Control Input Research Articles

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

Data-Driven Model Predictive Control for Redundant Manipulators With Unknown Model.

The tracking control of redundant manipulators plays a crucial role in robotics research and generally requires accurate knowledge of models of redundant manipulators. When the model information of a redundant manipulator is unknown, the trajectory-tracking control with model-based methods may fail to complete a given task. To this end, this article proposes a data-driven neural dynamics-based model predictive control (NDMPC) algorithm, which consists of a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law. With the help of the DTJM updating law, the future output of the model-unknown redundant manipulator is predicted, and the MPC scheme for trajectory tracking is constructed. The ND solver is designed to solve the MPC scheme to generate control input driving the redundant manipulator. The convergence of the proposed data-driven NDMPC algorithm is proven via theoretical analyses, and its feasibility and superiority are demonstrated via simulations and experiments on a redundant manipulator. Under the drive of the proposed algorithm, the redundant manipulator successfully carries out the trajectory-tracking task without the need for its kinematics model.

Nodal Operating Envelopes vs. Network-wide Constraints: What is better for network-safe coordination of DERs?

For network-safe coordination of distributed energy resources (DERs), many approaches impose some form of constraint set to guarantee the safe operation of a distribution network. This paper presents a comparative analysis of two distinct approaches that leverage a constraint set for network-safe coordination: nodal operating envelopes versus network-wide constraints on the action of a DER aggregator. We investigate their respective strengths and limitations by considering information and communication requirements and trade-offs in terms of flexibility and fairness. The availability of potentially private information to the aggregator or to the distribution system operator determines which of the approaches is feasible. The results of the case studies suggest that if the goal is to maximize DER flexibility, a nodal constraint approach should be used. However, if the aggregator does not have information on how the constraints map to its DERs and/or does not have the ability to send out node-specific control inputs, the nodal constraint approach is not feasible and a network-wide constraint approach is needed. We show that a network-wide constraint approach constraining the aggregator’s control input provides a good balance between flexibility and fairness.

A switching approach to repetitive control for Takagi-Sugeno fuzzy systems

Repetitive control has learning properties and exhibits high accuracy in periodic control but struggles with nonlinearities and disturbances. To address this issue, the study presents a composite method of repetitive control and equivalent input disturbance based on the Takagi-Sugeno fuzzy model. The control structure takes into account both the continuous-discrete two-dimensional characteristics of the repetitive control and the membership function of the fuzzy system. As the first component of the control configuration, a repetitive controller is adopted for the high-precision tracking of periodic references. Then, an equivalent-input-disturbance estimator is used to compensate for exogenous disturbances. The closed-loop system is stabilized using the state feedback and the state observer. To ensure stability, membership function-dependent Lyapunov candidates are used to derive a less conservative stability condition. Consequently, all gains in the controller switch with the derivative sign of the premise variables. Finally, the developed approach is validated through comparisons with typical methods, demonstrating its effectiveness and advantages.

Appointed time control for flexible satellite with active vibration suppression

This work addresses the problem of achieving precise appointed time attitude control and rapid active vibration suppression in flexible satellites. Firstly, an appointed time piecewise sliding mode controller is developed to ensure the convergence of attitude error to the vicinity of the origin at the appointed time. The convergence time can be predetermined using a parameter. Secondly, a modal observer is introduced to estimate the vibrations in the flexible elastic modal, and a predefined-time active vibration suppression controller is designed to mitigate the elastic vibrations in flexible solar panels. The stability of both controllers is proven using the Lyapunov theory. Finally, simulations are conducted to validate the proposed control strategy. Through comparison with two predefined time controllers, it is evident that the proposed controller achieves more precise convergence time, smoother attitude variations, and minimal magnitude of control inputs.

Designing adaptive continuous event-triggered consensus protocol for nonlinear multi-agent systems with a nonautonomous leader

This article investigates the leader-follower consensus (LFC) issue for a class of Lipschitz-type nonlinear multi-agent systems (NMASs) that include one leader and multiple followers. To enable the leader to denote a flexible noncooperative reference trajectory, it is considered that the leader's control input (LCI) exists and cannot be obtained by other followers. A novel robust distributed adaptive continuous event-triggered control protocol is developed. Through rigorous analysis, it is shown that under this control protocol, the NMASs can reach LFC and the Zeno behavior is not exhibited. Compared to existing research works, the main merits of this control protocol are as follows: 1) It is chattering-free while can completely eliminate the adverse effects brought by the LCI; 2) The communication traffic between agents is lower; 3) It can be executed in a fully distributed way (i.e., no global information is required for the design process of this control protocol). Finally, the effectiveness of the developed control protocol is validated through an example.

A temporal prediction model for ship maneuvering motion based on multi-head attention mechanism

In real-world maritime conditions, accurately predicting ship maneuvering motion over ultrashort periods can enhance the development of more precise vessel control algorithms. This study proposes a prediction model for ship maneuvering that utilizes a multi-head attention mechanism. To form a basis for this data-driven approach, we gathered training data from turning and zigzag tests of a ship conducted in natural sea environments. The model's predictive capability is assessed by comparing two strategies over a fixed 30-time-step forecast horizon: iterative multi-step forecasting (IMS) and direct multi-step forecasting (DMS). Notably, the model employs distinct attention layers to examine the temporal relationships among various motion variables across the three degrees of freedom in the output. Moreover, it uses separate attention layers to explore the distinct time response traits of the input motion states and control signals. The results demonstrate that different motion state variables display unique temporal correlations under different signal sequences, and the proposed network architecture effectively captures these temporal response traits.

Precision motion control of rotary flexible link manipulators using polynomial input trajectories and feedback control

This paper aims to develop a high-precision controller for a flexible link manipulator using polynomial reference, feedforward input, and feedback control. The control objective is to move the tip of the flexible link to a target position as quickly as possible without exciting its resonant modes. Upon a frequency response-based system identification, a frequency-domain feedback controller is designed for reference tracking. The controller includes a proportional-integral controller, a lead filter, and a set of notch filters to control the rigid body and the flexible link dynamics. A set of polynomial reference and feedforward input trajectories are then designed to improve the tracking performance and minimize residual vibrations. The conventional polynomial reference is generated based on the rigid body motion of the system. A revised polynomial reference generation method is then developed by adding flexible motions to the conventional reference. Simulations and experimental tests indicate significant improvements in the performance of the control system using the revised polynomial trajectories.

Multiple feedback recurrent neural network based super-twisting predefined-time nonsingular terminal sliding mode control for quad-rotor UAV

This research deals with the overall predefined-time stability (PTS) of Unmanned Aerial Vehicles (UAV) ensuring rapid convergence based on a novel sliding mode control (SMC). The strength of predefined-time sliding manifolds lies in the convergence rate can be adjusted by an explicit parameter. For the limitation of chattering encountered by predefined-time SMC (PTSMC), a variable gain super-twisting algorithm (STA) with additional linear items is designed as the switch controller. To conserve the restrained computational resources of quadrotors, the equivalent control input is approximated by a multiple feedback recurrent neural network (MFRNN) directly, which is challenging for general recurrent neural networks. The proposed MFRNN is characterized by the incorporation of double-loop feedback within the layers, augmenting its capacity for accurate approximation. To address the vanishing gradients commonly encountered with traditional activation functions, LeakyRelu is chosen. The Lyapunov theory is utilized to ensure the PTS of overall system and obtain the MFRNN weight update laws. An experiment is conducted to validate the proposed scheme.

Control strategy for trading off solar power and control input while rendezvous and docking

This paper presents a novel strategy for decoupling the attitude and orbit equations of a CubeSat for rendezvous and docking with an uncontrollable cooperative target. For computing a safe trajectory, the proposed control algorithm takes into account the solar energy received by the CubeSat. The CubeSat is equipped with a thruster for orbit maneuvers, magnetic coils for attitude control, and four fixed single-sided rectangular solar panels. The coupled dynamics arising from the dynamical model of the spacecraft is solved using a novel weighted vector-based approach. This paper presents a linearized nonlinear optimal control problem arising in small spacecrafts while trying to maximize solar energy input in the rendezvous and docking process. An intermediate orbit is defined and used to divide the problem into two different optimal control problems: rendezvous optimization and docking optimization problems. A geometrical approach based on the shape of the chaser and the target is contemplated for collision avoidance while docking. The proposed controller design is modified whenever the sunlight is obstructed by the Earth, and the maneuvering controls are redesigned accordingly for optimal rendezvous and docking. Numerical simulations have been carried out to show the efficacy of the proposed concept for steering the trade-off between solar energy and control input during the rendezvous and docking of CubeSat with a tumbling target.

Periodic output regulation for 1-D wave equation subject to non-collocated control

In this paper, we focus on the periodic output regulation for a one-dimensional wave equation where the performance output is non-collocated with the control input. The exosystems considered here are multi-periodic time-varying, capable of generating periodic reference signals and disturbances within the domain and at the boundaries, each with distinct periods. Initially, a backstepping-based state feedback regulator is designed by tackling an ill-posed periodic regulator equation. Subsequently, leveraging an external matrix, we design a novel time-varying observer tailored for the transformed wave equation and the exosystems. An output feedback regulator is then obtained by replacing the states with their estimations. The tracking error is shown to decay exponentially and the closed-loop system is proved to be bounded. Some numerical simulations are presented to validate the results.

On [formula omitted]-performance of weakly-hard real-time control systems

This paper considers control systems with failures in the feedback channel, that occasionally lead to loss of the control input signal. A useful approach for modeling such failures is to consider window-based constraints on possible loss sequences, for example that at least r control attempts in every window of s are successful. A powerful framework to model such constraints are weakly-hard real-time constraints. Various approaches for stability analysis and the synthesis of stabilizing controllers for such systems have been presented in the past. However, existing results are mostly limited to asymptotic stability and rarely consider performance measures such as the resulting ℓ2-gain. To address this problem, we adapt a switched system description where the switching sequence is constrained by a graph that captures the loss information. We present an approach for ℓ2-performance analysis involving linear matrix inequalities (LMI). Further, leveraging a system lifting method, we propose an LMI-based approach for synthesizing state-feedback controllers with guaranteed ℓ2-performance. The results are illustrated by a numerical example.

Improvements to a novel learning algorithm for model-based wind turbine controllers

Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.

Distributed Optimal Attitude Synchronization Control of Multiple QUAVs via Adaptive Dynamic Programming.

This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) through the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. Considering attitude constraints in complex flying environments, a one-to-one mapping technique is utilized to transform the constrained systems into equivalent unconstrained systems. An improved nonquadratic cost function is constructed for each QUAV, which reflects the requirements of robustness and the constraints of control input simultaneously. To overcome the issue that the persistence of excitation (PE) condition is difficult to meet, a novel tuning rule of critic neural network (NN) weights is developed via the concurrent learning (CL) technique. In terms of the Lyapunov stability theorem, the stability of the closed-loop system and the convergence of critic NN weights are proved. Finally, simulation results on multiple QUAVs show the effectiveness of the proposed control strategy.

Adaptive Optimal Tracking Control of an Underactuated Surface Vessel Using Actor-Critic Reinforcement Learning.

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

Extended Two-State Observer-Based Speed Control for PMSM With Uncertainties of Control Input Gain and Lumped Disturbance

Extended Two-State Observer-Based Speed Control for PMSM With Uncertainties of Control Input Gain and Lumped Disturbance

Neural Network Control of Underactuated Surface Vehicles With Prescribed Trajectory Tracking Performance.

This article is concerned with the fast and accurate trajectory tracking control problem for a sort of underactuated surface vehicle under model uncertainties and environmental disturbances. A novel neural networks (NNs)-based prescribed performance control strategy is proposed to solve the problem. In the control design, a new type of performance function is constructed which provides a way to predefine the settling time and accuracy, straightforward. Then, a pair of barrier functions are employed to combat not only the position error but also the virtual control input. This evades the possible singularity or discontinuity of the control solution. Next, an initialization technique is exploited, removing the requirement for the initial condition of the control system. Finally, two NNs are employed to deal with the unknown ship nonlinearities. The performance analysis not only demonstrates the effectiveness of the proposed approach but also reveals its robustness against disturbances and unknown reference trajectory derivatives. There is, thus, no need to acquire such knowledge or employ specialized tools to handle disturbances. The theoretical findings are illustrated by a simulation study.

Robust residual generator design for sensor fault detection in twin rotor aerodynamic system

The twin rotor aerodynamic system (TRAS) reflects the dynamics of vertical take-off rotor systems. TRAS considers the coupling effect and gyroscopic disturbance torque as unwanted signals. For safe and reliable TRAS operation, proper control input is vital, along with fast fault indication. This paper addresses the output sensor fault detection problem in the linear model of TRAS subjected to l2-norm bounded disturbance. A robust design of an observer is formulated as H-/H∞ optimization problem to maximize the sensitivity/robustness criterion. The approach minimizes the disturbance effect on the residual signal, which leads to successful fault detection. Furthermore, a linear quadratic regulator-based state feedback controller is proposed to meet the system's desired transient response requirement. The incorporation of an observer-based residual generator for fault detection along with the state-feedback controller differs the current work from the existing work. Successful fault detection in the output sensor of TRAS depicts the good performance of the proposed observer.

A virtual wind tunnel for deforming airborne wind energy kites

This paper presents a quasi-steady simulation framework for soft-wing kites with suspended control unit employed for airborne wind energy. The kites are subject to actuation-induced and aero-elastic deformation and are described by a coupled aero-structural model in a virtual wind tunnel setup. Key contributions of the present work are a kinetic dynamic relaxation algorithm and a procedure to define a physically consistent initial state. For symmetric actuation, the kite is pitch-statically stable and the simulations converge to a static equilibrium state. Most soft-wing kites are not roll-statically stable and do not find a static equilibrium without a symmetry assumption, as this introduces non-zero roll- and yaw moments. Another important contribution is the introduction of a steady circular flight state that enables convergence without a symmetry assumption. By neglecting gravity, the kite can fly in a perfectly circular turning motion around the wind vector with a constant radius and constant rotational velocity without requiring active control input. In an idealized wind-aligned tether case, the difference in aerodynamic- and centrifugal force application centers makes it impossible to achieve both a force- and moment equilibrium. This was resolved by including an elevation angle that introduces a radial tether force component, which introduces a centrifugal and aerodynamic force difference. Therefore, an operating point with roll equilibrium can be found where the kite finds a static equilibrium, enabling the first quasi-steady simulations of turning flights. Simulated quantifications of soft-wing kite turning behavior, i.e., turning laws, contribute to better kite- and control design.

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.