Proposed Control Algorithm Research Articles

Digitalized Control Algorithm of Bridgeless Totem-Pole PFC with a Simple Control Structure Based on the Phase Angle

Compared to the conventional boost power factor correction (PFC) converter, a totem-pole bridgeless PFC has high efficiency because it does not have an input diode rectifier stage, but a current spike may occur when the polarity of the grid voltage changes. This paper proposes a digital control algorithm for bridgeless totem-pole PFC with a simple control structure based on the phase angle of grid voltage. The proposed algorithm has a PI-based double-loop control structure and performs DC-link voltage and input inductor current control. Rectifying switches operate based on the proposed rectification algorithm using phase angle information calculated through a single-phase phase-locked loop (PLL) to prevent current spikes. The feed-forward duty ratio value is calculated according to the polarity of the grid voltage and added to the double-loop controller to perform appropriate power factor control. The performance and feasibility of the proposed control algorithm are verified through a 3 kW hardware prototype.

Leader-Follower Formation of Unicycle Mobile Robots Using Quasi-Sliding Modes

This article presents a distributed control of unicycle mobile robots for the leader-follower configuration while maintaining a specific geometric distribution in the horizontal plane. The proposed control algorithm facilitates the formation of the follower robots with respect to the leader by using a quasi-sliding mode. The technique uses a pair of sliding variables, and the proposed controller guarantees the convergence of the sliding variables to a region close to zero. The particular formulation of these sliding variables ensures that their convergence to a bounded region around zero, provides asymptotic convergence of the formation errors towards a bounded region around the origin.

Formation tracking of multi-robot systems with switching directed topologies based on Udwadia-Kalaba approach

In this paper, a distributed formation tracking protocol is proposed for multi-robot systems with switching directed topologies based on the Udwadia-Kalaba approach. The basic idea is to use the consensus-based scheme to reconstruct formation tracking control requirement into a second-order constraint first, and then apply the Udwadia-Kalaba approach to obtain the constraint force required to achieve the formation tracking, and afterward derive the explicit equations of motion for the constrained multi-robot systems for the control algorithm design. The convergence analysis of the tracking error system is conducted to affirm that the proposed control algorithm can achieve the formation tracking when the switching directed topologies have a directed spanning tree across each switching time interval. Numerical simulations are performed to validate the effectiveness of the proposed algorithm.

Robust Position Control for an Electrical Automatic Transmission under Gear-Shifting Link Friction

The automotive industry is evolving, with software becoming a vital part of vehicles. Conventional automakers are shifting to software-centric entities, embracing over-the-air (OTA) updates and service-centric models. To move software-driven vehicles, the vehicle must also be electrified. Several automobile manufacturers are electrifying vehicle parts, and recently, a gear shift selector for automatic transmissions was adapted from mechanical to electronic. However, as conventional mechanical systems are modified to electrical systems, problems such as shift delay and accuracy emerge. This study addresses these problems that emerge in the electronic system type of automatic transmission, including a gear shift selector developed to electrify automobiles. Accordingly, we first analyze the structure of automatic transmission systems, then define the operation sequence. Next, a novel position control algorithm based on a disturbance observer is proposed to reduce shift delay and increase accuracy. The proposed algorithm operates harmoniously with the vehicle control unit (VCU). To verify the proposed algorithm, a hardware-in-the-loop simulation (HILS) was developed to experiment with vehicle shifting using a commercial electronic gear shift selector. Moreover, the proposed control algorithm for gear shifting in an automatic transmission was analyzed using experimental results obtained by assuming a specific driving situation in the HILS.

High precision control of direct-drive permanent magnet linear motor with adaptive friction compensation

To improve the control precision and stability of Direct-Drive Permanent Magnet Linear Motor (DPMLM) with frictional nonlinearity and uncertain disturbances at a low speed, an adaptive robust integral sign error control algorithm based on friction compensation of dual nonlinear observer is proposed in this study. An improved LuGre friction model is established to describe the friction phenomena of the system. The dual nonlinear observer is designed to observe the internal frictional state of the model. A parameter adaptive law is designed to perform parameter estimation of structured uncertainty. Robust integral of the sign of the error (RISE) term is designed to overcome the frictional nonlinear disturbances. The friction nonlinearity and parameter uncertainty are compensated by feedforward compensation. The bounded stability of the proposed controller is proved by Lyapunov stability theory. As suggested by the experimental results, RMSE index decreased by 10.8% and the control precision was improved effectively by the proposed control algorithm at a low speed.

Decentralized Control Mechanism Underlying Morphology-Dependent Quadruped Turning

Quadruped mammals can control the movement of their center of gravity when turning by skillfully utilizing their bodies to achieve adaptive turning movements. Interestingly, the low-speed turning behavior also changes depending on the animal’s morphology. Therefore, this study aims to understand the control algorithm of low-speed turning, which can reproduce the turning behavior according to the location of the center of gravity. Specifically, we constructed a control algorithm based on the knowledge that animals steer with the leg closest to the center of gravity and verified it with a quadruped robot whose center of gravity could be adjusted. Consequently, the behavior observed in animals was successfully reproduced, with a stable and large turning angle per time when the proposed control algorithm was used.

Electric Power Flow Control in Double Three-Phase Machines without Shift between Magnetic Axes

This paper examines the control strategy adopted for six-phase machines that have been designed as double three-phase machines without a displacement between the magnetic axes of the two sets of three-phase windings (6PH-ZS). The approach is based on vector control principles, enabling the independent control of direct- and quadrature-current components in both three-phase windings. The advantages of such an approach stem from the flexibility of the current distribution between windings, which aims to achieve the required flux and torque. The proposed control strategy is considered for both motor and generator operating regimes. However, an additional quasi-transformer regime is also addressed. While the classic operation regime requires the machine to produce either driving or generating/braking torque, the quasi-transformer regime enables the control of electric power flow between two three-phase windings. A theoretical analysis of the machine model and control algorithm for the flow of electric power through machines is presented for the three aforementioned operating regimes. Verification of the proposed control algorithm is performed through extensive experimental verification.

Integral sliding mode control design for uncertain impulsive systems with delayed impulses

This paper addresses the integral sliding mode control (ISMC) problem of uncertain impulsive systems with delayed impulses. Firstly, an integral sliding surface with embedded impulse information is constructed, which is able to counteract the intermittent impact of delayed impulses. Then an ISMC law is designed to guarantee the finite-time reachability of the predefined sliding surface without special restriction on system dynamics. Secondly, two different types of linear control laws with switching gains are proposed to robustly stabilize the sliding mode dynamics. The first type is independent of the size of impulse delays, and is used to tackle the impulses with unknown bounded delays. The second type aims at providing impulse-delay-dependent stabilization algorithms under the assumption that the delays are smaller than the impulse intervals. In the situation where the upper bounds of the uncertainties are characterized by two unknown constants, an adaptive ISMC law is proposed to ensure the existence of sliding mode. Switching Lyapunov functions and the state augmentation technique are introduced to exploit the positive/negative effects of delayed impulses. Several tractable conditions for designing the switching gains and the sliding surfaces are derived in terms of linear matrix inequalities. Finally, three numerical examples are presented to show the effectiveness of the proposed control algorithms and highlight their merits.

Sliding Mode Control of Buck DC–DC Converter with LC Input Filter

The employment of input filters in modern DC–DC converters is mandatory in order to ensure EMC (Electro-Magnetic Compatibility), provide power electronics with decent voltage and minimize the converter influence on the power grid. The LC type input filters bring also a possibility of voltage and current oscillation that may occur in the system. This oscillation may rise to a certain level that affects system stability. A wide range of methods are employed in order to attenuate these oscillations and allow appropriate system transient response. In this paper, the sliding mode control (SMC) strategy is proposed to reduce the input LC filter voltage and current oscillation and allow DC (Direct Current) output voltage control for both resistive and constant power load. The proposed control algorithm is intended for use in railway, tram and trolleybus DC/DC converters. For the proposed scheme of control simulation, a model is developed using Simulink software. Furthermore, laboratory stand experiments are carried out to verify simulation results.

Fully Distributed, Event-Triggered Containment Control of Multi-Agent Systems Based on Wireless Sensor Networks and Time Base Generators

In this study, wireless sensor networks and time base generators are used to solve the fixed-time containment control problem in multi-agent systems with fixed topologies. A new event-triggered control protocol is proposed, which combines a fully distributed method and a time base generator (TBG). The goal is to converge the states of all followers to the convex hull formed by the leader. The controller reduces communication and improves control efficiency by integrating a fully distributed control mechanism using wireless sensor networks. In addition, a time base generator (TBG) is added to ensure that the dwell time continues to be pre-specified and independent of initial conditions. Using matrix theory, the original system is transformed into an error system, and its stability is analyzed by the Lyapunov method. The necessary and sufficient conditions for solving the time consensus containment control problem in multi-agent systems are determined and Zeno behavior is avoided. The effectiveness of the proposed control algorithm is illustrated by numerical examples.

MEASUREMENT OF LINEAR VELOCITY USING A MOBILE ROBOTIC PLATFORM WITH COMPUTER VISION

This article presents an approach to integrating computer vision algorithms into the control system of traction electric drives in rail transport. It demonstrates the utilization of computer vision algorithms for calculating linear velocity as an alternative to conventional sensors like wheel odometers, GPS, DGPS and inertial sensors, which may prove ineffective on slippery surfaces and at low speeds. As a result, this article focuses on employing linear velocity as feedback within the control system to enhance power efficiency during starting and stopping and to prevent wheel slip. The electric drive control system has been successfully implemented and tested on a robotics platform designed for simulating dynamic behaviors in real rail transports scenarios. The article details the development process of this robotics platform, which is employed to mimic real-world dynamic conditions in rail transport. The proposed control algorithm for speed estimation is assessed using a specially designed test bench, revealing its capability to predict speed with a relatively high degree of accuracy. Additionally, an optical flow algorithm for velocity estimation is introduced and evaluated through a specially designed test rig, indicating a strong correlation between the predicted vehicle speed and the measurements from precision optical encoders. The study also determines the optimal feature window size for real-time optical flow rate estimation. In summary, this approach exhibits significant potential for accurate speed estimation. Ongoing experiments are being conducted under various real-world conditions, with future research aimed at developing a dependable autonomous system for speed measurement. The integration of modern digital computer vision technologies not only enhances the traction characteristics of electric drives but also extends the capabilities of traction electric drives to meet the rigorous demands of industrial equipment.

Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

AbstractTo address the challenge of tracking the optimal oxygen excess ratio (OER) in the cell stack cathode of the proton exchange membrane fuel cell (PEMFC), which is subject to norm‐bounded parameter uncertainty and frequent external perturbations, this manuscript provides a novel robust model predictive control strategy with a state feedback control structure and hard constraints. We aim to minimize the tracking error of OER while maximizing the net power under varying operational conditions of the PEMFC. Initially, a control‐oriented model for the air supply system is built and its accuracy is validated by comparing it with experimental results. Next, we construct the discrete state‐space equations for optimal control and employ the linear matrix inequality (LMI) method to transform the problem of difficult min‐max optimization solutions into a convex optimization problem with LMI constraints. Finally, the effectiveness of the proposed control algorithm is validated through simulation. The results demonstrate the strong robustness and superiority of the proposed controller against uncertainties in plant parameters for robust OER trajectory tracking. It improves the transient performance through robust precision tracking applications, which will eventually accelerate PEMFC commercialization.

Fast Distributed Platooning of Connected Vehicular Systems With Inaccurate Velocity Measurement

Fast and smooth driving is a preferable consideration in platooning algorithm development for intelligent autonomous vehicular systems. It can improve traffic efficiency while guaranteeing passenger comfort. To this end, this work investigates the fast distributed platooning problem of connected vehicular systems. Taking the inaccurate velocity measurement into consideration, an extended state observer (ESO) based on a fractional order faster nonsingular terminal sliding mode (FNTSM) is proposed for velocity and disturbance estimation simultaneously. An FNTSM control algorithm based on the double power reaching law is developed to reach fast platooning while guaranteeing string stability of the connected vehicular systems regardless of zero or nonzero initial spacing error conditions. The salient features of the proposed platoon controller are that system convergence can be achieved in finite time and the time-varying external disturbances can be estimated accurately. Finally, simulation and experiment studies are conducted to demonstrate the effectiveness and efficiency of the proposed control algorithm.

Adaptive Finite-Time Optimal Formation Control for Second-Order Nonlinear Multiagent Systems

This article addresses an adaptive backstepping finite-time optimal formation control problem for second-order multiagent systems (MASs) with unknown nonlinear dynamics. Neural networks (NNs) are used to identify the unknown uncertain terms in the controlled system. Then, the finite-time optimal formation control is designed by constructing a novel optimal performance index function containing exponential power terms based on the framework of identifier–actor–critic. It is proved that all signals in the system are bounded in finite time, and the formation control is simultaneously achieved at minimum cost. The effectiveness and superiority of the proposed control algorithm are verified by simulation comparisons and data analyses.

Adaptive Cooperative Secure Control of Networked Multiple Unmanned Systems under FDI Attacks

With the expanding applications of multiple unmanned systems in various fields, more and more research attention has been paid to their security. The aim is to enhance the anti-interference ability, ensure their reliability and stability, and better serve human society. This article conducts adaptive cooperative secure tracking consensus of networked multiple unmanned systems subjected to false data injection attacks. From a practical perspective, each unmanned system is modeled using high-order unknown nonlinear discrete-time systems. To reduce the communication bandwidth between agents, a quantizer-based codec mechanism is constructed. This quantizer uses a uniform logarithmic quantizer, combining the advantages of both quantizers. Because the transmission information attached with the false data can affect the accuracy of the decoder, a new adaptive law is added to the decoder to overcome this difficulty. A distributed controller is devised in the backstepping framework. Rigorous mathematical analysis shows that our proposed control algorithms ensure that all signals of the resultant systems remain bounded. Finally, simulation examples reveal the practical utility of the theoretical analysis.

Robust Consensus-based Formation Control of a Group of UAV

Consensus-based formation control (CFC) is one of the most phenomenal formation control methods designed to achieve consensus between any vehicles that using and sharing their position and/or linear velocity with each other in a swarm mission. In this paper, a robust CFC (R-CFC) has been designed and proposed to realize pre-defined formation shapes with a team of unmanned aerial vehicles (UAVs). First, the double integrator dynamics of an UAV is presented. Second, the graph theory is explained briefly to understand any adjacency between UAVs. Then, the proposed R-CFC algorithm has been derived and the stability analysis has been proven via algebraic Riccati stability theory based on Lyapunov stability theorem. After that, the effectiveness of the proposed controller has been tested in real time outdoor tests. The experimental results show that the UAVs have been able to create the desired formation shapes and are less affected by external disturbances such as wind thanks to the proposed control algorithm.

Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning

This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an enhanced version of the Deep Deterministic Policy Gradient (DDPG) algorithm, is adopted. Incorporating Distributed Prioritized Experience Replay (DPER), Ape-X DDPG trains neural network function approximators to obtain a data-driven DIRW active steering controller. This controller is utilized to control the input torque of each wheel, aiming to improve the steering capability of IRWs. Simulation results indicate that compared to the existing model-based H∞ control algorithm and data-driven DDPG control algorithm, the Ape-X DDPG active steering controller demonstrates better curving steering performance and centering ability in straight tracks across different running conditions and significantly reduces wheel–rail wear. To validate the proposed algorithm’s efficacy in real vehicles, a 1:5 scale model of the DIRW vehicle and its digital twin dynamic model were designed and manufactured. The proposed control algorithm was deployed on the scale vehicle and subjected to active steering control experiments on a scaled track. The experimental results reveal that under the active steering control of the Ape-X DDPG controller, the steering performance of the DIRW scale model on both straight and curved tracks is significantly enhanced.

Adaptive Predefined-Time Bipartite Consensus Tracking Control of Constrained Nonlinear MASs: An Improved Nonlinear Mapping Function Method.

This work focuses on the problem of predefined-time bipartite consensus tracking control for a class of nonlinear MASs with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is developed, where both cooperative communication and adversarial communication among neighbor agents are implemented. Different from the finite-time and the fixed-time controller design methods for MASs, the prominent advantage of the controller design algorithm presented in this work is that our algorithm can make the followers track either the output or the opposite output of the leader within the predefined time in accordance to the user requirements. In order to obtain the desired control performance, an improved time-varying nonlinear transformed function is skillfully introduced for the first time to handle the asymmetric full-state constraints and radial basis function neural networks (RBF NNs) are employed to deal with the unknown nonlinear functions. Then, the predefined-time adaptive neural virtual control laws are constructed by using the backstepping technique, while their derivatives are estimated by the first-order sliding-mode differentiators. It is theoretically testified that the proposed control algorithm not only guarantees the bipartite consensus tracking performance of the constrained nonlinear MASs in the predefined time but also remains the boundedness of all the resulting closed-loop signals. Finally, the simulation research on a practical example shows the validity of the presented control algorithm.

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation.

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

Adaptive Fed-batch Control of Escherichia coli Fermentation for Protein Production

A new adaptive linearizing control algorithm that stabilizes the carbon source concentration in a desired value is proposed. This algorithm is applied to recombinant protein production by Escherichia coli. A model for control of the investigated process is derived. The model identification is made based on experimental data of the batch phase of the process. The operating model includes two sub-models. Each of them describes one of the two physiological states through which the process passes. Switching from one model to another depends on the sign of a key parameter obtained from the acetate measurements. A cascade scheme of software sensors for the estimation of two biomass growth rates included in the structure of the proposed control algorithm is derived. Simulation studies of the developed closed system have been carried out. The results of the impact of an open-loop control system on the same object are compared.