1,720,976 research outputs found
Model Predictive Control for a Linear Parameter Varying Model of an UAV
No full textThis paper presents a Model Predictive Control (MPC) based autopilot for a fixed-wing Unmanned Aircraft Vehicle (UAV) for meteorological data sampling tasks, named Aerosonde. Aerosonde missions are featured by predetermined operating conditions, allowing the design of ad-hoc controllers for each control task by using the future knowledge of the reference signals driving the aircraft during operations. To develop the controller, the nonlinear dynamics of the vehicle has been described by a Linear Parameter-Varying (LPV) model identified from the plant data by using a subspace identification technique. The LPV model is used to design a MPC to drive the UAV. Two different Linear Parameter-Varying MPC (MPCLPV) algorithms have been proposed by introducing the previewing technique in the controller due to the a priori knowledge of full reference signals. In the design of the inner Attitude Controller (AC), a future LPV scheduling parameters estimation policy has been introduced (PF −MPCLPV) for improving the control results of the standard Previewing MPCLPV (P-MPCLPV). Furthermore, an anticipative switching approach (PS −MPCS) has been considered for the altitude External Controller (EC) to improve the control performances of the standard previewing switching MPC (P-MPCS). Both PF −MPCLPV and PS −MPCS algorithms have been compared to the P-MPCLPV and P-MPCS baseline algorithms, showing the effectiveness of proposed methods
Optimal error governor for PID controllers
Full text linkError Governor (EG) deals with the problem of dynamically modifying the feedback error driving a controller having bounded control signals, for preventing controller or actuators saturation, avoiding integrator and/or slow dynamics windup and preserving the nominal linear controller behaviour. In this paper, an optimisation-based EG scheme is proposed for discrete-time Proportional-Integral-Derivative (PID) controllers driving Single-Input Single-Output (SISO) plants. The PID controller is considered in state-space form, and this formulation is used to pose the EG problem as a constrained quadratic programme (QP). Because the QP problem is subject to inequality constraints related to controller saturation, in order to use the proposed scheme in real-world applications, it should be necessary to consider appropriate algorithms for efficiently solving the optimisation problem. An efficient way to efficiently compute the solution of the EG problem is presented, reducing the computational effort required to solve the EG QP for using the proposed scheme in real control loops with high sampling rate. An analysis of control performance and computational burden is provided, comparing in simulation studies the optimal EG scheme performance with respect to control results provided by saturated PID with and without anti-windup action
Reference governor for switching converters with power factor correction
No full textIt is increasingly common to require performance improvement for power converters without modifying the original integrated controller, due to the multiple uses a single converter can serve. Such a goal can be met with the design of a regulator for the pre-compensated system, which serves as an add-on to enable new features and tighter performance targets. That is usually addressed with a Reference Governor (RG). We present here the design of an RG algorithm for a single-phase AC-DC boost converter, which is already controlled by an integrated digital Power Factor Correction (PFC) module. The converter operates in continuous conduction mode, and unitary power factor is achieved by a standard cascaded proportional-integral control with input voltage feedforward. We show that the non-invasive RG algorithm can improve the total harmonic distortion, power factor and voltage regulation. The proposed approach is based on model predictive control and subspace identification of the closed-loop prediction model. Numerical results on a high-fidelity simulator for power electronics confirm the advantages of the approach
Error governor for active fault tolerance in PID control of MIMO systems
No full textInput saturation and actuator faults are common issues in control system engineering. This paper proposes an Error Governor (EG) policy that dynamically manages the feedback error to enhance the tracking error in Multiple Input-Multiple Output (MIMO) closed-loop system controlled by Proportional-Integral-Derivative (PID) regulators. By integrating an Adaptive Kalman Filter (AKF) for optimal fault estimation with the EG scheme, the solution enables fault-tolerant control without requiring modifications to the baseline controller, which can be impractical or unsuitable in certain applications. Furthermore, the proposed control scheme completely avoids windup, thereby replacing conventional Anti-Windup (AW) schemes for PIDs, and is computationally cheap and easy to implement, needing only the same inputs as conventional AW algorithms and the actuator fault estimation. Simulation results on the MIMO model of the Zagi flying-wing aerial vehicle show that the EG reduces the tracking error in presence of actuator faults by (Formula presented.) in terms of Integral Absolute Error (IAE), outperforming conventional AW methods
Data-Driven Adaptive Torque Allocation for Electric Vehicles
No full textThis paper presents a preliminary study considering the design of an adaptive torque allocation policy for electric vehicles combining optimal control with data-driven techniques. The vehicle is equipped with four independent actuated wheels driven by electric motors. The policy aims to control the vehicle powertrain by allocating available power among motors to satisfy the driver control torque request and adjust torque allocated to different motors according to estimated wheels slip ratio change due to terrain varying conditions. A constrained optimal torque allocation algorithm is designed to distribute available power among wheels. In order to adjust the power allocation result, a data-driven adaptive policy is designed to adjust the control allocation parameters and the torque distribution reflecting wheel's operating conditions. The combination of torque allocation and data-driven adaptation policies permits the adjustment of the allocated power according to the wheel/road contact conditions. The algorithm has been tested and validated in simulation, showing the improvement given by the proposed approach compared with respect to the control system neglecting the data-driven adaptation of the torque allocation policy
A comparative study of driver torque demand prediction methods
Full text linkThe performances of energy management systems or electric vehicles and hybrid electric vehicles are highly dependent on the forecast of future driver torque/power request sequence that affects vehicle efficiency and economy. Since the behaviour of the driver is challenging to model/predict by first-principles models, modern artificial intelligence algorithms would represent feasible methods for approaching this problem in real-world automotive systems. This work provides a comparative study and analysis of performances of different data-driven torque prediction strategies. The studied and compared torque demand prediction techniques are exponentially varying model, linear regression, shallow and deep neural networks, and least square support vector machine-based approaches. The prediction performance and computational cost of these techniques are evaluated and reported, and the possibility of exploiting these techniques in real-world scenarios is also discussed
LS-SVM for LPV-ARX Identification: Efficient Online Update by Low-Rank Matrix Approximation
No full textLeast-Squares Support Vector Machine (LS-SVM) is a promising approach to data-driven identification of Linear Parameter-Varying (LPV) models. As for other data-driven methods, the performance of the LS-SVM model identification method is strictly related to data available off-line for training the algorithm. Further, this method does not consider the possibility to learn from on-line data, or at least this is not possible in a computationally efficient way. These aspects limit the possibility to exploit the features of the algorithm in real-world applications. This paper presents an online updating procedure of LPV-ARX (AutoRegressive with eXogenous input) model based on the Low-Rank (LR) matrix approximation aided to overcome these limits. The proposed method permits to improve the base of knowledge of the provided LS-SVM by introducing the possibility to learn from on-line data, neglecting to perform the time-expensive training phase, such that the proposed approach is suitable for on-line execution. In order to further limit the computational cost and the storage memory related to the on-line learning feature, the proposed approach permits to maintain the original algorithm requirements by introducing a forgetting method able to neglect less important data. The performance of the proposed solution has been evaluate considering as case study a Spark-Ignited (SI) aircraft engine system identification
Model Predictive Control for UAV Geofencing
No full textA geofence is a virtual perimeter representing the limits of a real-world operating area. The development of a control policy allowing to guarantee the safety of the Unmanned Aircraft Vehicles' (UAVs) users and stakeholders represents an important industrial world problem, yet studied in depth by the international scientific research community. In this paper, a geofencing system for UAVs based on the Model Predictive Control (MPC) paradigm is proposed. MPC permits to optimally drive dynamical systems explicitly imposing constraints on input and output by the prediction of the future evolution of the controlled plant. In this paper, an MPC policy is proposed to impose the geofencing area of a pre-compensated multi-rotor UAV. The proposed approach considers recomputing iteratively the controlled UAV speed constraints with respect to a prescribed maximum vehicle deceleration, in order to correctly impose the limits on vehicle speed and to stop the UAV on the borders of the prescribed geofence operating area. The proposed algorithm has been verified in simulation tests controlling a pre-compensated multi-rotor vehicle in a considered control scenario
Optimal Fault Tolerant Error Governor for PID Controllers
Full text linkThe Error Governor (EG) paradigm considers the issue of dynamically changing the error which drives a feedback controller featured by bounded control action magnitude to prevent the actuators’ saturation and to avoid the slow wind-up effects due to integrator or slow dynamics. Fault Tolerant (FT) policies are control methods permitting to mitigate the effect of faults occurring on driven actuators by modifying the structure of the controller which provides the reference signal for such actuators. In this paper, a FT policy based on an optimal EG approach is proposed. The policy, termed Fault-Tolerant Error Governor (FT-EG), permits to introduce a FT action in a closed-loop system driven by PID controllers neglecting changes in the controller structure and, further, the wind-up issue given by nominal actuator saturation. The FT-EG is based on the solution of a constrained optimization problem and a computationally efficient version of the algorithm is presented. An analysis of control performance and the computational burden is provided, comparing in simulation studies the optimal FT-EG scheme performance with respect to control results provided by the baseline EG policy and saturated PID controller in the fault-free and the faulty scenario
Sparse Approximation of LS-SVM for LPV-ARX Model Identification: Application to a Powertrain Subsystem
No full textLeast Squares Support Vector Machine (LS-SVM) has been recently applied to non-parametric identification of Linear Parameter Varying (LPV) systems, described by the AutoRegressive with eXogenous input (ARX). However, the online application of LPV-ARX system in the LS-SVM setting requires high computational time, related to the number of training data used to compute the coefficients of the identified model, limiting the possibility to use the method to real-time applications. In this paper, the authors propose the Low-Rank (LR) matrix approximation and a pruning based approach to compute a sparse solution. In particular, the pruning algorithm is considered to compute off-line a sparse solution of Lagrangian multipliers and then speed up the testing stage, whereas the LR matrix approximation allows to speed up the training stage. The proposed approach has been tested by identifying a subsystem of a vehicle powertrain model by the input/output data collected from the simulation model. The proposed approach has been compared with respect to the standard approach based on LS-SVM. The methods are tested on the considered real-world problem and the proposed approach permits to reduce the execution time of about 77% on average in the considered identification problem, corresponding to a degradation of the identification result less than 0.2% with respect to the standard solution
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