Challenges For Control Design Research Articles

Disturbance rejecting PID-FF controller design of a non-ideal buck converter using an innovative snake optimizer with pattern search algorithm

The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID–FF–controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications.

Robust adaptive control of delayed systems with multiple uncertainties and polynomial nonlinearities

This work discusses the issue of robust adaptive control for delayed systems with multiple uncertain factors. The system uncertainties cover uncertain dynamics, unknown system parameters, and external disturbances. Besides, the growing conditions have both low-order and high-order polynomial nonlinearities, which bring in numerous challenges for control design. The suitable transformations are first introduced to obtain a new system with a dynamic gain. Then, the control design of an auxiliary system is considered. By introducing the Lyapunov-Krasovskii (L-K) functional method, and providing an important result for an auxiliary system, the adaptive homogeneous control strategy is proposed to achieve global asymptotic stability (GAS) of the entire system. Besides, by considering the external disturbances, the method is also extended to design robust adaptive tracking controller. Numerical and practical examples are provided to verify the theory.

Bumpless transfer switched control of aircraft for heavy payload dropping missions

Heavy payload airdrops induce significant changes in flight dynamics, posing a formidable challenge in flight controller design. This paper is concerned with the flight control issue of aircraft for heavy payload dropping missions. A multi-equilibrium switched systems scheme is introduced to describe dramatic variations in flight dynamics and trim points, thus covering complex scenarios where the aircraft drops heavy payloads at non-central positions. Then, a bumpless transfer switched controller for the underlying systems is proposed, with the aim of ensuring system stability and suppressing undesirable control bumps. In contrast to existing studies on switched systems with a common equilibrium, the proposed stability conditions and bumpless transfer methods are extended to encompass the realm of multi-equilibrium switchings. Simulations and hardware-in-loop experiments demonstrate that the proposed method provides a theoretical assurance for the stability of the flight control system and exhibits superior bumpless transfer performance.

Model‐reference adaptive and self‐tuning controls of telescopic mobile cranes with the impact of elastic cables, viscoelastic cylinders, and winds

SummaryTelescopic truck crane is an under‐actuated mechanical system with three actuators controlling five outputs. Its complex structure, shape variation, uncertain parameters, elastic cables, and viscoelastic cylinders are great challenges in control design. This article proposes two adaptive robust controllers for such cranes. A control core assures robustness based on back‐stepping and Lyapunov stability. The adaptation is achieved by employing model‐reference adaptive and self‐tuning control approaches. Thus, the benefits of these adaptive techniques are introduced into the control systems. The application to a practical crane shows the effectiveness of the proposed controllers. The crane has robustness and adaptation despite the elasticity and friction of the load‐carrying cable, the viscoelasticity of the cylinders, the variation of crane parameters, and the presence of winds. Comprehensive comparison indicates the state of the art and the shortcomings of the control systems.

Modeling and Adaptive Control for Tendon Sheath Artificial Muscle Actuated Bending-Tip Systems With Unknown Parameters and Input Hysteresis: An Experimental Research

Due to the flexible structure and extension capacity, the tendon-sheath artificial muscle (TSAM) actuated bending-tip (TAB) system has attracted increasing efforts in the field of rehabilitation and surgical robots. However, the TAB system usually suffers from unknown input hysteresis, unidentifiable parameters, and unexpected external disturbance, which bring great challenges for precise dynamic modeling and robust controller design. So far, there is still no available strategy to deal with these complicated nonlinearities for the TAB system. In this paper, the Euler-Lagrange dynamics is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> established for the TAB system based on rigorous energy analysis. Then, a novel adaptive controller is developed based on the original dynamics <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> any approximation, which can suppress the unavoidable effects of unknown parameters and input hysteresis, and achieve high-precision tracking performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> . Specially, the proposed adaptive method can also weaken the widely-used constant curvature assumption. By utilizing the Lyapunov-based technique, the stability of the closed-loop system is proven. Finally, to further validate the effectiveness and the robustness of the proposed controller, a series of hardware experiments are carried out on a self-built flexible ureteroscopy robot.

A data-driven control methodology applied to throttle valves

Electric throttle valves represent a challenge for control design, as their dynamics involves strong nonlinearities, characterized by an asymmetric hysteresis. Large variability in the characteristics of each valve and erratic steady-state behaviors can also be noticed by carrying out experiments on multiple valves, impairing classical model-based control strategies. Nevertheless, local data-driven linear models can be obtained by system identification, and simple proportional–integral (PI) digital controllers can be tuned individually for each valve, providing good tracking performance. As these controllers cannot be transposed from one valve to another, a robust control design is considered. Taking into account the variability of electric throttle valves, a real-time data-driven strategy is then proposed, using identification in closed-loop and controller re-design. This methodology is necessary if control performance is a key issue, and can be embedded on a low-cost controller board (Arduino® Mega 2560). Experimental results going from frequency analysis and linear design to real-time data-driven control illustrate the methodology presented in the paper.

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme.

Oblique Projection-Based Modal Matching Algorithm for LPV Model Order Reduction of Aeroservoelastic Systems

An aeroservoelastic system can be described as a gridding-based linear parameter-varying (LPV) model, whose dynamic characteristics usually vary with the airspeed. Due to the high order of the system, it is necessary to perform order reduction on LPV models to overcome the control design challenges. However, when directly extending linear time-invariant (LTI) model order reduction technologies to the LPV system, states of the reduced-order LTI models generated separately at different grid points could be inconsistent. In this paper, a novel modal matching algorithm is proposed to solve the problem of state inconsistency by identifying the internal connection between the models at adjacent grid points. An oblique projection-based distance metric is defined to improve the reliability of the modal matching algorithm. The reduced-order LPV model constructed based on this method would have a high fidelity relative to the original model and a smooth interpolation performance between grid points. The proposed algorithm is applied to the X-56A aircraft, and numerical results show its effectiveness.

Formal Verification of Deep Brain Stimulation Controllers for Parkinson's Disease Treatment.

Deep brain stimulation (DBS) is a widely accepted treatment for the Parkinson's disease (PD). Traditionally, it is done in an open-loop manner, where stimulation is always ON, irrespective of the patient needs. As a consequence, patients can feel some side effects due to the continuous high-frequency stimulation. Closed-loop DBS can address this problem as it allows adjusting stimulation according to the patient need. The selection of open- or closed-loop DBS and an optimal algorithm for closed-loop DBS are some of the main challenges in DBS controller design, and typically the decision is made through sampling based simulations. In this letter, we used model checking, a formal verification technique used to exhaustively explore the complete state space of a system, for analyzing DBS controllers. We analyze the timed automata of the open-loop and closed-loop DBS controllers in response to the basal ganglia (BG) model. Furthermore, we present a formal verification approach for the closed-loop DBS controllers using timed computation tree logic (TCTL) properties, that is, safety, liveness (the property that under certain conditions, some event will eventually occur), and deadlock freeness. We show that the closed-loop DBS significantly outperforms existing open-loop DBS controllers in terms of energy efficiency. Moreover, we formally analyze the closed-loop DBS for energy efficiency and time behavior with two algorithms, the constant update algorithm and the error prediction update algorithm. Our results demonstrate that the closed-loop DBS running the error prediction update algorithm is efficient in terms of time and energy as compared to the constant update algorithm.

Study design approaches for future active-controlled HIV prevention trials.

Vigorous discussions are ongoing about future efficacy trial designs of candidate human immunodeficiency virus (HIV) prevention interventions. The study design challenges of HIV prevention interventions are considerable given rapid evolution of the prevention landscape and evidence of multiple modalities of highly effective products; future trials will likely be'active-controlled', i.e., not include a placebo arm. Thus, novel design approaches are needed to accurately assess new interventions against these highly effective active controls. To discuss active control design challenges and identify solutions, an initial virtual workshop series was hosted and supported by the International AIDS Enterprise (October 2020-March 2021). Subsequent symposia discussions continue to advance these efforts. As the non-inferiority design is an important conceptual reference design for guiding active control trials, we adopt several of its principles in our proposed design approaches. We discuss six potential study design approaches for formally evaluating absolute prevention efficacy given data from an active-controlled HIV prevention trial including using data from: 1) a registrational cohort, 2) recency assays, 3) an external trial placebo arm, 4) a biomarker of HIV incidence/exposure, 5) an anti-retroviral drug concentration as a mediator of prevention efficacy, and 6) immune biomarkers as a mediator of prevention efficacy. Our understanding of these proposed novel approaches to future trial designs remains incomplete and there are many future statistical research needs. Yet, each of these approaches, within the context of an active-controlled trial, have the potential to yield reliable evidence of efficacy for future biomedical interventions.

Efficient hierarchical robust adaptive control of mode transition for parallel hybrid electric vehicles

A fast and smooth mode transition process for parallel hybrid electric vehicles (HEVs) can improve vehicle drivability. At the same time, system uncertainty resulting from friction, gearbox, parameter perturbation, production deviations, and external disturbance of the mode transition process makes control design challenges. In this regard, this paper introduces a novel efficient hierarchical robust adaptive mode transition control strategy to achieve an accurate and fast engagement of the clutch of a parallel HEV during mode transition in the case of uncertain system parameters and unmeasurable actual clutch torque in this paper. At the upper level of the controller, a robust adaptive sliding mode control (RASMC) is proposed to calculate the clutch transmitted required torque based on the current states of the electric motor (EM) and engine, and the control parameters are optimized via the particle swarm optimization (PSO) algorithm by compromising the vehicle jerk and the clutch slipping energy loss. Moreover, a fuzzy controller is designed to calculate the expected clutch engaging speed by employing the deviation of the desired and actual clutch torques, and the actual clutch torque is not a measured value but an estimate obtained by a proportional-integral (PI) observer. At the lower level, an adaptive finite-time control (AFTC) scheme is developed to overcome the gear backlash, parameter perturbation, and external disturbance during the clutch position tracking process. The effectiveness and advantage of the proposed control strategy are verified by both MATLAB/Simulink simulations and the hardware-in-loop (HIL) test.

Improved Third Order PID Sliding Mode Controller for Electrohydraulic Actuator Tracking Control

An electrohydraulic actuator (EHA) system is a combination of hydraulic systems and electrical systems which can produce a rapid response, high power-to-weight ratio, and large stiffness. Nevertheless, the EHA system has nonlinear behaviors and modeling uncertainties such as frictions, internal and external leakages, and parametric uncertainties, which lead to significant challenges in controller design for trajectory tracking. Therefore, this paper presents the design of an intelligent adaptive sliding mode proportional integral and derivative (SMCPID) controller, which is the main contribution toward the development of effective control on a third-order model of a double-acting EHA system for trajectory tracking, which significantly reduces chattering under noise disturbance. The sliding mode controller (SMC) is created by utilizing the exponential rule and the Lyapunov theorem to ensure closed-loop stability. The chattering in the SMC controller has been significantly decreased by substituting the modified sigmoid function for the signum function. Particle swarm optimization (PSO) was used to lower the total of absolute errors to adjust the controller. In order to demonstrate the efficacy of the SMCPID controller, the results for trajectory tracking and noise disturbance rejection were compared to those obtained using the proportional integral and derivative (PID), the proportional and derivative (PD), and the sliding mode proportional and derivative (SMCPD) controllers, respectively. In conclusion, the results of the extensive research given have indicated that the SMCPID controller outperforms the PD, PID, and SMCPD controllers in terms of overall performance.

Robustness Analysis and Controller Design Based on a Generalized Model of Nonisolated Multiphase DC–DC Converter

The nonisolated multiphase dc–dc converter (NMDC) has important research value and broad application prospect in fields such as smart grid and new energy vehicles because of its high power density and low output ripple. However, with the increase of the phase number, the parameter inconsistency among each phase will make the NMDC model quite complex. Moreover, parameter uncertainty and quadratic nonlinearity in the small-signal model can degrade control system performance, leading to a big challenge in controller design. In this paper, the generalized robust control model and the method of robust controller designing of NMDC concerning parameter uncertainty and quadratic nonlinearity is developed. Firstly, parameter uncertainty is described by convex polyhedra model. Secondly, the Lyapunov theorem is applied to solve the linear and quadratic nonlinearity of the control model. Finally, with the method of Linear Matrix Inequality (LMI) region pole configuration, the analytical solution of the control model is achieved. Based on the proposed model, the independent control of NMDC with reduced sensors, including current sharing, flexible power distribution and robust control of the NMDC are realized. Both simulation and experimental results verify the effectiveness of the controller. Compared with traditional controller, the controller proposed in this paper can be stable in more complicated working conditions, and has a faster adjustment time, suitable for multiple topologies as well.

Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Pneumatic artificial muscle actuators possess great potential in compliant rehabilitation devices since they are flexible and lightweight. The inherent high nonlinearities, uncertainties, hysteresis and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. The angle tracking problem based on iterative learning control technology is considered in this work. This research proposes a new initial-rectification adaptive iterative learning control scheme for a pneumatic artificial muscle-actuated device with nonzero initial errors and iteration-varying reference trajectories. A barrier Lyapunov function is used to deal with the constraint requirement. A new initial rectification construction method is given to solve the nonzero initial error problem. Nonparametric uncertainties in the system are approximated by using a neural network, whose optimal weight is estimated by using difference learning method. As the iteration number increases, the system states of angle and angular velocity can accurately track the reference trajectories over the whole interval, respectively. In the end, the simulation results show excellent trajectory tracking performance of the iterative learning controller even if the reference trajectories are non-repetitive over the iteration domain.

Robust Predictive Control for Modular Solid-State Transformer With Reduced DC Link and Parameter Mismatch

This article presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method that can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This article focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues, such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link's nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching SST (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end rectifier as an example.

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

This paper deals with the trajectory tracking control problem of a planar underactuated tendon-driven truss-like manipulator (UTTM) by using fuzzy logic control methods, including type-1 fuzzy and interval type-2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon-driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic-based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type-1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type-1 fuzzy controllers, an interval type-2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type-1 and interval type-2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type-2 fuzzy-based trajectory tracking controller provides better performance than the type-1 counterpart in terms of tracking accuracy and capacity against uncertainties.

Behavioral Harmonization of a Cyclic Vehicular Platoon in a Closed Road Network

This paper presents a method to dynamically harmonize the behaviors of cyclic vehicular platoons in a closed road network. Different from the open-chain counterpart, a cyclic platoon formulates a traffic flow ring constrained by interconnection coupling feature and finite-length geometry, which brings challenges for platoon control design and analysis. First, by exploiting the circulant structure of homogeneous cyclic platoons, the closed-loop dynamics is decomposed into a set of decoupled systems with the same low dimension as a single vehicle. Based on this decomposition, an analytical necessary and sufficient stability condition is derived for the design of distributed feedback controllers. This necessary and sufficient condition is further relaxed into sufficient conditions to remove the dependence on the platoon size. Next, the string stability is proved with the sufficient conditions derived above based on frequency-domain analysis. Then, based on integral sliding mode control, we prove that heterogeneous vehicles can behave like homogeneous ones so that the results on homogeneous cyclic platoons can be extended to heterogeneous cyclic platoons. Finally, the equilibrium velocity and spacing errors are derived to explain the effect of geometry conditions on the steady-state behavior. Numerical simulations using both linear and nonlinear vehicle models are conducted to validate our findings.

Hierarchical model-based predictive controller for a hybrid UAV powertrain

The emerging trend of vehicle electrification is transforming the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design. A notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This article seeks to address this challenge through the design and validation of a model predictive controller for a hybrid unmanned aerial vehicle powertrain. A multi-domain extension of the graph-based modeling framework is formulated and used to model the multi-physics behavior of the air vehicle. An extensive model validation procedure is performed and the validated graph model is used to develop two control strategies: one baseline and one predictive controller. To coordinate multi-timescale system dynamics, the predictive controller leverages a hierarchical control architecture to plan a battery state of charge bound. The control strategies are experimentally validated and show that the advanced controller yields improvements in performance and reliability metrics while reducing fuel consumption by ∼10%.

Admissibility analysis for autonomous underwater vehicle based on descriptor model with time-varying delay

This article investigates an enhanced optimal robust time-delay stabilizer for an autonomous underwater vehicle in the descriptor model. Time-delay, model uncertainty, and actuator saturation constraint are some practical challenges in autonomous underwater vehicle controller design. In this regard, an appropriate autonomous underwater vehicle descriptor model is obtained, and sufficient stabilization conditions are determined in the terms of linear matrix inequality. The obtained criterion guarantees the system to be regular, impulse-free, and stable. Meanwhile, the delay-dependent and rate-dependent conditions are taken into account. Furthermore, uncertainty and time-delay are time-variant. This method includes a tuning factor for practical design aspects and tradeoff among desired requirements. Also, as an essential general requirement in non-linear systems, the maximal estimate of the attraction domain is proposed as an optimization problem. Numerical examples and simulations illustrate that the proposed methods are effective and useful in less conservative results. The technique can be generalized and applied to the most conventional autonomous underwater vehicles.

Desired dynamic equational proportional‐integral‐derivative controller design based on probabilistic robustness

AbstractUncertainties existing in industrial processes always result in many challenges for controller design. To enhance the ability of the closed‐loop system to handle the uncertainties, a desired dynamic equational (DDE) proportional‐integral‐derivative (PID) controller is designed based on probabilistic robustness (PR) in this article. The necessity of the proposed design method is demonstrated by introducing the problem formulation. Based on its fundamentals, DDE PID designed based on PR (DDE‐PR PID) is proposed for uncertain systems and corresponding design procedure is summarized as a flow chart. Then the proposed DDE‐PR PID is designed for several typical processes and simulation results indicate that the proposed DDE‐PR PID cannot only achieve satisfactory control performance for nominal systems, but also satisfy control requirements for all uncertain systems with the maximum probability. Finally, the proposed DDE‐PR PID is applied to the nonlinear model of a fluidized bed combustor unit and the level system of a water tank. Its superiority in robustness is validated by both simulations and experiments, which shows the promising prospect of DDE‐PR PID in future power industry. On the other hand, the DDE method is extended to the generalized two‐degree‐of‐freedom PID controller and its bandwidth‐parameterization is proposed in this article as well.