Reference Command Research Articles

Robust reference governor for input-constrained model predictive control to enforce state constraints at low computational cost

In the setting of discrete-time linear systems with unmeasured set-bounded disturbances, this paper proposes a control scheme that combines a disturbance free input-constrained Model Predictive Control (uMPC) policy and a reference governor (RG) which modifies the reference command to enforce constraints that are not enforced by the uMPC policy. Such a scheme, referred to as the robust RGMPC, can handle (possibly nonlinear) state and input constraints and only requires solving an optimisation problem for MPC with polytopic input constraints, for which fast algorithms exist. Conditions are given which ensure recursive feasibility of the robust RGMPC scheme, finite-time convergence of the modified reference command to the desired reference command and asymptotic convergence of the state to an invariant neighbourhood of the associated set-point. Simulation results for a constrained spacecraft rendezvous manoeuvre demonstrate that the rRGMPC scheme has lower average computational time than several robust state and input-constrained MPC controllers.

Omni-directional orientation analysis of a triplet-stator spherical piezoelectric motor system

ABSTRACT Spherical piezoelectric motors (SPMs) represent a promising actuator technology applicable to the development of motion control systems with multiple degrees of freedom. However, few SPMs have made it successfully to market, as an effective and systematic rotor orientation analysis and manipulation technique has yet to be developed. This study thus developed a novel triplet-stator SPM design that integrates electrode configuration to excite lateral vibrations and offers two fundamental rotor orientations for each set of piezoelectric plates by changing the contact position between the rotor and stator. To accomplish omnidirectional orientations for the proposed SPM system, we present a systematic orientation analysis based on spherical coordinates. We further apply the concept of vector composition to derive new composite orientations by adjusting the magnitude ratio of the provided torques between three stators. An orientation angle representation accounting for the rotor dynamics is used to validate the correctness of the synthesized orientation vectors both in simulation and experiments. The orientation analysis results in a three-dimensional sphere containing eight vector spaces corresponding to eight stator actuation modes, in which an arbitrary rotor orientation reference command can be generated under the designed actuation modes. Experimental results were well correlated with simulations, thereby verifying the effectiveness of the proposed SPM system design and orientation analysis.

Integrated entry guidance with no-fly zone constraint using reinforcement learning and predictor-corrector technique

This paper presents an integrated entry guidance law for hypersonic glide vehicles with no-fly zone constraint. Existing methods that employ predictor-corrector technique and lateral guidance logic for both guidance and avoidance, may have limitations in response time and maneuverability when facing sudden threats, because the guidance cycle is limited by computational efficiency and the bank angle magnitude cannot be adjusted according to the urgency of the avoidance. To overcome these challenges, the proposed method divides the entry process into safe flight stages and no-fly zone avoidance stages, and introduces reinforcement learning to develop an intelligent avoidance strategy for the latter. This division reduces the complexity of the learning problem by restricting the state space and increases the applicability in the presence of multiple no-fly zones. The trained avoidance strategy can directly output continuous bank angle command through a single forward calculation, considering both guidance and avoidance requirements. This enables the full utilization of the vehicle’s maneuverability and supports a high command update frequency to effectively handle threats. Additionally, a network trained via supervised learning is employed to generate reference commands, accelerating the training convergence of reinforcement learning. Simulation results demonstrate the effectiveness of the proposed guidance law, highlighting its high computational efficiency, command stability, and robustness. Importantly, the approach offers convenience in extending to multiple no-fly zones and accommodating vast initial state spaces.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

This paper investigates the adaptive control problem for the strict-feedback nonlinear time-varying system (NTVS) with unknown control coefficients and model uncertainties. To address the problem that the neural network (NN) can only achieve local approximation of unknown nonlinear functions, a novel NN control scheme is proposed. The scheme consists of a NN controller that operates inside the approximation domain and a robust controller that operates outside the approximation domain, and a novel switching function is designed to enable the two controllers to switch smoothly in the vicinity of the approximation domain to ensure that all signals of the closed-loop system are globally uniformly ultimately bounded. To address the problem that unknown time-varying control coefficients are difficult to handle, an adaptive fault-tolerant control scheme is proposed in combination with the congelation of variables method. The scheme employs classical adaptive control to adapt to the variation of unknown control coefficients and robust control to eliminate the time-varying disturbance terms, and introduces a positive integrable time-varying function to achieve asymptotic tracking of an arbitrary reference signal. The combination of these two schemes constitutes a global adaptive neural network control (GANNC) method for the NTVS with unknown control coefficients and model uncertainties. Finally, an adaptive attitude fault-tolerant controller for launch vehicles is designed by using the GANNC method, which can realize the accurate tracking of the attitude control system to the reference command under normal status, strong disturbances and actuator failures, and comparative experiments prove the effectiveness and superiority of the controller.

Modeling, simulation and maneuvering control of a generic submarine

This work introduces two multi-level control strategies to address the problem of guidance and control of underwater vehicles. An outer-loop path-following algorithm and an outer-loop trajectory tracking algorithm are presented. Both outer-loop algorithms provide reference commands that enable the generic submarine to adhere to a three-dimensional path, and both use an inner-loop adaptive controller to determine the required actuation commands. Further, a reduced order model of a generic submarine is presented. Computational fluid dynamics (CFD) results are used to create and validate a model that includes depth dependence and the effect of waves on the craft. Results from the reduced order model for each control strategy are compared.

Research on high-bandwidth linear active disturbance rejection control method for variable speed turboshaft engine

Abstract The wide flight range and high torsional vibration frequency of high-speed helicopters impose stricter criteria for the high-bandwidth control of turboshaft engines. Consequently, research is underway to implement a high-bandwidth control method for turboshaft engines using the linear active disturbance rejection control (LADRC) theory. Initially, the LADRC is designed based on the mathematical model of the integrated helicopter/engine system. To address the challenge of maintaining control quality with varying speed reference commands for the power turbine, an improved LADRC method with tracking differentiators (TD) is developed. Numerical simulations comparing the control effectiveness of LADRC with TD to cascade PID and conventional LADRC methods are conducted. The results demonstrate that the improved LADRC gains have a wider tuning range than the LADRC controller, and the power turbine speed tracking effect of LADRC with TD is optimal. It is more conducive to accomplish high-bandwidth control of turboshaft engine with variable rotational speed.

Implementation of a Robust PMAE Algorithm to Limit Large Gain Variation in a Fuel-Cell Integrated EV Charging Architecture

This paper proposes an adaptive error convergence technique to improve the charging dynamics of an electric vehicle (EV) charging system in a grid-integrated proton-exchange membrane fuel cell (PEMFC) architecture. The fuel cell is most often characterized by its slow dynamics during multiple load transients and preferably operated as an auxiliary energy source in EV charging architecture. The delayed response affects the overall charging dynamics with further controller phase lag and non-linear system parameters to induce a significant convergence error during reference command tracking. To effectively control multi-source integrated charging dynamics in an EV charging architecture, a projection-modified adaptive estimation (PMAE) algorithm is proposed in this paper. The control algorithm establishes a variable gain adaptation within a bounded error contour and updates the controller gains based on the tracking error dynamics gradient. The fuel cell, supporting other energy resources, provides a stronger charging resiliency with a duty-modified control technique at the EV charging station. To validate the effectiveness of the controller implementation, a grid-integrated multi-objective EV charging prototype is designed and developed for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) at variable operating conditions.

Finite-time robust speed control of synchronous reluctance motor using disturbance rejection sliding mode control with advanced reaching law.

In recent years, there has been a significant focus on synchronous reluctance motors (SynRM) owing to their impressive efficiency and absence of magnetic material. Although the SynRM shows great potential for use in electric vehicles, its widespread adoption is limited by unmodeled dynamics and external disturbances. Moreover, the uncertainty factor significantly restricts SynRM's peak efficiency and superior control performance, leading to an unjustifiable current loop reference command. To address these issues, this work presents various new research contributions which focus on the robust control of SynRM to optimize performance through the novel reaching law-based sliding mode control. Initially, a novel advanced sliding mode control reaching law (ASMCRL) with adaptive gain is proposed, to enhance the acceleration of the system state reaching the sliding surface. After that, an extended state observer (ESO) is designed to estimate and compensate for the overall disturbances of the system. Finally, the ASMCRL and ESO are integrated to design two nonlinear controllers namely, the disturbance-rejection sliding mode controller (DRSMC) and the disturbance-rejection sliding mode speed regulator (DRSMSR) for SynRM. The proposed DRSMSR eliminates the steady-state error and eradicates inherent chattering in DRSMC. Moreover, this yields a system trajectory that converges to a predetermined proximity of the sliding surface, irrespective of any lumped disturbances. The steady-state error of DRSMSR is less as compared to DRSMC. Furthermore, the speed response of this technique is 22.62% faster as compared to the state-of-the-art finite-time adaptive terminal sliding mode control. Additionally, the asymptotic stability of the proposed system is validated using Lyapunov's theorem. Thus the experimental results demonstrate the effectiveness and robustness of the proposed approach.

Asymmetric BLOS constraint-involved intelligent integrated guidance and control approach for flight vehicle equipped with strapdown seeker

The paper investigates the integrated guidance and control (IGC) issue for flight vehicle equipped with strapdown seeker. Distinguished from the routine flight vehicle in literatures, the equipped strapdown seeker is fixedly connected with the vehicle body and thus asymmetric body line of sight (BLOS) constraint is imposed, which makes the IGC design more challenging. Focusing on the point, an intelligent integrated guidance and control approach is proposed by virtue of two specific mechanisms. The improved explicit reference governor technique is adopted to generate auxiliary reference command to ensure asymmetric BLOS limitation not to be violated. On the other hand, the reinforcement learning-based adaptive dynamic programming algorithm is further integrated to guarantee the fine tracking of the auxiliary reference command. Consequently, the satisfactory miss distance is achieved despite of the asymmetric BLOS constraint and unmatched uncertainties, and the stability of the closed-loop system is analyzed. The numerical simulation results demonstrate the effectiveness of the proposed method.

H∞ robust control for underwater supercavitating vehicle with time delay

According to Logvinovich cavity dependent expansion principle, a cavity developed by cavitator has memory effect, which arises cavity deformation at aft part of a supercavitating vehicle and leads time delay of travel state. This delay affects the dynamic behavior through changing hydrodynamic and moment on the control surface, and even induces instability. This paper focuses on the time delay effect of the underwater supercavitating vehicle, and a simplified dynamic model of underwater supercavitating vehicle with delay was established. First, a planing force was simplified in straight level state, then a simplified model of supercavitating vehicle with delay in longitude plane was obtained. Taking depth, pitch angle, vertical speed and pitch rate as state variant, H ∞ state feedback controller was designed by linear matrix inequality (LMI) and Lyapunov stable theory was exploited to prove the asymptotic stability of the system. Simulation results show that, the controlled system is effectiveness for both simplified model and non-simplified model and it can track reference command signal.

KENDALI TORKA PREDIKTIF DENGAN PREDIKSI DUA LANGKAH MOTOR INDUKSI YANG DICATU OLEH SEBUAH INVERTER TIGA FASE

The predictive torque control (PTC) technique of electric motor drive is based on the principle that the determined optimum voltage vector will be applied to the motor in the next cycle. If a three-phase inverter is used, this inverter can only realize eight different switching states. One of the eight switching states is used as the optimum inverter switching state for the next cycle. PTC with one-step prediction cannot obtain an experimental motor speed control, because in the real-time implementation, there is a single sampling delay between input and optimal actuation due to a large number of real time calculations. The delay time problem could be solved by modifying the optimization steps by using the two-step prediction of PTC model. The experimental results show that the motor can follow the reference speed and command torque in the step response and variable speed experiments. In variable speed experiment, good speed and torque dynamics can be achieved by selecting the proportional gain value that corresponds to the motor speed

Speed-command-independent parameters self-tuning and mismatch compensation for servo speed control

Usually the servo system using the traditional generalized predictive control is limited by the reference command, the expression of which is only a step signal or a ramp signal. Meanwhile, during the identification convergence, the model mismatch composed of the modeling errors and the nonlinear disturbance, often leads to the obvious speed spike and even damping oscillation. To solve the above problems, a speed-command-independent 2 degree-of-freedom (2DOF) N-type PI (NPI) controller based on an improved generalized predictive control (IGPC) is designed, as well as an extended sliding-mode compensator (ESMC) is proposed in this paper. First, to enhance the steady-state performance for tracking speed command, a 2DOF NPI speed controller is utilized to track the arbitrary-order polynomial command. Then, based on the forward N-order difference operator, an extended prediction model, the output of which only contains the speed error, is established to free the IGPC from the speed command. Next, the optimal control law is obtained by the rolling optimization of a reconstructed cost function, and further is simplified to match with the 2DOF NPI controller online. Finally, the ESMC is designed to effectively weaken the speed spike caused by the model mismatch, and a special positive definite function is utilized to ensure its stability. Simulation and experimental results verify the advantages of the proposed scheme.

Evaluation of Start-Up Performance of Induction Motor Speed Sensorless Vector Control Using Adaptive Flux Observer Considering Inverter Voltage Error

Speed sensorless vector control for induction motors using an adaptive flux observer makes it difficult to output high and stable torque at a standstill condition when the output voltage error mainly due to dead-time of the inverter. This voltage error leads to a speed estimation error. Consequently, axis misalignment occurs, and a torque value that tracks the reference command value cannot be output. A method has been proposed to compensate for this voltage error by using a current command value; however, the compensation accuracy is limited because of the forward voltage drop and nonlinear characteristics of power switching devices. This study applies the conventional dead-time compensation to the model reference adaptive system, and the effect of varying the amount of compensation, instead of investigating the true dead-time length, on the start-up is evaluated using a 2.2 kW experimental system and through dynamic analysis.

H∞-Based Control Design for Grid-Forming Inverters With Enhanced Damping and Virtual Inertia

Grid-forming inverters (GFMIs) are identified as an important asset for achieving renewable energy-rich power grids. GFMIs are attracting significant attention due to their superior characteristics over grid-following inverters in both grid-connected (GC) and standalone (SA) scenarios. In this article, a second-order discrete-time controller is proposed to achieve a well-damped step response for power reference commands and improved virtual inertia provision capability. In this article, a control design method based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty} $ </tex-math></inline-formula> is proposed, which is based on the frequency response of the system, to tune the proposed controller. The proposed control design presents a methodical process to specify the desired performance indices through frequency-domain constraints. The performance of the controller is thoroughly validated analytically and through simulation results. The superior performance of the proposed controller over the virtual synchronous generator controller in terms of tracking performance and virtual inertia provision capability is verified through experimental results.

Implementation and performance comparison of harmonic mitigation schemes for three-phase grid-connected voltage-source converter under grid voltage distortion: HiL and experimental validation

This paper presents an implementation methodology for a current harmonic compensation technique of three-phase grid-connected voltage-source converter (VSC), which was applied under the grid voltage distortion and frequency variation. The proportional–integral plus multi-resonant controllers (PIMR) were used to mitigate the high-order harmonic current. The harmonic compensators were used in the synchronous reference frame at harmonic orders 6th and 12th for suppression of the grid voltage harmonic orders 5th, 7th, 11th, and 13th with a total harmonic distortion of 4.69%. The PIMR controller was transformed to the discrete-time domain and implemented on a TMS320F28379D digital signal processor. The PIMR controller was compared with the conventional multiple synchronous reference frame (PIMSR) controller. The two control schemes were validated with a hardware-in-the-loop (HiL) and with a 5 kW three-phase LCL-filtered grid-connected VSC. The PIMR and PIMSR controllers exhibited excellent harmonic rejection on the grid current with a total harmonic distortion close to 1%, which complied with the IEEE 1547 standard. The grid current can track the reference command with a fast dynamic response. Moreover, the PIMR controller consumed a computational time of 1.625μs, 35% of the PIMSR controller.

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN’s online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.

Resilient Composite Learning Neuro-Adaptive Integrated Guidance and Control for reusable Launch Vehicle

An integrated guidance and control system for tracking a reference flight path trajectory of a reusable launch vehicle in the presence of model uncertainties, external disturbances, and measurement noises is proposed. To achieve this goal, a properly-trained feedforward neural network to estimate uncertainties in conjunction with a disturbance observer, which estimates external disturbances and the estimation error made by the neural network, is implemented in both the guidance and control loops. In addition, a state observer is designed whose state estimation error is included (along with the tracking error) in the learning algorithm of the neural network and the disturbance observer; such learning is called composite learning. The combined neural network and disturbance observer (employed in both the guidance and control loops) can consistently compensate for complex uncertain terms in the dynamic model of a reusable launch vehicle. More specifically, the introduced design results in an asymptotic tracking of the reference command, thereby providing a resilient flight control system. Extensive simulations are performed to show the effectiveness and robustness of the proposed integrated guidance and control system for trajectory tracking in the presence of uncertainties, aerodynamic disturbances, and measurement noises.

Balancing Unicycle Travelling on an Inclined Surface

A self-balancing unicycle robot is equipped with roll stabilizing unit and a wheel which is in charge of simultaneous unicycle frame upright holding and path tracking. Linear quadratic regulators (LQR) are designed for the roll, pitch and path tracking to make the vehicle successfully perform its desired operations. Even though control loops in general and LQR in specific subdue disturbances such as road slope, still some extra strong measures may be required. The road tilt alters the device dynamic and exerts forces influencing its behavior against reference commands. In this respect, gain scheduling LQR is designed and its impact on the system performance with regard to the conventionally designed LQR is discussed. Simulation results are used to validate the propositions.

Pressurized Water Reactor Core Power Control Using BAS-RBF-PID Approach During Transient Operation

A pressurized water reactor (PWR) is a system of several integrated components such as the core, steam generator, hot leg, cold leg, and plenums. The subsystems consist of critical parameters and malfunctions that cause potential accidents. Therefore, a PWR requires a control system for safe and stable operation over its lifetime. In this study, the state-space model of the PWR core is established and validated with published work. Then, a beetle antenna search (BAS) algorithm–optimized radial basis function (RBF) neural network proportional-integral-derivative (PID) control (BAS-RBF-PID) strategy is proposed to regulate the core power. The BAS-RBF-PID control approach computes the control input to optimize the PWR core output power to track the reference command. The integral absolute error and integral time absolute error criterion functions are used to measure the control performance. The sensitivity of the control input on the PWR output is examined through the Jacobian, and the stability is analyzed by using the Lyapunov approach and Nichols chart. The simulation results verified that the PWR core output power chased the reference command smoothly as compared with the BAS-PID and PID strategies with good performance. This confirms that the control signal optimizes the core power effectively. This study gives the benefit to apply the BAS-RBF-PID algorithm in other nuclear engineering fields for control purposes.

Safety Control Design With Flight Envelope Protection and Reference Command Generation

When confronting different levels of aircraft actuator faults, a safety control system without considering the postfault performance degradation raises the probability of flight incidents. This article presents a safety control scheme against actuator malfunctions, on the basis of flight envelope protection and reference command regeneration. First, with respect to a high-fidelity aircraft model, a flight envelope estimation method is developed to respect to the state limits of postfault aircraft (i.e., trim and maneuvering envelopes). Simultaneously, in an attempt to alleviate the burden of the damaged actuators, a degraded reference command is online regenerated, thus, maintaining the closed-loop aircraft within the safe flight envelope. Second, a sliding mode control scheme with the integration of a prescribed performance function is proposed to track the degraded reference command. When comparing to the existing control schemes, the gap between safety control and reference command regeneration is bridged to ameliorate practical flight safety. Finally, comparative simulation studies, based on the high-fidelity Boeing 747 longitudinal model, are conducted to examine the effectiveness of the proposed scheme.