflection quickly when subjected to unknown payloads, first a direct adaptive control (DAC) is proposed. The proposed DAC uses a Lyapunov based nonlinear adaptive control scheme ensuring overall system stability for the control of TLFM. For the developed control laws, the stability proof of the closed-loop system is also presented. The design of this DAC involves choosing a control law with tunable TLFM parameters, and then an adaptation law is developed using the closed loop error dynamics. The performance of the developed controller is then compared with that of a fuzzy learning based adaptive controller (FLAC). The FLAC consists of three major components namely a fuzzy logic controller, a reference model and a learning mechanism. It utilizes a learning mechanism, which automatically adjusts the rule base of the fuzzy controller so that the closed loop performs according to the user defined reference model containing information of the desired behavior of the controlled system. Although the proposed DAC shows better performance compared to FLAC but it suffers from the complexity of formulating a multivariable regressor vector for the TLFM. Also, the adaptive mechanism for parameter updates of both the DAC and FLAC depend upon feedback error based supervised learning. Hence, a reinforcement learning (RL) technique is employed to derive an adaptive controller for the TLFM. The new reinforcement learning based adaptive control (RLAC) has an advantage that it attains optimal control adaptively in on-line. Also, the performance of the RLAC is compared with that of the DAC and FLAC. In the past, most of the indirect adaptive controls for a FLM are based on linear identified model. However, the considered TLFM dynamics is highly nonlinear. Hence, a nonlinear autoregressive moving average with exogenous input (NARMAX) model based new Self-Tuning Control (NMSTC) is proposed. The proposed adaptive controller uses a multivariable Proportional Integral Derivative (PID) self-tuning control strategy. The parameters of the PID are adapted online using a nonlinear autoregressive moving average with exogenous-input (NARMAX) model of the TLFM. Performance of the proposed NMSTC is compared with that of RLAC. The proposed NMSTC law suffers from over-parameterization of the controller. To overcome this a new nonlinear adaptive model predictive control using the NARMAX model of the TLFM (NMPC) developed next. For the proposed NMPC, the current control action is obtained by solving a finite horizon open loop optimal control problem on-line, at each sampling instant, using the future predicted model of the TLFM. NMPC is based on minimization of a set of predicted system errors based on available input-output data, with some constraints placed on the projected control signals resulting in an optimal control sequence. The performance of the proposed NMPC is also compared with that of the NMSTC. Performances of all the developed algorithms are assessed by numerical simulation in MATLAB/SIMULINK environment and also validated through experimental studies using a physical TLFM set-up available in Advanced Control and Robotics Research Laboratory, National Institute of Technology Rourkela. It is observed from the comparative assessment of the performances of the developed adaptive controllers that proposed NMPC exhibits superior 7performance in terms of accurate tip position tracking (steady state error ≈ 0.01°) while suppressing the tip deflections (maximum amplitude of the tip deflection ≈ 0.1 mm) when the manipulator handles variation in payload (increased payload of 0.3 kg). The adaptive control strategies proposed in this thesis can be applied to control of complex flexible space shuttle systems, long reach manipulators for hazardous waste management from safer distance and for damping of oscillations for similar vibration systems

Model Predictive Control of a Flexible Links Mechanism

none3noneBOSCARIOL P; GASPARETTO A; ZANOTTO VBoscariol, Paolo; Gasparetto, A; Zanotto, V