A Frequency Domain Quantitative Technique for Robust Control System Design

Robust Model-Following control of a three-level Neutral Point Clamped shunt active filter in the medium voltage range

The aim of this paper is to develop a novel robust variable sampling period controller (RVSPC) for networked control systems (NCSs) in the presence of random time delays and packet losses. Different from the existing control techniques for NCSs, the RVSPC controller is constructed as a hybrid robust controller with the adaptive variable sampling period. To adapt to the delay variation, a so-called variable sampling period adjuster based on a time delay predictor (TDP) and a time delay and packet detector is designed to adjust effectively the sampling period. To efficiently compensate for the random delays and packet losses, the hybrid controller is designed as a combination of a quantitative feedback theory (QFT)-based robust controller and a robust state feedback controller (RSFC) with adaptive control gain. A smart switch based on the TDP is then used to select properly the main control unit as the QFT or RSFC corresponding to the current delay and packet loss status. Illustrative examples with NCSs including both computation and communication delays and packet losses are finally carried out to illustrate the effectiveness of the proposed method.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

Robust Control of Muti Input Multi Output(MIMO) systems are important class of problems and their design is challenging one. This paper proposes an efficient algorithm for automatic synthesis of Quantitative Feedback Theory(QFT) based robust controllers. The proposed method uses Interval Analysis, hybrid optimization and consistency techniques. Hull consistency is used to prune the initial input domain by removing the inconsistent values which are not part of the solution. Hybrid optimization part combines interval global optimization and nonlinear local optimization methods. The proposed overall algorithm is demonstrated to design a robust MIMO QFT controller for uncertain Magnetic levitation System (ECP 750).

This paper presents the design of a robust proportional integral and derivative (PID) controller for a first-order lag with pure delay (FOLPD) model using particle swarm optimization (PSO)-enabled automated quantitative feedback theory (QFT). The plant model considered here can be approximated as a first-order system with a non-minimum phase (NMP) zero. Synthesis of controller for the FOLPD model via manual graphical technique involved in the QFT method is always a challenging and cumbersome task, because an NMP system stabilizes by a small gain. In this paper, a proposal is being presented to automate the loop-shaping phase in the QFT design method to synthesize a robust controller that can undertake the exact amount of plant uncertainty even in the presence of larger uncertainties than those assumed initially and can ensure a proper trade-off between robust stability and tracking performance specifications over the entire range of design frequencies. In this paper,s the PSO technique has been employed to tune the controller automatically,which can significantly reduce the computational effort compared with manual graphical techniques. It has also been demonstrated that this methodology not only automates loop shaping but also improves design quality and, most usefully, improves performance with optimally tuned PID controller in quantitative manner.

This paper deals with uncertain model structures, model validation and robust performance analysis of active magnetic bearings. The dynamics of active magnetic bearing systems are characterized by their instability and complex dynamics of rotor and electromagnets. The feedback control is indispensable to stabilize the system, further the closed-loop systems of magnetic bearings should have robustness for stability and performance against model uncertainties. First we derive a nominal mathematical model of AMBs as a linear state-space model under some assumption and idealization, then we consider the discrepancy between the real physical systems and the obtained nominal design model. This discrepancy can be expressed as the structured uncertainties by Linear Fractional Transformation. These uncertainties include linearization error, parametric uncertainties, unmodeled dynamics, and gyroscopic effect. Then we set the interconnection structure which contains the above structurally represented uncertainties. Next we design a robust controller which achieves robust performance condition. Finally, we validate the interconnection structure with the nominal model and uncertainties, and analyze the robustness of stability and performance of the closed-loop system via the mixed structured singular value.

This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

In this work, the design of a quantitative PID controller is proposed for Ball and Beam system. This controller is designed to robustly compensate for the nonlinear and uncertain behavior of the ball and beam system. The PID controller parameters are obtained using the Black Hole Optimization (BHO) method subject to Quantitative Feedback Theory (QFT) consteaints. The QFT is used to design a simple and robust controller with a more desirable performance. The simulation results show that the proposed quantitative PID controller can effectively compensate the ball and beam system with robust behavior and desirable time response specifications. Index Terms— Robust control, Quantitative Feedback Theory (QFT), PID, Ball and Beam System, Black Hole Optimization.

Some robust control methods have been developed in the past in order to increase tracking performance in the presence of parametric uncertainties. In the presence of parametric uncertainty, unmodelled dynamics and other sources of uncertainties, robust control laws are used. Corless-Leitmann [1] approach is a popular approach used for designing robust controllers for robot manipulators. In early application of Corless-Leitmann [1] approach to robot manipulators [2, 3], it is difficult to compute uncertainty bound precisely. Because, uncertainty bound on parameters depends on the inertia parameters, the reference trajectory and manipulator state vector. Spong [4] proposed a new robust controller for robot manipulators using the Lyapunov theory that guaranties stability of uncertain systems. In this approach, Leithmann [5] or Corless-Leithman [1] approach is used for designing the robust controller. One of the advantage of Spong’s approach [4] is that uncertainty on parameter is needed to derive robust controller and uncertainty bound parameters depends only on the inertia parameters of the robots. Yaz [6] proposed a robust control law based on Spong’s study [4] and global exponential stability of uncertain system is guaranteed. However, disturbance and unmodelled dynamics are not considered in algorithm of [4, 6]. Danesh at al [7] develop Spong’s approach [4] in such a manner that control scheme is made robust not only to uncertain inertia parameters but also to robust unmodelled dynamics and disturbances. Koo and Kim [8] introduce adaptive scheme of uncertainty bound on parameters for robust control of robot manipulators. In [8], upper uncertainty bound is not known as would be in robust controller [4] and uncertainty bound is estimated with estimation law in order to control the uncertain system. A new robust control approach is proposed by Liu and Goldenerg [9] for robot manipulators based on a decomposition of model uncertainty. Parameterized uncertainty is distinguished from unparameterized uncertainty and a compensator is designed for parameterized and unparameterized uncertainty. A decomposition-based control design framework for mechanical systems with model uncertainties is proposed by Liu [10]. In order to increases tracking performance of uncertain systems, design of uncertainty bound estimation functions are considered. For this purpose, some uncertainty bound estimation functions are developed [11-15] based on a Lyapunov function, thus, stability of

Adaptive Backstepping Integral Sliding Mode Control Combined with Super-Twisting Algorithm For Nonlinear UAV Quadrotor System

Quantitative feedback theory (QFT) is an engineering design method with strong practical value in the field of robust control and it is aimed at the objects with parameters changes. This paper introduces the basic principles and the design process of QFT, and for parameter uncertainty of the robot joint control, this paper designs the feedback-controller and the pre-filter of the system based on QFT. MATLAB simulation results show that the method is with good robust performance and dynamic performance in the robot joint control, and meets the performance indicators.

In this paper, a computationally-efficient and robust direct-model predictive control (DMPC) technique for doubly-fed induction generators in variable-speed wind turbines systems is proposed. In order to avoid several predictions of the rotor currents, the reference voltage (RV) is directly computed from the reference currents, which notably reduces the calculation load. Furthermore, the disturbances due to parameters mismatches and un-modeled dynamics are considered in the RV calculation. Accordingly, the sensitivity to mismatches in the model parameters is avoided and a zero steady-state error is realized. Finally, based on the location of this RV, the quality function is evaluated for only two times to find the best switching state, which is applied to the power converter in the next sample. The performance of the suggested DMPC is experimentally validated and compared with the convention DMPC.

In this article, a novel robust observer-based nonlinear control approach is proposed for quadrotors with unmodeled dynamics and external disturbances, wherein the tracking errors are restricted effectively. Two nonlinear disturbance observers are proposed to deal with uncertainties in the inner and outer loops, respectively. On this basis, a robust observer-based nonlinear control approach is put forward for quadrotor systems. Specifically, in the outer loop subsystem, a robust observer-based control scheme is proposed, which can reduce unexpected tracking errors for quadrotors effectively. Subsequently, based upon the geometric methods, the coordinate-free attitude controller and the nonlinear disturbance observer are integrated as a robust controller in the inner loop subsystem. Based upon full nonlinear dynamics, the solutions of the closed-loop system are proven to be uniformly ultimately bounded by means of rigorous Lyapunov analysis. A series of comparative experiments consisting of the implementation of a nonlinear proportional-integral-derivative (PID)-type controller, an observer-based sliding mode controller, and the proposed controller are conducted to demonstrate the remarkable performance of the proposed method in terms of higher tracking accuracy and stronger robustness.

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

Approximations and uncertainties in system modeling lead to inaccurate state estimation and thereby the failure of systematically designed control techniques. A robust control technique is therefore needed for state estimation and control when the system is complex and is accompanied by uncertainties. One of the robust control techniques is sliding mode control (SMC). Sliding mode control is a non-linear control technique with remarkable properties like robustness and insensitive to disturbances. Hence, it is highly recommended for the control of extremely complex plants, like nuclear reactors. However, chattering is one of the main disadvantages that arise while using a sliding mode control. In this paper, a sliding mode technique is used for estimating neutron precursor density, xenon, and iodine concentrations which otherwise are very difficult to measure, and then a sliding mode controller with a reduced chattering effect is proposed for pressurized water reactor (PWR). Stability analysis of the system is carried out using the Lyapunov approach. Simulation results are presented to show the efficiency of the proposed system.