Hybrid Optimal Control Research Articles

Hybrid Optimal Control for Malware Propagation in UAV-WSN System: A Stacking Ensemble Learning Control Algorithm

Hybrid Optimal Control for Malware Propagation in UAV-WSN System: A Stacking Ensemble Learning Control Algorithm

Loss control regions in optimal control problems

This paper addresses optimal control problems with loss control regions. In that context the state space is partitioned into disjoint subsets, referred to as regions, which are classified into two types: control regions and loss control regions. When the state belongs to a control region, the control is permanent (i.e. the control value is authorized to be modified at any time). On the contrary, when the state belongs to a loss control region, the control must remain constant as long as the state belongs to this region. The objective of this paper is twofold. First, we reformulate the above setting into a hybrid optimal control problem (with spatially heterogeneous dynamics) involving moreover a regionally switching parameter, and we prove a corresponding hybrid maximum principle: hence first-order necessary optimality conditions in a Pontryagin form are obtained. Second, this paper proposes a two-steps numerical scheme to solve optimal control problems with loss control regions. It is based on a direct numerical method (applied to a regularized problem) which initializes an indirect numerical method (applied to the original problem and based on the aforementioned necessary optimality conditions). This numerical approach is applied to several illustrative examples.

Reinforcement learning for optimal control of linear impulsive systems with periodic impulses

This paper focuses on the finite- and infinite-horizon optimal control problems of linear impulsive systems with periodic impulses under the quadratic performance index. Necessary and sufficient conditions for the optimal impulsive system are derived in terms of hybrid Riccati equations by utilizing the variational method and the collocation method combined with a time-varying Lyapunov function. Different from the existing model-based impulsive control schemes, three reinforcement learning (RL)-based algorithms are proposed to solve the optimal impulsive controller and hybrid controller for the impulsive system without the exact knowledge of the system dynamics. The asymptotical stability of the impulsive control system and the convergence of the RL-based algorithms are proved rigorously. Finally, a numerical simulation illustrates the effectiveness of the proposed control methods.

An optimal hybrid control strategy for supporting frequency regulation considering fatigue load mitigation of wind turbines

An optimal hybrid control strategy for supporting frequency regulation considering fatigue load mitigation of wind turbines

Data-Based Optimal Switching and Control With Admissibility Guaranteed Q -Learning.

This article addresses the data-based optimal switching and control codesign for discrete-time nonlinear switched systems via a two-stage approximate dynamic programming (ADP) algorithm. Through offline policy improvement and policy evaluation, the proposed algorithm iteratively determines the optimal hybrid control policy using system input/output data. Moreover, a strict proof of the convergence is given for the two-stage ADP algorithm. Admissibility, an essential property of the hybrid control policy must be ensured for practical application. To this end, the properties of the hybrid control policies are analyzed and an admissibility criterion is obtained. To realize the proposed Q -learning algorithm, an actor-critic neural network (NN) structure that employs multiple NNs to approximate the Q -functions and control policies for different subsystems is adopted. By applying the proposed admissibility criterion, the obtained hybrid control policy is guaranteed to be admissible. Finally, two numerical simulations verify the effectiveness of the proposed algorithm.

Hybrid Optimal Control of a Flying+Sailing Drone: Flying with 6 and Sailing with 5 Degrees of Freedom

This paper studies a multi-modal robotic system referred to as a flying+sailing drone with the consideration of six degrees of freedom (6-DoF) for flying and 5-DoF for sailing, i.e., a twelve-dimensional state space for flying and a ten-dimensional state space for sailing. Key characteristics of hybrid systems emerging in tasks involving both flying and sailing are (i) changes in the dimension of the state space as the system switches from flying to sailing and vice versa, (ii) the presence of autonomous switchings triggered upon the landing of the drone on the water surface, and (iii) non-identity jumps in the state upon switching. For the scenario in which the drone’s initial state is in the flying mode and a fixed terminal state is specified in the sailing mode, the associated optimal control problems are studied for the minimization of time and the minimization of the control effort. The necessary optimality conditions are obtained from the Hybrid Minimum Principle (HMP), and the associated numerical simulations are presented.

A stochastic hybrid optimal control with regime switching and recursive cost functional

A stochastic hybrid optimal control with regime switching and recursive cost functional

Coordination and Optimization Control Framework for Vessels Platooning in Inland Waterborne Transportation System

Vessels sailing in a single platoon could reduce resistance from the perspective of the whole platoon and the individual vessel, and contribute to improving energy benefits. Moreover, transportation energy costs and traffic efficiency are essential indicators for measuring waterborne transportation systems. We attempt to minimize transportation energy costs by coordinating platoon formation using a distributed framework of controllers. A large-scale coordinated vessel platooning program is proposed to minimize transportation energy costs and optimize traffic efficiency while guaranteeing safety. The control framework covers routing, energy consumption-dependent cooperative platooning decision and speed optimization based on graph search algorithm, cluster analysis, optimal control approach and model predictive control. Firstly, a local scheduling strategy combined with the leader vessel selection algorithm is adopted. Furthermore, we used cluster analysis to create a series of mergeable vessel platooning sets. Then, we used the mathematical planning method and a two-step hybrid optimal control approach to calculate the improvement and optimization of each vessel platoon’s path and speed. Finally, the scalability of the scheduling strategy is elucidated. In a simulation of large scale inland waterborne network, savings surpassed 3.5% when six hundreds vessels participated in the system. These simulation results reveal that the scheduling strategy coordinating vessels into vessel platooning, which improves transportation efficiency as well as descends cost, comparing to a fixed origin route in the waterway network.

Dynamic Modelling and Intelligent Hybrid Optimal Controller of Hybrid Manipulator for Vibration Suppression and Tracking Control

Dynamic Modelling and Intelligent Hybrid Optimal Controller of Hybrid Manipulator for Vibration Suppression and Tracking Control

The minimum principle of hybrid optimal control theory

The minimum principle of hybrid optimal control theory

Area control error enhancement of two-area power system using hybrid intelligence optimal controller

Area control error (ACE) is a critical factor in linked power systems. When a disturbance occurs, ACE is utilized to determine how much power should be deployed. As a result, it is critical that the ACE have as little inaccuracy as feasible. This research provided a strategy for improving the dynamic response of ACE in a power system. A hybrid optimal controller is the name given to this technology. Coordination between the proportional-integral (PI) controller and the state feedback controller based on the linear quadratic regulator (LQR) is the concept of a hybrid optimum controller. All controller parameters are created utilizing artificial immune system (AIS) clonal selection to improve coordination. The proposed control mechanism is demonstrated using a two-area power system as a test system. To investigate the efficacy of the suggested strategy, time domain simulation is used. The simulation results show that the suggested method outperforms the previous situations in this work (the overshot of frequency deviation in areas 1 and 2 is 0.00029 and 0.00015, respectively)

Uncertain stochastic hybrid differential game system with V-n jumps: Saddle point equilibrium strategies and application to advertising duopoly game

This paper focuses on the saddle point equilibrium strategies for uncertain stochastic hybrid differential game systems under chance theory. We consider systems with dynamics described by both an uncertain differential equation with V-n jumps (UDEV) and a stochastic differential equation (SDE), with linear and quadratic performance index functions. Based on chance theory and the equation of optimality of uncertain stochastic hybrid optimal control model, the saddle equilibrium equation for general uncertain stochastic hybrid differential game system is established. Furthermore, three classes of differential game models are discussed by using equilibrium equation. Finally, an advertising duopoly game problem as an application is provided, the corresponding optimal profit difference value and advertising strategies are analyzed.

A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios.

Constrained hybrid optimal model predictive control for intelligent electric vehicle adaptive cruise using energy storage management strategy

This paper presents a constrained hybrid optimal model predictive control method for the mobile energy storage system of Intelligent Electric Vehicle. A novel adaptive cruise control system is designed to optimize mobile energy storage management, active safety control, and fuel economy. A hierarchical control structure is proposed for active safety control and energy flow management. The main-loop is proposed to analyze and optimize active cruise safety control and energy management index using non-linear constrained hybrid optimal model predictive control method. The inner loop is used to chase the aim signal from the main loop using hysteresis current control method. Then, an electronic longitudinal control system is designed to avoid the collision and optimize energy management between the IEV and cruise following vehicles. At last, the simulations with typical driving conditions are built to justify the performance of the proposed controller. The results illustrate that the IEV with the designed hybrid controller can adaptively tracking the following vehicles, reduce the possibility of collision with optimal energy flow management.

Optimal energy management system for the design of off grid hybrid renewable energy systems: A hybrid technique

This manuscript proposes an efficient hybrid strategy to obtain the optimal solution of operational cost reduction, size reduction of hybrid renewable energy sources and optimal power flow control for off-grid system. Here, off-grid is incorporated with photovoltaic array, wind turbine, Diesel generator, and battery energy storage system. The hybrid method is joint execution of Giza Pyramids Construction (GPC) and Billiards-inspired optimization algorithm (BOA) hence it is named GPC-BOA technique. The major purpose of proposed method is minimizing the operational cost as well as size of hybrid renewable energy sources and improves the power flow of system. In this energy management system of off-grid provides cost reduction which includes the generation, replacement, operating and maintenance, cost of fuel consumption, cost of exchanged power with grid, and the penalty for emissions. Here, the GPC method is employed for forecasting the load requirement of system. The BOA technique optimizes the off-grid system through the deliberation of forecasted load requirement. At last, the proposed approach is performed on MATLAB platform and the performance is assessed using existing techniques.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

This study aimed to minimize the tumor cell population by using minimal medicine for chemotherapy treatment while maintaining the effector-immune cell population at a healthy threshold. Therefore, a mathematical model was developed in the form of ordinary differential equations (ODE), and the solution to Multi-Objective Optimal Control Problem (MOOCP) was obtained using Multi-Objective Optimization algorithms. In this study, the interaction of the tumor-effector cell population with chemotherapy was investigated using Pure MOOCP and Hybrid MOOCP methods. The handling of constraints and the Pontryagin Maximum Principle (PMP) differ among these methods. Swarm Intelligence (SI) and Evolutionary Algorithms (EA) were used to process the results of these methods. The numerical outcomes of SI and EA are displayed via Pareto Optimal Front. In addition, the solutions from these algorithms were further analyzed using the Hypervolume Indicator. The findings of this study demonstrate that the hybrid method outperforms Pure MOOCP via Multi-Objective Differential Evolution (MODE). The MODE produces a point on the Pareto Optimal Front with minimal distance to the origin, where the distance represents the best solution.

Multi-Agent Multi-Objective Optimization of a Very-Long-Baseline Interferometry Mission

Deep-space-distributed spacecraft missions continue to gain relevance as real-world missions such as the Laser Interferometer Space Antenna, Mars Sample Return, and others are conceived, designed, and flown. These classes of mission designs continue to challenge state-of-the-art trajectory optimization tools, often requiring significant changes to software and even new problem transcriptions entirely, in order to solve for optimal solutions. Trajectory optimization for multiple-vehicle missions poses all the challenges of the optimization of single-vehicle missions, but with added complexity of increased combinatorial scope due to multiple spacecraft, interspacecraft coordination constraints, and coordinated science objectives, motivating the need for new technical capabilities. The goal of this paper is to apply a multi-objective, multi-agent hybrid optimal control problem transcription, utilizing several new techniques, to optimize a very-long-baseline interferometry mission design. We develop and apply several new techniques in this capability, including a HashMap archive utility to prevent resolving missions, and a null gene transcription to vary both fleet size, and observation multiplicity. Applying these techniques enables efficient exploration the multi-objective nondominated front of a multivehicle design space where the number of spacecraft varies. The HashMap archive utility proves an essential component of this capability as duplicate candidate missions are discovered on average 17% of the time, and it yields an order of magnitude quicker lookup time compared with a text file analog. Within the resulting nondominated front of solution missions, many interesting solutions appear, including one mission with a fleet of 7 spacecraft that images 16 radio sources.

Steering and Speed Control System Design for Autonomous Vehicles by Developing an Optimal Hybrid Controller to Track Reference Trajectory

In this paper, a longitudinal and lateral control system of an autonomous vehicle is presented by developing a novel hybrid trajectory tracking algorithm. In this proposed method, the longitudinal control system is developed based on the curvature information of the reference path. The autonomous vehicle modifies the desired speed according to the estimated size and types of the reference trajectory curves. This desired speed is integrated into the PID controller to maintain an optimal speed of the vehicle while following the given path. The lateral control system is designed based on feedforward (preview control) and feedback (LQR) controllers to reduce lateral errors between the trajectory and autonomous vehicle. The feedforward and the feedback controllers generate precise steering angles to eliminate orientation and lateral errors caused by the curvature of the trajectory and external disturbances. The effectiveness of the proposed method is evaluated by comparing simulation and experimental results with different trajectory tracking algorithms on simulated and experimented paths. It is proven that the proposed algorithm is capable of significantly minimizing lateral errors on sharp curves compared to other path tracking methods.

An optimal hybrid control scheme to achieve power quality enhancement in micro grid connected system

AbstractIn this manuscript, a novel control scheme is proposed to achieve the power quality (PQ) enhancement of renewable energy sources (RES), such as photovoltaic (PV), wind turbine (WT), fuel cell (FC), and battery. The proposed hybrid technique is the consolidation of both the Improved Bat Algorithm (IBat) and Moth Flame Optimization Algorithm (MFOA), therefore it is known as Improved Bat search Algorithm with Moth Flame Optimization Algorithm (IBatMFOA) control strategy. The crossover and mutation function is utilized to modify the bats search behavior function. Here, MFOA is utilized to enhance the searching behavior of IBat technique by reducing the error function. The main goal of proposed IBatMFOA approach is “to enhance the PQ depending on active with reactive power varience.” To attain the target, MFOA is optimized to lessen the power variation. Moreover, the functioning cost of RESs is diminished based on daily with weekly data forecast, like grid electricity price, electrical load, environmental parameters. By using IBatMFOA technique, the entire system efficiency is enhanced. By then, the proposed method is activated in MATLAB site, then the performance is examined with existing methods, like artificial bee colony, Gravitational Search Algorithm, and Firefly algorithm. The active power controller parameters of proposed technique are 8.8554 and 1.8569. The reactive power controller parameters of proposed technique are 8.1657 and 1.5698.