Presence Of Large Disturbances Research Articles

Exploiting Different Symmetries for Trajectory Tracking Control with Application to Quadrotors*

High performance trajectory tracking control of quadrotor vehicles is an important challenge in aerial robotics. Symmetry is a fundamental property of physical systems and offers the potential to provide a tool to design high-performance control algorithms. We propose a design methodology that takes any given symmetry, linearises the associated error in a single set of coordinates, and uses LQR design to obtain a high performance control; an approach we term Equivariant Regulator design. We show that quadrotor vehicles admit several different symmetries: the direct product symmetry, the extended pose symmetry and the pose and velocity symmetry, and show that each symmetry can be used to define a global error. We compare the linearised systems via simulation and find that the extended pose and pose and velocity symmetries outperform the direct product symmetry in the presence of large disturbances. This suggests that choices of equivariant and group affine symmetries have improved linearisation error.

Attitude Regulation With Bounded Control in the Presence of Large Disturbances With Bounded Moving Average

The attitude regulation problem with bounded control for a class of satellites in the presence of large disturbances, with bounded moving average, is solved using a Lyapunov-like design. The analysis and design approaches are introduced in the case in which the underlying system is an integrator and are then applied to the satellite attitude regulation problem. The performance of the resulting closed-loop systems are studied in detail and it is shown that trajectories are ultimately bounded despite the effect of the persistent disturbance. Simulation results on a model of a small satellite subject to large, but bounded in moving average, disturbances are presented.

Adaptive Fault-tolerant and Anti-disturbance Attitude Control of the Reconfigurable Modular CubeSat

This paper addresses the dynamical coupling and attitude control problems caused by the large-scale abrupt changes of the system mass characteristics and the relative motion of the sub-body during the autonomous reconfiguration of the modular satellite in orbit, the attitude dynamics equations of the reconfigurable modular CubeSat system with time-varying mass characteristics parameters are derived. An improved adaptive fault-tolerant anti-disturbance control algorithm that does not rely on an accurate dynamics model and converges quickly in finite time is also proposed based on the composite control idea. In the attitude control of modular CubeSat with large external disturbances and large range of moment of inertia time variation, the method has a faster convergence speed and higher accuracy than the traditional sliding mode control. Without any fault detection and diagnostic information, the method is able to achieve fast and highly accurate attitude adaptive fault-tolerant control in the presence of large disturbances and actuator time-varying, stochastic-type faults through real-time observation of total disturbances inside and outside the system. The final simulation results verify the effectiveness and reliability of the method.

Heat Exchanger Performance and Stability Improvement Using H2 Synthesis Control Design Technique

The heat exchanger is a device that helps to circulate calculated amount of heat in a system. It can be applied in order to reduce the number of heat sources while maintaining a precise level of heat. Heat exchanger is expected to be part of the solution to CO2 emission and climate issues since its application reduces the sources of heat and cost of production such as in electrical power plant. Due to the critical need for the solution to the enormous emission of CO2 and the need to reduce cost of running power plants, the study and improvement of the heat exchanger has become very important. The heat exchanger suffers from disturbances due to its harsh environment. In order to maintain desired performance the heat exchanger requires an adequate control measure. Many types of controllers have been designed, however from the review it was observed that most of the controllers produced marginal stability which will not maintain good performance of the system in the presence of significant disturbance. The major objectives of this work are to reduce the tracking error for performance improvement, to reduce the peak sensitivity for better disturbance rejection and to improve the stability margins for stability robustness. In this work, an optimal robust control was developed for the heat exchanger using H2 synthesis technique. From the results, the controlled system trajectory tracking error and overshoot were reduced to zero and the peak sensitivity to disturbance was reduced to 0.189 dB. Gain and phase margins satisfied the robust stability characteristics; gain margin was greater than 20 dB and phase margin was greater 60 dB. This means that the designed optimal controller will guarantee robust performance and stability of the system even in the presence of large disturbance.

Fast hybrid dual mode NMPC for a parallel double inverted pendulum with experimental validation

This study presents a novel fast non-linear model predictive control approach for a parallel double inverted pendulum. The approach uses dual-mode closed-loop predictions to obtain numerically robust optimal solutions. Moreover, it uses the real-time iteration (RTI) scheme to reduce the computational burden and achieve real-time performance. Furthermore, two main modifications are proposed which significantly improve the performance of the RTI scheme in the presence of large disturbances, namely; additional energy-based costs, and a hybrid switching scheme. Finally, the approach uses a non-standard discretised model combined with an online system identification scheme to address parameter uncertainty, and with an extended Kalman filter for state-estimation. The resulting performance is validated through both simulations and experimental results.

Nutation control strategy through state feedback: application to a spin-stabilized autonomous system

This research relies on nutation control strategy through state feedback with its application to a spin-stabilized autonomous system such as spacecraft. The key subject behind the investigation is to present a new formulation in the area of attitude control to be able to handle the rotational angles in connection with angular rates, while the system under control is in a predefined mission operation plan. In a word, the main contribution of the research is to cope with the nutation in the process of spin-stabilized attitude control approach in association with the y, z-axis rotational angles to be desirable, at each instant of time. With this goal, the x-axis spin up mode of the system under control is first handled, while the y, z-axis rotational angles of such a system can instantly be adjustable to guarantee the performance of the aforementioned mission operation plan with high accuracy. And then the nutation and angular rates of the system are appropriately controlled in the presence of large disturbances, as long as the propellant engine on mode is addressed to complete the maneuvers in the predefined space mission. The novelty of this research with respect to state-of-the-art materials in this filed is briefly taken in the unique system’s integration and its investigated outcomes. The aforementioned ones are finally considered with regard to some measurable techniques in a number of different cases to indicate that the proposed control approach is well resulted, tangibly.

Recurrent fuzzy wavelet neural network variable impedance control of robotic manipulators with fuzzy gain dynamic surface in an unknown varied environment

In this paper, an intelligent variable impedance control combined with a fuzzy gain dynamic surface is proposed to improve the interaction of the robot manipulator with an unknown varied environment. The parameters of the proposed variable impedance are adapted by optimization an introduced cost function using a recurrent fuzzy wavelet network. The stability conditions for the varying inertial, stiffness and damping are presented to guarantee the stability of the variable impedance. Additionally, a fuzzy dynamic surface method is developed to tune the gains of the dynamic surface as a robust controller. The proposed fuzzy gain dynamic surface is used to force the end-effector of the manipulator to track the desired impedance profile in the presence of large disturbances. Using Lyapunov's method, the stability of the mentioned closed-loop system is proved. Finally, by using a designed simulator for IRB120 (ABB) robot, several simulations are carried out to verify the performance of the proposed method for the execution of various tasks in an unknown varied environment in the presence of large disturbances.

SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO

There is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. Traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. This paper reports the design of a novel nonlinear model predictive controller (NMPC) based on convolutional neural network (CNN) and ant colony optimizer (ACO) which is superior to a linear proportional integral-derivative counterpart. This combination helps the control system to deal with model uncertainties with robustness. The results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of large disturbances.

Frequency Regulation of Micro-grid Connected Hybrid Power System with SMES

This paper presents the frequency regulation analysis of a micro-grid connected hybrid power system based on solar Photovoltaic (PV), Wind and Diesel-Engine Generator (DEG) with Superconducting Magnetic Energy Storage system (SMES) unit. Abrupt change in load demand and power fluctuations from PV and wind power source causes frequency variability in the system. In order to mitigate this issue, the hybrid power system incorporates along with the energy storage devices. A case study of the impact of SMES operations on the performance of frequency stability of hybrid system has been carried out. Simulation of a Hybrid Power System (HPS) model has been carried out in two different scenarios with the two phases. First is the steady state investigation of HPS using PI controller with step load change response. Second is the dynamic performance study throughout a typical day with arbitrary variation in load demands. Controller parameters are tuned with Genetic Algorithm based optimization approach. The limits on the inductor (coil) current due to the possibilities of discontinuous conduction in the presence of large disturbances and limited storage capacity have also been incorporated. HPS has analyzed in the Matlab Simulink environment. Simulation results indicate that SMES system can contribute towards the frequency stability in both cases for variable power generations from renewable sources and irregular demand loads.

Stability analysis of autonomous space systems in the presence of large disturbances: A Lyapunov-based constrained control strategy

The research addresses a Lyapunov-based constrained control strategy to deal with the autonomous space system in the presence of large disturbances. The aforementioned autonomous space system under control is first represented through a dynamics model and subsequently the proposed control strategy is fully investigated with a focus on the three-axis detumbling and the corresponding pointing mode control approaches. The three-axis detumbling mode control approach is designed to deal with the unwanted angular rates of the system to be zero, while the saturations of the actuators are taken into consideration. Moreover, the three-axis pointing mode control approach is designed in the similar state to deal with the rotational angles of the system to be desirable. The contribution of the research is mathematically made to propose a control law in connection with a new candidate of Lyapunov function to deal with the rotational angles and the related angular rates of the present autonomous space system with respect to state-of-the-art. A series of experiments are carried out to consider the efficiency of the proposed control strategy, as long as a number of benchmarks are realized in the same condition to verify and guarantee the strategy performance in both modes of control approaches.

Detection and Classification of Islanding and Nonislanding Events in Distributed Generation Based on Fuzzy Decision Tree

This paper presents a new approach for islanding detection of distributed generation systems (DGs), using the features obtained from a new time-frequency transform with the negative sequence voltage, negative sequence current and the 3-phase voltage and current signals as inputs. The well-known S-transform suffers from high computational complexity, for on-line applications and hence, a new time-frequency transform (frequency filtering S-transform or simply the FFST) similar to it but faster by almost 30 times is proposed here using the frequency sampling and band pass filtering only for neighborhood of significant frequency components, determined from FFT of the power disturbance signals at the DG terminals. Thus using only the important frequency components present in the signal, it results in a significant reduction of computational complexity of the discrete S-transform. A data mining approach using a certainty factor-based fuzzy decision tree is used to yield fuzzy rules with the extracted features from the FFST output to recognize disturbance events like islanding or non-islanding for a variety of operating conditions of both the DGs and the electric power system. The results achieved during testing using existing hybrid distribution systems show that the proposed method is very reliable, and fast even in the presence of large disturbances like faults and capacitor bank switching.

Net Power Control Based on Linear Matrix Inequality for Proton Exchange Membrane Fuel Cell System

An analysis for net power characterization of a proton exchange membrane fuel cell (PEMFC) system is developed with Ballard 1.2-kW Nexa power module. A net power control (NPC) strategy based on the linear matrix inequality (LMI) approach is proposed to achieve optimal oxygen excess ratio (OER). The H∞ suboptimal control problem is solved by LMI and the simulations are carried out under different conditional tests. The results verify that the proposed strategy can track the optimal OER trajectory and reduce the parasitic power as the running process of electrical vehicle is simulated. The comparisons testify that the NPC has better robust performance and less settling time in the presence of large disturbances and environmental noises. Therefore, the proposed strategy will provide a novel approach to increase the net power for the PEMFC system.

Control of proton exchange membrane fuel cell system breathing based on maximum net power control strategy

In order to achieve the maximum net power, the analysis for the maximum net power characterization of a proton exchange membrane fuel cell (PEMFC) system is carried out. A maximum net power control (MNPC) strategy based on an implicit generalized predictive control (IGPC) and a reference governor is proposed to keep optimal oxygen excess ratio (OER) trajectory. The IGPC based on an effective informed adaptive particle swarm optimization (EIA-PSO) algorithm is developed to solve the predictive control law and reduce the computational complexity in the rolling optimization process. The simulations of three conditional tests are implemented and the results demonstrate that the proposed strategy can track the optimal OER trajectory, reduce the parasitic power and maximize the output net power. The comprehensive comparisons based on three conditional tests verify that the MNPC–IGPC has better robust performance in the presence of large disturbances, time delay and various noises. The experimental comparison with internal control system of Ballard 1.2kW Nexa Power Module testifies the validity of the MNPC–IGPC for increasing the net power. Hence, this proposed strategy can provide better behavior to guarantee optimal OER trajectory and the maximum net power even though the disturbances and uncertainties occur.

Robust Hybrid Control for Ballistic Missile Longitudinal Autopilot

This paper investigates the boost phase's longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor (RLC) network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative (PID) with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic (LQ) in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.

Control of a reactive distillation process: MTBE synthesis

This paper explores the closed-loop control performance of a heterogeneously catalysed reactive distillation process for the synthesis of methyl tert-butyl ether (MTBE). Two fresh reactant feed streams, namely methanol (MeOH) and mixed butenes, are introduced in the column. Distillate containing mostly n-butene (NB) and bottoms enriched with MTBE are collected from the process. Obviously, the stoichiometric amounts of fresh feeds are desired. The proposed control algorithm uses a composition analyser in the reactive section of the column to detect the inventory of MeOH so that fresh MeOH feed rate can be adjusted to satisfy the reaction stoichiometry. In addition, the closed-loop configuration includes two product compositions, two liquid levels, column pressure and fresh mixed butenes rate control loops. The developed control strategy provides tight control of MTBE product composition in presence of large disturbance and measurement lag.

Flexible Needle-tissue Interaction Modelling Using Depth-varying Mean Parameter

Needle insertion in soft tissue has raised a lot of research interests lately. A spring-beam-damper model has been developed for needle-tissue interaction. Considering the tissue inhomogeneity, depth-varying mean parameters are proposed to calculate the spring/damper reaction forces. Based on this approach, an online parameter estimator has been designed using modified least square method with forgetting factor. In this paper, mathematical relationships are developed for efficient and robust needle tip trajectory prediction in an inhomogeneous and noisy environment. Extensive experiments have been carried out in various phantoms to validate the improved needle-tissue interaction model based on the online experimental data. Results reveal that the model can track the needle tip trajectory with satisfactory accuracy even in the presence of large disturbances and noises, but sometimes at the cost of the parameter estimation. Experiments also show that the convergent rate and estimation accuracy can be greatl...

Robust Performance Verification of Adaptive Robust Controller for Hard Disk Drives

An adaptive robust controller (ARC) has been recently developed for read/write head embedded control systems of hard disk drives (HDDs). This structure is applicable to both track seeking and track following modes, and it makes the mode switching control algorithms found in conventional HDD servosystems unnecessary. An Improved Desired Compensation ARC (IDCARC) scheme is proposed in this paper, in which the traditional ARC is powered by a dynamic adaptive term. In this approach the adaptation regressor is calculated using reference trajectory information. Moreover, a robust analysis of this method is developed, in which a controller designed based on a simple model of the system is verified in a closed loop performance of a more comprehensive model of the system. The simulation result verifies the significant improvement of the performance of IDCARC compared to that of ARC and its robustness for this model. It is observed that in the presence of large disturbances the proposed method preserves the stability and a suitable performance while the ARC fails even in stability.

Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning

A ball mill grinding circuit is a nonlinear system characterised by significant controller interaction between the manipulated variables. A rigorous ball mill grinding circuit is simulated and used in its entirety for the development of a neurocontroller through the use of evolutionary reinforcement learning. Reinforcement learning entails learning to achieve a desired control objective from direct cause—effect interactions with a simulated process plant. The SANE (symbiotic adaptive neuro-evolution) algorithm is able to learn implicitly to eliminate controller interactions in the grinding circuit by taking a plant wide approach to controller design. The ability of the neurocontroller to maintain high performance in the presence of large disturbances in feed particle size distribution and ore hardness variations is demonstrated. The generalisation afforded by the SANE algorithm in dealing with considerable uncertainty in its operating environment attests to a large degree of controller autonomy.

Application of an Adaptive Algorithm to the Control of the Plasma Vertical Position

The plasma vertical position in a tokamak can be open-loop unstable with time-varying dynamics. The limitation in the output power of the control amplifier makes the time-varying unstable system particularly difficult to control. Fixed-coefficient linear controllers usually fail to maintain control in the presence of large disturbances, like edge-localized modes (ELMs), which saturate the amplifier output. During the saturation period, the vertical position of the plasma will grow exponentially with the unstable eigenvalue and may reach values that cannot be controlled by the energy provided by the control amplifier, which is limited by practical constraints. The primary sources of disturbances and measurement noise that effect the vertical position are the ELMs and the 600-Hz noise from the thyristor power supplies. The former are present in the form of pulses and appear during high-energy confinement plasma configurations. A novel nonlinear controller for the vertical position based on a discrete adaptive near-time optimum control algorithm (DANTOC) is used to stabilize the system, to maximize the stability region, and to provide robustness with respect to the aforementioned sources of disturbances and measurement noise. The controller is tested in simulation for the Joint European Torus tokamak, and the results demonstrate its feasibility in controlling the vertical position for different plasma configurations. The controller is also tested on the COMPASS-D tokamak, and the results demonstrate the improvement with respect to a simple linear proportional and derivative controller in the presence of disturbances and measurement noise.