Nominal Tracking Performance Research Articles

Enhanced dynamic performance in DC–DC converter‐PMDC motor combination through an intelligent non‐linear adaptive control scheme

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

Deep Reinforcement Learning and Simultaneous Stabilization-Based Flight Controller for Nano Aerial Vehicle

The plants of nano aerial vehicles (NAVs) are inherently unstable. Hence, a NAV needs a flight controller to accomplish a mission. Furthermore, the sensing and computational capabilities of NAV's autopilot hardware are limited. Hence, the implementation of the full state feedback controllers with gain scheduling is difficult. This paper proposes a flight controller scheme that consists of two parts: a Simultaneously Stabilizing Output Feedback Linear (SSOFL) controller and a Proximal Policy Optimization (PPO) deep reinforcement learning agent, which is connected in parallel to the SSOFL controller. In this scheme, the single SSOFL controller provides stabilization and nominal tracking performance to the NAV throughout its flight envelope by accomplishing simultaneous stabilization (SS). Additionally, the PPO agent is trained using the closed-loop (CL) nonlinear plant with this SSOFL controller to enhance the tracking performance. The effectiveness of the proposed flight controller scheme is verified using the six-degree-of-freedom nonlinear simulations of the fixed-wing nano aerial vehicle.

Design and Real-Time Implementation of Cascaded Model Reference Adaptive Controllers for a Three-Phase Grid-Connected PV System

This article proposes the development of a model reference adaptive controller (MRAC) for grid integration of a single-stage three-phase grid-connected photovoltaic system (GCPVS). The performance of a GCPVS is influenced by uncertainties such as measurement noise, solar irradiance, photovoltaic (PV) operating point, aging of the dc-link capacitor, and variation in the filter impedance. In order to achieve the desired tracking performance under both nominal and parametric uncertainties, MRACs are designed for both the outer PV voltage control loop and the inner grid current control loop. The proposed control scheme comprises a reference model and a Lyapunov-based parameter adaptation scheme, which provides the nominal tracking performance despite uncertainties in the GCPVS dynamics. As a result, improved performance of the GCPVS is achieved in terms of low dc-link voltage ripple, superior power extraction, and improved grid power quality. The performance of the proposed adaptive controller is verified by pursuing simulation in MATLAB/Simulink followed by experimentation on a 2.5-kW GCPVS. The control algorithm is implemented in real time on the SPARTAN-6 FPGA scheme. The efficiency of maximum power point tracking (MPPT) is obtained as 99.92%. The total harmonic distortion of grid currents and voltages at the point of common coupling is found to be less than 3%, which is well within the limit specified by the IEEE-1547 standard.

Adaptive Regulation to Nominal Response for Uncertain Mechanical Systems and Its Application to Optical Disk Drive

In this paper, we present an add-on type robust controller for uncertain mechanical systems with a sinusoidal disturbance of an unknown frequency. The main purpose is to recover (predefined) nominal tracking performance in an “asymptotic” sense, that is, the actual output asymptotically converges to the nominal trajectory, as well as the maximum difference between them is maintained small enough during the operation. As a key component of the proposed controller, a disturbance observer that contains an internal model is proposed. Especially, the parameters of the internal model are updated in an “adaptive” fashion in order to cope with the unknown frequency of the disturbance, for which a frequency identifier is employed in the controller design. The nominal performance recovery of the proposed controller is analyzed in the sense of Lyapunov and singular perturbation theory. Both simulation and experimental results point out that the proposed control scheme can serve as a robust track following controller for uncertain optical disk drive systems where eccentricity of disk causes a sinusoidal disturbance.

Recovering Nominal Tracking Performance in an Asymptotic Sense for Uncertain Linear Systems

In this paper, we consider the problem of recovering a (predefined) nominal output trajectory in the presence of model uncertainty and external disturbance. In particular, whereas the nominal perfo...

Experimental Study on Optimal Tracking Control of a Micro Ground Vehicle

This paper investigates the application of optimal control to a micro ground vehicle (MGV) experimentally. The model predictive control (MPC) technique is used for the overall tracking controller during the maneuver. The reference trajectory for MPC is preliminarily obtained by numerical computation of the optimal control problem, which is prescribed as a minimum-time maneuver. The results provide nominal tracking performance and validate the feasibility of the approach.

Design and Implementation of a Nonlinear PI Predictive Controller for a Grid-Tied Photovoltaic Inverter

This paper presents the design, implementation, and performance testing of a nonlinear proportional-integral (PI) predictive controller for a grid-tied inverter used in photovoltaic systems. A conventional cascade structure is adopted to design the proposed controller, where the outer loop is used to regulate the dc-link voltage, and the inner loop is designed as a current controller for adjusting the active and reactive powers injected into the grid. For each loop, the controller is derived based on combining a continuous-time nonlinear model predictive control and nonlinear disturbance observer techniques. It turns out that the composite controller reduces to a nonlinear PI controller with a predictive term that plays an important role in improving tracking performance. The salient feature of the proposed approach is its ability to approximately preserve the nominal tracking performance during the startup phase. Both simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach in terms of nominal performance recovery, disturbance rejection, and current control.

Optimal Tracking Control of a Micro Ground Vehicle

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/07.jpg"" width=""300"" /> Coordinate system of MGV</div>This paper investigates the application of optimal micro ground vehicle (MGV) control involving overall tracking by model-predictive control (MPC) during a minimum-time maneuver. The MPC’s reference trajectory is obtained beforehand by numerically calculating an optimal control problem described as a minimum-time maneuver. Results provide nominal tracking performance and confirm the feasibility of our approach.

3402 最適軌道に対するスキッドステア式小型無人車両の追従制御

This paper investigates an optimal control maneuver of a skid-steer small unmanned ground vehicle (UGV). The optimal control problem is prescribed as a minimum-time maneuver of the UGV, and it is solved using pseudospectral (PS) optimal control theory. The optimal trajectory which includes the pivot-turn maneuver is provided to the real UGV system as reference input commands, and the vehicle tracks it by the gain-scheduled feedback law on the vehicle position error. The results provide nominal tracking performance and validate the feasibility of the approach.

Preview and Feedforward in Model Predictive Control: Conceptual and Design Issues

AbstractModel Predictive Control has been, and continues to be, one of the major success stories of advanced control in industry. This is a result of several factors including its ability to deal with constraints. Early implementations were concentrated in the process industries. These implementations were characterised by several distinctive features including long sample periods and constant set-points. However, new techniques and faster sampling rates have opened the door to new classes of applications where neither of these earlier features apply. In this paper we re-examine the question of reference tracking in MPC and formulate a novel strategy (termed PF-MPC) aimed at problems where the reference is not constant. The strategy combines both preview and feedforward of the reference signal, with which we seek to improve nominal MPC tracking performance. This preliminary study stands as the conceptual basis for a future robust output-feedback MPC strategy focused on improving tracking of time-varying references.

A High Gain Integral Dynamic Compensation Method for Nonlinear Missile Control

AbstractA high gain integral dynamic compensation method for a missile pitch motion autopilot design is proposed. The external disturbances and model mismatch are lumped into an unknown input term, and then a high gain integral observer is constructed for the estimation of this term. Through the singular perturbation theory, it is concluded that with the control direction known and the integral gain high enough, the observer output can converge to the unknown input fast enough. The Lyapunov analysis also shows the bounded stability of the closed loop system. Numerical simulation results demonstrate this simple method improves the nominal tracking performance and robustness of dynamic inversion controller against unmodeled dynamics and external disturbances.

Plug-In Robust Compensator for a 3 DOF Piezoelectric Nanorobotic Positioner

In current AFM-based nanomanipulation systems, the commercial position closed-loop controller for piezoelectric nanopositioning stages are implemented with success in a wide range of industrial applications. Even if these controllers operate with satisfactory nominal tracking performance, considerable attention has been focused on appropriate control strategies to compensate hysteresis, nonlinearities, drift and creep for high bandwidths and large scanning regimes. As these closed-loop controllers are very cost-effective, a special interest in robust plug-in compensators seems to be a solution. We proposed in this paper a robust plug-in compensator using the H∞ loop-shaping techniques which can be plugged into the existing controller without affecting the already satisfactory nominal tracking performance of the existing closed-loop system. Dynamic modeling, identification and robust control of a 3 d.o.f. piezoelectric nanorobotic positioner are presented in this paper in order to improve the nanorobot performance under plant parameter variations and in the presence of external disturbances. Simulation and experimental results are given to validate the proposed plug-in robust compensator in the case of a nanorobotic manipulation task.

Design and analysis of a plug-in robust compensator: an application to indirect-field-oriented-control induction machine drives

It is well known that the system performance for an indirect-field-oriented-control induction motor drive degrades under the variation of rotor resistance and in the presence of external load torque. In this paper, a plug-in robust compensator for speed and position control enhancement of an indirect-field-oriented-control induction machine drive is developed. In the case where a controller for the induction machine already exists or is in operation with satisfactory nominal tracking performance, this plug-in compensator, designed using the H/sub /spl infin// loop-shaping techniques, can be plugged into the existing controller without affecting the already satisfactory nominal tracking performance of the existing closed-loop system but with the capability to improve the system performance under plant parameter variations and in the presence of external disturbances. Simulation and experimental results are given to validate the proposed plug-in robust compensator.

A PLUG-IN ROBUST COMPENSATOR FOR IFOC INDUCTION MACHINE DRIVES

It is well-known that the system performance for an indirect field-oriented control (IFOC) induction motor drive degrades under parameter variations and in the presence of external load torque. In this paper, a novel plug-in robust compensator for position control enhancement of an indirect field-oriented induction machine drive is developed. This plug-in compensator, designed using the ℋ∞ loop shaping techniques, can be plugged into the existing controller without affecting the already satisfactory nominal tracking performance of the existing closed loop system, but can improve the system performance under plant parameter variations and in the presence of external disturbances.

Modeling of Disturbed Flat System for Robust Control Design

This paper aims at presenting a modelization of the uncertainty on the linear model obtained from the so-called linearizing feedback of dynamic flat systems. After such a linearizing feedback is computed to ensure good nominal tracking performance, a compact set of models can be computed by taking account of both state space disturbances and parametric errors. A robust linear feedback is then designed in order to guarantee a specified level of performance. This representation of the compact set of models makes use of the Linear Fractional Transformation. so that the global robustness of tracking performance is assessed by μanalysis.

Robust Control of Flat Nonlinear System

This paper proposes a systematic approach for robust tracking control design of a class of nonlinear systems, refered to dynamic flat systems. First, a nonlinear dynamic feedback is designed to ensure nominal path tracking performance. Next, a compact set of models is elaborated in the vicinity of the nominal path, taking into account both state space disturbances and parametric uncertainties. Finally, a robust linear controller is designed which guarantees the path tracking perfomance objectives for the above so-obtained compact set. Simulations results obtained from speed control of a synchronous actuator demonstrate the potential of the proposed approach.

Robust Control of Uncertain Systems with Nominal Tracking Performance

This paper addresses the design of quadraucally stabilizing regulators for linear discrete-time systems subject to parameter uncertainty. The controller is designed in two steps. First, uncertainty is neglected and a controller ensuring nominal tracking performance is worked out. e.g. by means of H2 techniques. Then, based on an internal model scheme, a "robustifying" regulator is synthesized using the standard H∞ machinery. Provided that a feasible solution exists, quadratic stability is guaranteed without affecting the tracking performance in nominal conditions.