Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

With the development of marine technology, the research of unmanned surface vehicles (USVs) has received wide attention from the world. Designing effective collision avoidance algorithm in dynamic sea environment is necessary for the navigation of USVs. This paper describes a collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV. For small podded propulsion USV, a three-DOFs maneuvering model is established and the parameters of model are determined by the parameter identification method based on field experiments. About the COLREGs rules, the different maneuvering behaviors are summed up according to encounter situation and collision risk assessment. In the CAS, finite control set model predictive control is adopted, and COLREGs rules are taken as an evaluation standard in the quality function. Finally, the system is verified to avoid multiple dynamic obstacles and compliance vessel in the simulation and real environment, and the results of collision avoidance under four encounter situations are described in detail.

In this paper, a Lyaupnov-based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The eight voltage vectors of the two-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the maximum torque per ampere operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the eight vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

The Unmanned Surface Vehicles (USVs) have the characteristics of small volume, fast speed, and its application field is often in unknown complex ocean environment. Therefore, it is necessary to solve the fast collision avoidance problem of USV in unknown ocean environment. To solve this problem, a fast collision avoidance method based on velocity resolution is proposed. The motion constraint of the USV in the method is mainly based on the Lanxin USV of Dalian Maritime University. Finally, the safety and rapidity of the method are verified in the complex static and dynamic environments, which can provide a reference for the research of collision avoidance technology about USV.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

Finite control set model predictive control (FS-MPC) has been shown to be a very effective approach to control of PMSM drives. FS-MPC is a very flexible tool since it can evaluate an arbitrary loss function. However, design of the appropriate loss function for the problem can be a challenge especially when the design input is visible only on the long horizon. An example where this problem becomes apparent is the main propulsion drive of a traction vehicle fed from a dc catenary. Specifically, the catenary voltage is subject to short circuits, fast changes, harmonics and other disturbances which can vary in very wide range. Therefore, the drive is equipped with the trolley-wire input LC filter. The filter is almost undamped by design in order to achieve maximum efficiency and the control strategy needs to secure active damping of the filter to guarantee the drive stability. While it is possible to introduce active damping terms to the loss function, it is hard to predict its properties. In this paper, we consider decomposition of the problem to control of the LC filter and PMSM drive. We show that the resulting controllers can be elegantly combined using Bellman's principle of optimality. The resulting controller is easy to design and its performance is demonstrated in simulation and experiments on 10.7 kW drive.

A novel intelligent collision avoidance algorithm based on deep reinforcement learning approach for USV

In this paper, we introduce a small and low-cost unmanned surface vehicle (USV), the SMARTBoat 5, capable of removing harmful algal blooms (HABs), which are a rising environmental issue worldwide. The developed USV is a hovercraft type, operated by two propellers with duct fans; it is able to freely move even in shallow water and to approach shorelines. For eco-friendly, immediate, and safe control of algae, the USV is equipped with a novel water suction mechanism that enables it to actively collect algae without physical contact. In addition, it is equipped with a mesh net-based algae filter system that is easily disassembled and replaced. The USV system is supported by the Robot Operating System (ROS) for expandability and use in diverse applications. The performance of the proposed water suction mechanism and USV platform overall are validated through computational fluid simulation (CFD) and experiments in both lab and real environments.

This Paper evaluates the integration of fuel cell power system with the grid using finite control set model predictive controller. A mathematical model of the phosphoric acid fuel cell power system is designed. The grid and the fuel cell are modeled in PSCAD simulation environment. Direct power control technique for model predictive controller is used to control the converter. The steady state results are shown in the paper, Also, a single phase fault and three phase fault is applied at the grid side to study the effect of integrating the fuel cell with the grid. The simulation results show model predictive controller can effectively integrate fuel cell with the grid.

Three‐phase induction motor (IM) drives play an important role in the variable speed applications from fractional horsepower to multi Megawatt power; it controls and evaluates them to be an interesting subject. This study presents a novel model‐predictive direct torque control (MPDTC) for IM drives. It combines the direct torque control (DTC) and finite control set model‐predictive control (FCS‐MPC) methods, and uses the predictive look‐up tables to enhance the overall performance of IM as the good steady‐state performance, lower switching frequency, and to achieve relatively low computational complexity. The simulations and experiments are performed to verify the effectiveness of the proposed control strategy. As the main conclusion of this work, it overcomes the disadvantages of the DTC method such as the high torque and stator flux ripples and those of the FCS‐MPC method such as the large amount of calculation and high average switching frequency. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

This paper proposes a reduced-computation-burden Finite Control Set - Model Predictive Control (FCS-MPC) applied to a dual-converter-based rectifier with a floating DC link. The main goal of this paper is providing a proof of concept of the discussed system employing the proposed FCS-MPC, highlighting its feasibility, simple multivariable control and straightforward implementation. The proposed FCS-MPC reduces the number of tested vectors from the available 64 to only nine, efficiently controlling grid currents and floating DC-link voltage. To evaluate the performance, steady- and transient-state simulations were carried out to compare the proposed FCS-MPC with the conventional PI-based method. The results indicate that FCS-MPC provides a better dynamic response than the PI-based method. However, its total harmonic distortion (THD) at the same sampling frequency is higher, as the PI-based method benefits from a modulation stage that reduces the current ripple. Additionally, the proposed FCS-MPC shows significantly lower switching losses than the PI-based approach. On the other hand, for the same switching frequency, the proposed FCS-MPC presents a somewhat higher, but similar THD and losses values to the PI-based method. Experimental results further validate the feasibility of the proposed FCS-MPC, reinforcing its potential as an efficient alternative to traditional control strategies in dual-converter-based-rectifier.

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

<span>This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>

In this paper, a simplified finite‐control‐set model predictive control (FCS‐MPC) method with a deadbeat solution is proposed for eight‐switch, three‐phase, neutral‐point‐clamped converters (ESTP‐NPCs). Specifically, in order to improve the computational efficiency, a novel sector distribution scheme is presented by dividing the whole voltage vector space into eight sectors. In the each sector, only three voltage vectors are determined as new candidate finite control sets (FCSs), and then the optimal voltage vector that minimizes the predesigned cost function is selected from the new candidate FCS. Meanwhile, in order to avoid the selection of the weighting factor, a multiobjective optimization strategy based on an average ranking is introduced into the proposed strategy. In addition, the DC‐link capacitor voltage oscillations and deviations are suppressed effectively, and the time delay of control command is compensated. In view of these characteristics, the proposed control strategy (i) has lower computational burden, (ii) has good dynamic performance, (iii) does not need the tuning of weighting factor, and (iv) does not need the modulation method. Finally, compared to the conventional FCS‐MPC, the proposed method is more efficient as demonstrated by simulation results. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.