Superior Control Performance Research Articles

Nonlinear Model Predictive Control of Rotation Floating Space Robots for Autonomous Active Debris Removal

Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based systems present difficulties in their feasible implementation. Lack of a fixed base arises serious problems in controlling the space manipulators for precision tasks like capture of an orbiting space debris. This paper presents systematic modelling and control approaches for Rotation floating space robots in order to draw a comparison between them while tracking a moving target representing autonomous debris capture. We propose a Nonlinear Model Predictive Controller (NMPC) for the space robot in order to design an optimal path that the end-effector can follow while being controlled to capture the target. To the best of the knowledge of the authors, such a controller has not been tested for a Rotation floating space robot before. Further, the current work implements and reviews one of the most commonly used Transpose Jacobian Cartesian (TJC) controller for Rotation floating space robots through the use of Generalized Jacobian Matrix (GJM). The results provide sufficient evidence of the superior performance of the nonlinear model predictive controller over the TJC controller. Finally, the current work also implements the same nonlinear model predictive controller on a more popular state of the art Free floating space robot and compares it with the Rotation floating space robot.

Star Polymer for Filtration Control in Water-based Fluids

In high-performance water-based drilling fluids, filtration control is a closely monitored mud characteristic. Several problems have been found that are linked to excessive invasion of aqueous filtrate into a permeable formation. A high filtrate loss often causes thick filter cake formation that lead to tight spots in the hole. Elevated temperatures and fluid loss in deep holes can cause situations such as differential sticking of pipes. At the same time, loss of filtration control catastrophically affects the rheology of the water-based fluid via excessive thickening. Seepage of the filtrate into sensitive shale formations may lead to swelling, sloughing or even cave-in. Filtrate invasion into a production zone can lead to a condition known as water blocking, where the reservoir pressure is not strong enough to drive the aqueous filtrate out of the pores, leading to drastic reduction of oil and gas flow. In this contribution, we explore the synthesis of a novel star block copolymer fluid loss control additive using reversible addition-fragmentation chain transfer (RAFT) polymerization using arm-first approach. The star polymer developed in this work is able to provide superior filtration control performance (3 to 6 times better performance) over commercial additives. In addition, the thermal and mechanical stability of this novel material afforded direct incorporation into drilling mud formulations commonly used in the middle east drilling operations, which led to excellent fluid loss control both at room temperature and high temperature, high pressure conditions with thin filter cakes.

Towards Data-driven LQR with Koopmanizing Flows⋆

We propose a novel framework for learning linear time-invariant (LTI) models for a class of continuous-time non-autonomous nonlinear dynamics based on a representation of Koopman operators. In general, the operator is infinite-dimensional but, crucially, linear. To utilize it for effcient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates. For the latter, we rely on Koopmanizing Flows - a diffeomorphism-based representation of Koopman operators and extend it to systems with linear control entry. With such a learned model, we can replace the nonlinear optimal control problem with quadratic cost to that of a linear quadratic regulator (LQR), facilitating efficacious optimal control for nonlinear systems. The superior control performance of the proposed method is demonstrated on simulation examples.

Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer

The novel extreme learning machine (ELM) neural network disturbance observer (NNDO) - based sliding mode control (SMC) strategy is proposed for the precise tracking control of a hypersonic vehicle (HV) under various disturbance situations. By converting nonlinear dynamics into state-dependent linear model, the control law design process is simplified, and the sliding mode control law based on the power function reaching rate is designed to suppress the chattering effect. Considering the disturbances, the ELM-NNDO is designed based on the single-hidden layer feedforward network (SLFN). Different from conventional ELM using least square optimization approach, the output weight here is updated based on the Lyapunov synthesis approach. In addition, the influences of the disturbances on the velocity and altitude are attenuated by the direct feedback compensation (DFC), and the offset-free tracking control is realized for the output reference signal. Comparison of simulation results verify the superior control performance of the proposed method.

Hybrid Model Predictive Control for Hybrid Electric Vehicle Energy Management Using an Efficient Mixed-Integer Formulation

This paper proposes to use hybrid model predictive control (HMPC) for energy management in hybrid electric vehicle (HEV) using an efficient formulation. HEV has two sources of energy - electric motor and internal combustion engine (ICE) - allowing it an additional degree of freedom to optimize the ratio between the use of two energy sources. HEV energy management is crucial to exploit its potential to reduce fuel consumption and emissions. However, it is challenging to achieve the optimal solution along with the fast dynamics of HEVs. In this paper, instead of using a nonlinear, computationally expensive dynamic model of HEV, a piecewise afne (PWA) model is used to depict its behavior, implemented with multiple linear regression. The validation of the developed model is taken with standard drive cycles. Based on the PWA model, an optimal control problem of HMPC was formulated as mixed integer linear programming (MILP). Conventionally, mixed logical dynamical (MLD) system has been used in the process control field, including early studies of HEV control. This paper applies Big-M formulation which is efficient in its problem size and tight inequality relation for integer variables. The performance of the HMPC controller was examined in a simulated environment based on MATLAB/Simulink HEVP2 application. As a result, HMPC shows superior control performance than the equivalent consumption minimization strategy (ECMS).

NSGA-II algorithm-based LQG controller design for nuclear reactor power control

The design of reactor power control system is closely related to the safety and economy of nuclear power plants. To improve its power control performance, this study takes a pressurized water reactor as the research object. The nonlinear dynamic mathematical model and its state space model were first derived; Then the Linear Quadratic Gaussian (LQG) optimal control method was adopted to design the reactor power controller; Furthermore, the multi-objective optimization of the linear quadratic regulator (LQR) weighting coefficient is carried out based on the elitist nondominated sorting genetic algorithm version II (NSGA-II). The results show that the LQG controller optimized based on NSGA-II method not only has fast response, high control accuracy and good stability, but also has good robustness and can obtain superior reactor power control performance.

Optimization of VEDs for Vibration Control of Transmission Line Tower

This paper investigates the optimization of viscoelastic dampers (VEDs) for vibration control of a transmission line tower. Considering the stiffness of the steel brace connected to a VED, the mechanical model of the VED-brace system was established. Subsequently, the additional modal damping ratio of the transmission line tower attached with VEDs was obtained analytically. Furthermore, the finite element model of a two-circuit transmission line tower with VEDs was built in ANSYS software, and the influences of installation positions and parameters of VEDs on the additional modal damping ratio were clarified. In addition, the control performance of VEDs on the transmission line tower subjected to wind excitations was emphatically illustrated. The results show that the stiffness of the steel brace connected to a VED has a significant effect on the maximum additional modal damping ratio of the VED-brace system provided for the transmission line tower and the optimal parameters of the VED. Meanwhile, the installation positions of VEDs dramatically influence the additional modal damping ratio. Moreover, the increase of the brace stiffness and the loss factor is beneficial to improve the control performance of VEDs. Besides that, the VEDs present superior control performance on the top displacement of the transmission line tower as well as the transverse bending vibration energy.

Effects of force and moment actuation in active two dimensional VIV control of an elastic circular cylinder in power-law fluid flow

The coupled in-line/crosswise (2-DOF) Vortex-Induced Vibration (VIV) control of a flexibly-supported impenetrable circular cylinder immersed in uniform 2D laminar cross-flow of incompressible non-Newtonian power-law fluids is numerically investigated. Two effective active closed-loop control strategies are separately applied and compared in a real-time collaborative simulation framework that interactively connects the Fluent CFD solver with Matlab/Simulink. Numerical simulations reveal the important effects of power-law rheology and controller configuration on the key cylinder response/aerodynamic parameters and flow structure for a wide range of power-law index parameters (0.4≤n≤1.8). In the absence of the control system, the boundary layer thickness, the attached wake size/strength, and the length/thickness of the associated “shear stretching layer” (vortex shedding frequency) are found to noticeably increase (decrease) with increasing the power law index parameter. As the value of power-law index is largely increased, there is a notable shift of the synchronization (lock-in) region into the higher Reynolds number range besides its marked contraction which can passively complement the cylinder VIV control problem in the highly viscous shear-thickening fluid. When the active control system becomes operative, the superior performance and efficiency of the moment-based controller in effective and rapid mitigation of cylinder VIV and weakening of the vortex intensities through a desynchronization type of action with minimum actuator power requirements is demonstrated.

Nonlinear state feedback controller combined with RBF for nonlinear underactuated overhead crane system

This paper proposes a robust control scheme for an underactuated crane system. The presented scheme contains two control strategies, feedback control term and corrective control term, based on Radial Basis Function (RBF) neural networks. A feedback control term is deigned based on the nominal dynamic model of the controlled system. RBF neural networks have been used as adaptive control term to compensate for the system uncertainties and external disturbance. Lyapunov stability theorem has been used to derive updating laws for the weights of the RBF neural networks. To illustrate the robustness and effectiveness of the proposed controller, Matlab program is used to simulate the model of the nonlinear overhead crane system with the proposed control method, taking into account system uncertainties and external disturbance. Simulation results indicated superior control performance of the proposed control method compared to the other control methods used in the test.

H∞ Optimal Control Design of Static Var Compensator Coupling Hybrid Active Power Filter Based on Harmonic State-Space Modeling

Compared with the conventional active power filter (APF), the static var compensator coupling hybrid active power filter (SVC-HAPF) has distinct characteristics of low DC-link operating voltage, which can lower the system and operational costs. It also has a wider operational range than the conventional hybrid active power filter (HAPF). In this paper, a harmonic state-space (HSS) modeling is firstly proposed to address the nonlinearity issue of the SVC-HAPF. The HSS based SVC-HAPF model is linear time-invariant (LTI), which enables the optimal control techniques. Then, the linear matrix inequality (LMI) based H ∞ optimal control design is proposed, which finds the optimal controller parameters via convex optimization algorithm. The proposed controller for the SVC-HAPF achieves high steady-state accuracy, fast transient response, and fixed switching frequency simultaneously. Finally, simulation and experimental results are also provided to verify the effectiveness and performance of the optimal controller for the SVC-HAPF in comparison with the recent developed state-of-the-art multi-quasi-proportional-resonant (MQPR) controller, which shows superior control performances.

Wave flume tests of a semi-submersible platform controlled by a novel rotational inertia damper

Semi-submersible platforms (SSPs) may subject to excessive wave-induced vibrations, which should be effectively suppressed. In the past decades, some vibration control methods have been proposed and developed, such as the fixed heave plate (FHP) and tuned heave plate (THP). Recently, the authors proposed using inerter-based control device, i.e. rotational inertia dampers (RIDs), to control the vibrations of SSPs, and the control effectiveness has been demonstrated through analytical studies. To further investigate the feasibility and effectiveness of the proposed method, wave flume tests are conducted and reported in the present study. In particular, a 1:70 scaled SSP model was manufactured, and the vibration characteristics of the platform were determined firstly through free vibration tests. After that, the bare and RID equipped SSPs were tested under the regular and irregular waves. For comparison, the SSP equipped with the commonly used FHP was also tested. The experimental results demonstrate that both the RID and FHP systems are effective in reducing the heave motion of SSP. However, compared to the FHP, the RID system showed a much better control performance with a very small physical mass. The wave flume tests demonstrated the superior vibration control performances of RID on SSP.

Energy efficient design of bio-butanol purification process from acetone butanol ethanol fermentation

Butanol has the potential as a transportation fuel since its energy density is similar to gasoline and higher than ethanol. The Acetone-Butanol-Ethanol (ABE) fermentation is the current main method to produce bio-butanol where the fermentation should be operated in diluted surroundings to avoid toxicity to microorganisms. This article aims to develop efficient separation sequences to purify individual components from the ABE fermentation. The base design comes from the reference proposed by Patraşcu et al. [3]. With an updated thermodynamic model, several novel design alternatives are proposed and investigated to reduce energy consumption in the separation sequences. Therein, the proposal of removing acetone at first, and purifying ethanol via integration of distillation and membrace not only reduces total energy consumption and total annual cost by 57% and 52%, but also reaches targeting purity of 99.5% for butanol, acetone and ethanol. Heat integration and column stacking can reduce the overall energy consumption furthermore. This study also carries on plantwide control for the processes with heat integration and column stacking. The superior control performance with ±10% changes in throughput and feed compositions is validated for the proposed plantwide process control scheme.

Distributed Lyapunov-Based Model Predictive Formation Tracking Control for Autonomous Underwater Vehicles Subject to Disturbances

This article studies the formation tracking problem of a team of autonomous underwater vehicles (AUVs) with the ocean current disturbances. A distributed Lyapunov-based model predictive controller (DLMPC) is designed such that AUVs can keep the desired formation while tracking the reference trajectory, despite the presence of external disturbances. The DLMPC inherits the stability and robustness of the extended state observer (ESO)-based auxiliary control law and invokes online optimization to improve formation tracking performance of the multi-AUV system. The closed-loop stability of the multi-AUV system is guaranteed by the stability constraint that utilizes the ESO-based auxiliary controller and the associated Lyapunov function. Furthermore, the inter-AUV collision avoidance can be achieved by incorporating well-designed artificial potential fields-based cost term in the formation tracking cost function. Extensive simulations on the Saab Falcon AUVs are carried out, demonstrating the superior control performance and robustness of the proposed method.

Application of Adaptive and Switching Control for Contact Maintenance of a Robotic Vehicle-Manipulator System for Underwater Asset Inspection.

The aim of this study is to design an adaptive controller for the hard contact interaction problem of underwater vehicle-manipulator systems (UVMS) to realize asset inspection through physical interaction. The proposed approach consists of a force and position controller in the operational space of the end effector of the robot manipulator mounted on an underwater vehicle. The force tracking algorithm keeps the end effector perpendicular to the unknown surface of the asset and the position tracking algorithm makes it follow a desired trajectory on the surface. The challenging problem in such a system is to maintain the end effector of the manipulator in continuous and stable contact with the unknown surface in the presence of disturbances and reaction forces that constantly move the floating robot base in an unexpected manner. The main contribution of the proposed controller is the development of the adaptive force tracking control algorithm based on switching actions between contact and noncontact states. When the end effector loses contact with the surface, a velocity feed-forward augmented impedance controller is activated to rapidly regain contact interaction by generating a desired position profile whose speed is adjusted depending on the time and the point where the contact was lost. Once the contact interaction is reestablished, a dynamic adaptive damping-based admittance controller is operated for fast adaptation and continuous stable force tracking. To validate the proposed controller, we conducted experiments with a land robotic setup composed of a 6 degrees of freedom (DOF) Stewart Platform imitating an underwater vehicle and a 7 DOF KUKA IIWA robotic arm imitating the underwater robot manipulator attached to the vehicle. The proposed scheme significantly increases the contact time under realistic disturbances, in comparison to our former controllers without an adaptive control scheme. We have demonstrated the superior performance of the current controller with experiments and quantified measures.

Research on Factors Affecting Power Flow of Direct Current Grid Considering Different Control Modes

Direct Current grid technology based on flexible Direct Current transmission is one of the key means to solve large-scale grid integration and consumption of new energy. The flexible Direct Current transmission technology developed in recent years has superior control performance, strong flexibility, and independent control of active power. And reactive power, can be connected to the weak Alternating Current system. This article derives the power flow equation of the Direct Current transmission network by establishing a mathematical model of the Direct Current transmission network and combining different system-level control strategies in the Direct Current transmission network. The Newton-Raphson method is used to solve the power flow distribution of the Direct Current network when the master-slave control and droop control are adopted. The influence of the two control methods on the power flow distribution of the Direct Current network is compared through a calculation example.

Piecewise affine modeling and hybrid optimal control of intelligent vehicle longitudinal dynamics for velocity regulation

In this paper, a novel piecewise affine (PWA) modeling framework and a hybrid optimal control scheme based on the developed PWA model for the intelligent vehicle longitudinal dynamics are proposed. The proposed methodology approximates the nonlinear characteristics of the system major components, i.e. the engine, the automatic transmission and the tire, using the PWA representation method firstly. Due to the affine submodel switching behaviors of the PWA model and the fact that the intelligent vehicle must be worked in two discrete modes (driving and braking) for regulating the longitudinal velocity automatically, the control procedure of the intelligent vehicle longitudinal dynamics actually shows a hybrid nature with both continuous variables and discrete events. Thus, to further solve the system hybrid control problem, the intelligent vehicle longitudinal dynamics whose major components are modelled in the PWA form is further transformed into a computational mixed logical dynamical (MLD) model in this work, based on which a hybrid model predictive control (HMPC) strategy, which can simultaneously calculate the switching sequences of the intelligent vehicle longitudinal working modes (binary control inputs) and the throttle angle and braking pressure (continuous control inputs) during autonomous velocity regulation, is designed by solving a mixed-integer quadratic programming (MIQP) problem. Simulation and experimental results are finally provided to verify the superior control performance of the designed hybrid controller in longitudinal velocity regulation under typical driving conditions.

Design of Syncretic Fuzzy-Neural Control for WWTP

Owing to the possible existence of system failures and packet dropouts in the wastewater treatment process (WWTP), it is difficult to obtain sufficient data, which will result in data shortage. Therefore, it is a challenge to design an effective data-driven controller with the above data shortage issue for WWTP. To solve this problem, a syncretic fuzzy-neural controller (SFNC) was developed and analyzed in this article. First, the knowledge obtained from the operation conditions was made full use by a knowledge reconstruction mechanism to construct the initial condition of SFNC. Then, the proposed SFNC was able to obtain the accurate parameters and compact structure in the initialization phase. Second, a syncretic-form strategy (SFS) was designed to syncretize the knowledge from the fuzzy rules and the data from the operation process to optimize the structure of SFNC. Then, the adaptability of SFNC can be improved to achieve good control performance in the presence of insufficient data. Third, the stability of the developed SFNC was proved by using Lyapunov stability theorem. Then, the stability of SFNC was given to guarantee its successful application. Finally, the proposed controller was tested on the Benchmark Simulation Model No.1 to confirm its effectiveness. The results demonstrated that the proposed SFNC can achieve superior control performance than some other existing controllers.

Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter

A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) and attitude (roll, pitch, and yaw) control of a quadrotor vehicle. The derivation of a mathematical model of the quadrotor is formulated based on the Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain a superior control performance. Therefore, we exploit the advantages of the nature-inspired optimization algorithm called Grey Wolf Optimizer (GWO) to search for those optimal values. Hence, an improved version of GWO called IGWO is proposed and used instead of the original one. A comparative study with the conventional controllers, namely proportional derivative (PD), proportional integral derivative (PID), linear quadratic regulator (LQR), proportional linear quadratic regulator (P-LQR), proportional derivative and linear quadratic regulator (PD-LQR), PD2-LQR, and original GWO-based PD2-LQR, was undertaken to show the effectiveness of the proposed approach. An investigation of 20 different quadcopter models using the proposed hybrid controller is presented. Simulation results prove that the IGWO-based PD2-LQR controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).

Composite Learning Control of Overactuated Manned Submersible Vehicle With Disturbance/Uncertainty and Measurement Noise.

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

A New Adaptive Factor-Based Output Compensation Strategy for Offset Free Control of MLD–MPC with Model–Plant Mismatch

The mixed logical dynamical (MLD) model is a powerful framework of multimodel combinations for nonlinear systems control. Although superior control performance can be obtained by MLD-MPC, model–pla...