Proposed Control Algorithm Research Articles

Byzantine-Resilient Second-Order Consensus in Networked Systems.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

Adaptive Formation Tracking Control of Multiple Vertical Takeoff and Landing UAVs With Bearing-Only Measurements.

This article studies the leader-following formation tracking control problem of multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) subject to uncertain parameters, in which the target formation configuration is defined by using the interneighbors' bearing vectors. An adaptive formation control algorithm with bearing-only measurements is proposed under a hierarchical control framework. More specifically, a saturated adaptive distributed control force is developed in the position loop with bearing-only measurements. Since the bearing vectors with respect to the neighbors can be directly measured by low-cost onboard cameras, the proposed formation algorithm can be implemented without interagent communication. Subsequently, in the attitude loop, an adaptive hybrid control scheme via two modified Rodrigues parameters (MRPs) sets is proposed, which achieves the command attitude tracking globally and also avoids the unwinding problem of MRPs. Based on the Lyapunov stability analysis and hybrid theory, we prove that the overall closed-loop error system is globally asymptotically stable. Finally, we provide a numerical example to demonstrate the effectiveness of the proposed control algorithm.

Arc Length Spacing-Driven Cooperative Elliptical Encircling Control of Robots: Design and Experiments

Most studies of cooperative enclosing control are centered around moving along the shared circle with available position information. Note that Global Positioning System (GPS) is typically inaccessible under hostile scenar- ios, and elliptical circumnavigation may provide a more flexible and high-efficiency entrapping solution, whilst the non-orthogonality of axial and tangential velocities and the lack of GPS enable elliptical entrapping to be more challeng- ing, and the related outcomes are still rare. Following this line, a novel coordinated error in terms of arc length spacing is skillfully formulated and included in elliptical encircling control to characterize cooperative behaviors without trigg- ering frequent speed variation and unstable observation. Particularly, to remedy the dependence on accessible global position, a new bearing-based estimator under a local frame is proposed to recover the relative coordinate, then a modified cooperative elliptical encircling control strategy is formulated without entailing global position data. Lyapunov stability analysis demonstrates that all error variables are ultimately bounded. Both simulations and experiments are implemented to illustrate the efficacy of proposed control algorithm.

Finite-Time Output-Feedback Formation Control for High-Order Nonlinear Multiagent Systems With Obstacle Avoidance

This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with the consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using an integral-multiplicative tangent Lyapunov-barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching. Then, combining the finite-time method and the backstepping theory, an output-feedback finite-time controller is designed for high-order nonlinear MASs. Theoretical analysis indicates that the control algorithm can not only make agents accomplish the formation obstacle avoidance task but also guarantee the boundedness of signals in the system. A simulation demonstrates the effectiveness of the proposed control algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article studies the formation obstacle avoidance problem for MASs which plays an important role in transportation, exploration and rescue activities. Unmeasurable states and dead-zone input, which often occur in practice, are also considered and handled by a neural networks k-filter observer. An integral-multiplicative tangent LBF is utilized to complete the obstacle avoid task which is necessary for actual systems. Moreover, to be more feasible in industrial applications, the finite-time control strategy is used in the controller design.

Current Measurement Gain Compensation Using High-Frequency Signal Injection in Dual Three-Phase PMSM Systems

To online compensate the scaling errors in current measurement caused by incorrect calibration, temperature, aging, and noise, this paper proposes a new solution based on high-frequency (HF) carrier voltage injection in dual three phase permanent magnet synchronous machines. The relationship between the measured HF currents and scaling errors is derived. It shows that the amplitudes of the HF currents are proportional to the scaling errors, based on which three HF current control loops are subsequently designed to eliminate the measured HF currents and compensate the scaling errors. The designed HF controller are independent from the main current and torque regulators, and thus, it can be used for initial scaling gain calibration or periodically used during normal machine operation. Different from general three phase machine systems, the generated HF current components only exist in z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> subspace, and thus, no extra torque ripples will be produced. Experimental results validate the proposed control algorithm.

Cooperative Control for Stochastic Multiagent Systems With Deferred Dynamic Constraints via a Novel Universal Barrier Function Approach.

This article investigates the cooperative control problem for stochastic multiagent systems (MASs) with dynamic constraints. A new universal barrier function is proposed, which is applicable to many systems with different types of constraint functions, even unconstrained systems. Several mapping functions are constructed to constrain the state variables directly without feasibility conditions, and the tracking control is achieved for stochastic MASs with deferred full-state constraints under the backstepping framework. In order to regulate the tracking error more precisely, the funnel error transformation is improved and the deferred funnel controller is developed by introducing a preassigned finite-time function. Based on the deferred funnel controller, the tracking error can be maintained within the predetermined funnel in the preassigned time. The convergence time can be defined according to the actual requirements, and it is independent of the design controller parameters and initial conditions. Finally, some simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults.

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.

A Junction Temperature Smoothing Control Method for SiC MOSFETs Based on the Gate Driving Signal Delay

Low frequency junction temperature fluctuation caused by changes in load current or ambient temperature can severely accelerate the aging development and reduce the operational lifetime of power switching devices. Therefore, junction temperature smoothing control (JTSC) is important for high reliability operation. Conventional JTSC methods require complex additional hardware. Moreover, they are invasive with compromising on the output power quality of the converter. To solve these problems, a novel JTSC method is proposed based on the adjustment of the gate driving signal delay ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> ). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , the relationship between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> and the power loss is analyzed. It is discovered that the power loss is positively linear to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , which is the basis for JTSC. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , a JTSC strategy is proposed based on the proportional-integral control. The gate driving signal delay can be adjusted adaptively according to the proposed control algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , the effectiveness of the proposed JTSC method is verified by experiments. The junction temperature fluctuation can be eliminated by 50 % in various load conditions. Compared with conventional JTSC methods, the proposed method has almost no compromising on the output power quality of the converter, and it requires no additional hardware with low cost.

Universal Control for Both Rendezvous and Tracking of Multiple Nonholonomic Unicycles

This paper studies the universal control for multiple nonholonomic unicycles, i.e., finding a control law that allows for both rendezvous and tracking uses. The reduced-order approach and optimal technique are applied to design an exponential observer that estimates the leader's states. Using the observer signal, we convert the pose errors into the form of nonholonomic integrators and derive a local tracking control law. The obtained control law guarantees exponential convergence of tracking errors and maintains the solution trajectory of converted states in an invariant set. More importantly, the original pose error between each unicycle and the leader is proven exponentially convergent to zero, whether or not the leader satisfies persistency of excitation (PE) conditions. The extension of the proposed control algorithm to leader-following formation control is also illustrated. Numerical simulations validate the control design.

Model predictive control of a dual fluidized bed gasification plant

Dual fluidized bed (DFB) gasification is a promising method for producing valuable gaseous energy carriers from biogenic feedstocks as a substitute for fossil fuels. State-of-the-art DFB gasification plants mainly rely on manual operation or single-input single-output control loops, and scientific contributions only exist for controlling individual process variables. This leaves a research gap in terms of comprehensive control strategies for DFB gasification. To address this gap, we propose a multivariate control strategy that focuses on crucial process variables, such as product gas quantity, gasification temperature, and bed material circulation rate. Our approach utilizes model predictive control (MPC), which enables effective process control while explicitly considering process constraints. A simulation study is given demonstrating how different MPC parametrizations influence the behavior of the closed-loop system. Experimental results from a 100kW pilot plant at TU Wien demonstrate the successful control achieved by the proposed control algorithm.

Adaptive prescribed performance output feedback control for full-state-constrained DC motors subject to uncertainties and input saturation

An adaptive output feedback controller with prescribed performance is performed for full-state-constrained DC motors with uncertainties (including parametric uncertainties and disturbances). A distinguishing feature is that when only partial states are measurable, not only parametric uncertainties and disturbances are addressed but also the quantitative analysis of control error and state constraints is realized. For this purpose, an extended-state observer is constructed to estimate disturbances and unmeasurable states, while adaptive control is employed to approximate unknown parameters. A new prescribed performance-barrier Lyapunov function is established to maintain the control error and all system states within specified boundaries. Then, taking saturation compensation into account, the proposed control algorithm is designed through the backstepping technique to ensure that all signals in the closed-loop system are bounded. Finally, several experimental results demonstrate the effectiveness of the proposed algorithm.

Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Coalitional Distributed Model Predictive Control Strategy with Switching Topologies for Multi-Agent Systems

Controlling multi-agent systems (MASs) has attracted increased interest within the control community. Since the control challenge consists of the fact that each agent has limited local capabilities, our adopted solution is tailored so that a group of such entities works together and shares resources and information to fulfill a given task. In this work, we propose a coalitional control solution using the distributed model predictive control (DMPC) framework, suitable for a multi-agent system. The methodology has a switching mechanism that selects the best communication topology for the overall system. The proposed control algorithm was validated in simulation using a homogeneous vehicle platooning application with longitudinal dynamics. The available communication topologies were specifically tailored taking into account the information flow between adjacent vehicles. The obtained results show that when the platoon’s string stability is risked, the algorithm switches between different communication topologies. The resulting coalitions between vehicles ensure an increase in the overall stability of the entire system and prove the efficacy of our proposed methodology.

Development of modified P&O algorithm based simple controller for constant voltage and frequency operation of three-phase IIG for micro-hydro application

This paper introduces Modified Perturb and Observe (MP&O) based MPPT algorithm for constant frequency and voltage operation of a three-phase Isolated Induction Generator (IIG) coupled to an ungoverned hydro turbine. The generator is operated at maximum output power point corresponding to the excitation capacitance connected across the Point of Common Coupling (PCC) of the IIG. The output power of the generator is always maintained constant. With a fixed capacitor bank connected across the stator terminals, the IIG simultaneously supplies power to both ac load (main load) and controlled dc load (dump load). This modified MPPT algorithm not only tracks the maximum output power point, but also diverts power from main load to dump load and vice-versa as per requirement to maintain the voltage and frequency at constant value. The electronic circuit is realized using an uncontrolled rectifier-chopper (diode-IGBT) combination connected across the IIG terminals supplying the controlled dc load. The IIG has been modeled based on stationary reference frame d-q axes theory. The proposed control algorithm has been experimentally implemented on a three-phase, 415 V, 2.2 kW induction machine using dSPACE 1104 platform and the effectiveness of the algorithm has been tested for various transient conditions, such as, sudden ac load application as well as removal.

Control of the PVTOL with Strong Input Coupling

This paper studies the modeling and control of a Planar Vertical Take-Off and Landing (PVTOL) with steerable thruster. A longitudinal model is obtained using Newton’s second law for the PVTOL which evolves in 3 degrees of freedom and has two control inputs. The aerial vehicle is driven by steerable propulsion controlling its evolution in the vertical plane through the thrust and torque control inputs, which drive the vehicle body and generate a rotation. The obtained model is nonlinear and is significantly different with respect to the well-known PVTOL. For this reason, different control algorithms are presented, and the closed-loop behavior is studied for each of them. The proposed control strategies perform a stationary flight at a desired altitude and control the position of the aerial vehicle. The performance of the proposed control algorithms is tested in numerical simulations.

Finite-Time Robust Path-Following Control of Perturbed Autonomous Ground Vehicles Using a Novel Self-Tuning Nonsingular Fast Terminal Sliding Manifold

This work presents a finite-time robust path-following control scheme for perturbed autonomous ground vehicles. Specifically, a novel self-tuning nonsingular fast-terminal sliding manifold that further enhances the convergence rate and tracking accuracy is proposed. Then, uncertain dynamics and external disturbances are estimated by a high-gain disturbance observer to compensate for the designed control input. Successively, a super-twisting algorithm is incorporated into the final control law, significantly mitigating the chattering phenomenon of both the input control signal and the output trajectory. Furthermore, the global finite-time convergence and stability of the whole proposed control algorithm are proven by the Lyapunov theory. Finally, the efficacy of the proposed method is validated with comparisons in a numerical example. It obtains high control performance, reduced chattering, fast convergence rate, singularity avoidance, and robustness against uncertainties.

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

Adaptive integral sliding mode fault‐tolerant control for diesel engine air path system via disturbance observer

AbstractThis paper proposes a fault‐tolerant control strategy for the air path system of a turbocharged diesel engine, considering the simultaneous presence of matched and mismatched external disturbances, additive and loss of effectiveness fault modes in the EGR and VGT subsystems. Firstly, a disturbance observer based on theory is designed for estimating additive faults and external disturbances, and the upper bound of observation error is obtained. Based on the observation information, an integral sliding mode surface is designed, and the effectiveness of the sliding mode surface is analyzed. For the loss of effectiveness faults in the actuators, a novel adaptive update sliding mode controller is designed. The proposed control algorithm can get the fault information of the system and achieve fault‐tolerant control of through controller reconstruction. Ultimately, the effectiveness of the proposed method is validated through simulations and comparative analysis.

A Multifilters Approach to Adaptive Event-Triggered Control of Uncertain Nonlinear Systems With Global Output Constraint.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

Fault‐tolerant adaptive tracking control for spacecraft attitude with asymptotic convergence

AbstractIn this paper, we present a novel spacecraft attitude adaptive tracking control strategy that addresses the challenges posed by unknown inertia parameters, bounded unstructured uncertainties, and various actuator faults. To mitigate the adverse effects of these multiple uncertainties, our proposed control algorithm utilizes a simple tracking‐error feedback and compensation term, which only requires two adaptive parameters. This approach ensures asymptotic tracking convergence by employing an infinitely integrable inequality that incorporates saturation functions. As a result, the complexity of controller design and computational burden are significantly reduced. Finally, several numerical simulations are conducted to validate the feasibility and superiority of our proposed controller.