Idea Of Model Predictive Control Research Articles

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

Ship autonomous collision avoidance has attracted increasing attention in recent years. However, more attention is paid to the scenario in which the target ship keeps its course and speed. Less attention is paid to the development of a collision avoidance decision-making system under the uncertainty of the target ship's movement in a complex multi-ship encounter situation. Based on the idea of model predictive control (MPC), this paper proposes an autonomous ship collision avoidance decision-making system (CADMS) suitable for the uncertainty of ship motion. The CADMS includes four modules: collision risk analysis module, control and execution module, ship trajectory prediction module and collision avoidance decision-making model. The proposed model can be implemented in the collision avoidance decision-making system for safe navigation or it can be included in the ship autonomous navigation process. The decision-making model is achieved from a risk identification-motion prediction-ship control-scheme implementation perspective, and the dynamic and uncertainty features of the ship action (i.e., alter course or change speed) are considered in the modelling process. The real-time rolling update of ship collision avoidance decisions is realised based on the time series rolling method. Four scenarios are designed to demonstrate the collision avoidance decision-making system's performance. The results show that the proposed collision avoidance decision-making system is a reasonable and effective system for collision avoidance, particularly in multi-ship encounter situations of target ships suddenly altering course or change speed.

Enhancing Dispatchability of Lithium-Ion Battery Sources in Integrated Energy-Transportation Systems With Feasible Power Characterization

Sizeable lithium-ion battery (LIB) sources in the transportation and power sectors provide a promising approach to alleviate the increasing volatility in energy systems. To dispatch LIBs durably and safely, operators need to estimate the battery power characteristics, which are commonly derived from external states of the battery obtained by empirical models. However, the internal states that play a decisive role are rarely considered. In this work, the power characterization is based on an interpretable and analytical electrochemical model. In addition to external states, internal states, including Li-ion concentrations, side reaction rates, and the energy conversion efficiency, are considered in the characterization. Since the dispatch time interval is usually longer than the time resolution of the battery model, an optimization-based approach taking the idea of model predictive control is designed for efficient calculation. A linearization scheme is proposed to embed power characteristics into the optimization-based dispatch of an integrated energy-transportation system with low complexity. Case studies on LiNCM and LiFePO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{4}$</tex-math></inline-formula> batteries in different temperatures are conducted. The calculation of power characteristics takes about two minutes. By considering power characteristics, the energy conversion efficiency of the dispatched battery can be increased by 5%–15%. At the same time, the degradation stress and heat generation can be reduced to around one-fourth of the naïve case.

Greenhouse gases mitigation: global externalities and short-termism

AbstractPolicies designed to control greenhouse gases imply domestic tradeoffs and international externalities, which lead to both domestic and international conflicts, influencing their feasibility and implementations. Our paper investigates two quantitative aspects within this debate. We intend to quantify the impact of: (a) the internalization of international externalities; and (b) the damage associated with a short-term view of climate policies. In this respect, we adopt the innovative (in this field) idea of model predictive control to formalize moving-horizon policy strategies and, thus, to build counterfactuals characterized by a different horizon for all policymakers.

Towards balanced three-phase charging: Phase optimization in adaptive charging networks

We study the problem of phase optimization for electric-vehicle (EV) charging. We formulate our problem as a non-convex mixed-integer programming problem whose objective is to minimize the charging loss. Despite the hardness of directly solving this non-convex problem, we solve a relaxation of the original problem by proposing the PXA algorithm where “P”, “X”, and “A” stand for three variable matrices in the formed phase optimization problems. We show that under certain conditions, the solution is given by the PXA precisely converges to the global optimum. In addition, using the idea of model predictive control (MPC), we design the PXA-MPC, which is an online implementation of the PXA. Compared to other empirical phase balancing strategies, the PXA algorithm significantly improves the charging performance by maximizing energy delivery, minimizing charging price, and assisting future energy planning. The efficacy of our algorithm is demonstrated using data collected from a real-world adaptive EV charging network (ACN).

Model-Based Control Allocation Strategies for Predictive Maintenance of Saturated Actuators

Predictive maintenance techniques are getting increasing interest in industry because instrumental to determine suitable reliability conditions of in-service equipment aimed at smartly establishing when a maintenance operation should take place. This approach presents economic benefits when compared to more traditional routines or time-based preventive maintenance because tasks are performed only when warranted. Within this context, this work presents a control allocation scheme based on Model Predictive Control ideas to deal with actuator loss of effectiveness related to their usage. These undesired events occur when the associated degradation effects, whose evolution is assumed to be measurable and/or predictable, overcome a given threshold. The proposed scheme consists of two modules: a prognostic unit in charge of monitoring the actuators’ health and a reconfigurable control allocation module whose action is based on the current actuators’ degradation level. Its main goal is to establish a suitable time interval during which a maintenance service can occur without affecting the stability of the controlled system and the feasibility properties of the control allocation scheme.

UAV formation obstacle avoidance control method based on artificial potential field and consistency

As a typical multi-agent formation, UAV formation is playing an increasingly powerful role in the civilian and military fields. Obstacle avoidance, as an important technology in controlling formation, determines the application prospects of UAVs. This paper studies the time-varying formation of UAVs with interactive topology to avoid obstacles, aiming to improve the ability of UAV formations to deal with complex environments while traveling. Firstly, a repulsive force field is reasonably introduced based on the existing control scheme, and an improved distributed time-varying formation control scheme based on artificial potential field is proposed. Then combined with the basic idea of model predictive control, an obstacle avoidance strategy in which UAV obstacle avoidance and formation shaping are carried out simultaneously is proposed. Finally, a time-varying formation simulation experiment containing four UAVs was carried out to verify the validity of the results.

A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms

Interactive driving simulation has become a key technology to support the development and optimization process of modern vehicle components and driver assistance systems both in academic research and in the automotive industry. However, the validity of the results obtained within the virtual environment depends essentially on the adequate reproduction of the simulated vehicle movements and the corresponding immersion of the driver. For that reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are used to replicate the simulated accelerations and angular velocities within the physical limitations of the driving simulator best possible. In this paper, we present a novel model-based approach to predict oncoming vehicle motion at runtime. For that purpose, a virtual driver model as well as a simplified vehicle dynamics model are introduced to estimate the future driver inputs and the resulting vehicle trajectories according to the current driving situation. This additional system knowledge enables control algorithms designed on the idea of Model Predictive Control (MPC) to exploit their potential more efficiently. The performance of the proposed prediction strategy is evaluated on the basis of measurement data from a real test run in comparison to an ideal prediction and a constant reference, using a hybrid kinematics motion system as an application example.

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

To enhance the real-time performance and reliability of existing ship collision avoidance methods by collision risk prediction, a predictive collision avoidance method based on an improved beetle antennae search (BAS) algorithm for underactuated surface vessels is proposed. Firstly, a simplified 3-DOF hydrodynamic model based on Abkowitz model is proposed, which provides a model basis for real-time prediction of ship states and collision risks. Referring to the idea of model predictive control (MPC), a predictive optimization strategy for real-time collision avoidance is established by minimizing the safety cost and the economic cost, i.e., collision risk and control changes at the same time. Specifically, the proposed simplified 3-DOF model is used as the state predictive model and the International Regulations for Preventing Collisions at Sea (COLREGs) is considered as the control constraints. To solve the optimization problem, an improved BAS algorithm is proposed to enhance the optimization performance of the original BAS algorithm under the known constraints, which is applied to solve the predictive collision avoidance problem. Simulation experiments under several typical encounter scenarios are carried out based on KVLCC2 ship model, and the effectiveness of the improved BAS based predictive collision avoidance method is verified.

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits. However, the substantial charging load brings challenging issues to the power grid. Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines. The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid. As a result, this paper investigates this important problem and three contributions are made. First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings. Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs. Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon. Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (i.e., myopic and greedy). The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings. Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid. Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines. As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid. However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method. To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there. To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings. Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply. Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings.

Contour error pre-compensation for five-axis high speed machining: offline gain adjustment approach

This paper presents an offline gain adjustment (OGA) approach to reduce contour error in five-axis high speed machining. The proposed contour error formulation is based on the estimation of tool contact points and the OGA is inspired from the idea of model predictive control (MPC). The control gains used in the position loop of servo drives are optimally adjusted offline to reduce the contour error for the considered trajectory. The obtained gain profiles are computed preserving axis kinematic limitations, stability criterion of servo drives, and the motor current constraints. The OGA is developed thanks to a validated machine simulator. The simulation results prove that the OGA reduces significantly the contour error in five-axis high speed machining.

Verification and Control of Hybrid Systems Under Safety Requirements

Abstract In this paper a framework for the formal verification of safety properties of constrained hybrid systems is developed and a stabilization problem is addressed by using model predictive control ideas. By resorting to piecewise affine state space descriptions, system properties are verified by means of forward and backward reachability concepts which are computed via set-theoretic arguments. Along similar lines, a low-demanding receding horizon controller is next derived to deal with the underlying constrained stabilization problem. Simulations on a benchmark train-gate model show the effectiveness of the proposed methodology.

An adaptive reentry guidance method considering the influence of blackout zone

Reentry guidance has been researched as a popular topic because it is critical for a successful flight. In view that the existing guidance methods do not take into account the accumulated navigation error of Inertial Navigation System (INS) in the blackout zone, in this paper, an adaptive reentry guidance method is proposed to obtain the optimal reentry trajectory quickly with the target of minimum aerodynamic heating rate. The terminal error in position and attitude can be also reduced with the proposed method. In this method, the whole reentry guidance task is divided into two phases, i.e., the trajectory updating phase and the trajectory planning phase. In the first phase, the idea of model predictive control (MPC) is used, and the receding optimization procedure ensures the optimal trajectory in the next few seconds. In the trajectory planning phase, after the vehicle has flown out of the blackout zone, the optimal reentry trajectory is obtained by online planning to adapt to the navigation information. An effective swarm intelligence algorithm, i.e. pigeon inspired optimization (PIO) algorithm, is applied to obtain the optimal reentry trajectory in both of the two phases. Compared to the trajectory updating method, the proposed method can reduce the terminal error by about 30% considering both the position and attitude, especially, the terminal error of height has almost been eliminated. Besides, the PIO algorithm performs better than the particle swarm optimization (PSO) algorithm both in the trajectory updating phase and the trajectory planning phases.

P.2.b.031 - Measuring change in depression using the LAPS – findings in the "Mosaic of Emotions” group experience with agomelatine

In our previous work, we have studied a bipedal walking model with a torso, in which the gait evolves from the stable period-1 pattern directly into the Neimark–Sacker bifurcation pattern. Using the Ott–Grebogi–Yorke method, the bifurcation gait could be suppressed into the period-1 gait with higher energy efficiency and walking speed. However, the disturbance rejection ability of the obtained period-1 gait was insufficient, i.e., the basin of attraction was small. In this paper, a new suppression method based on the idea of model predictive control is proposed. Because of the design of the new walking model, which has a time window for computation, and the ability to calculate the walking map quickly, the optimal parameter perturbation can be generated in real time during walking. As a result, the suppression of the bifurcation gait for our bipedal robot can be achieved on-line. This new method not only makes the gait of the controlled model converge to the target period-1 gait with desired high performance, but also guarantees that the obtained gait is better able to reject disturbances.

Bifurcation gait suppression of a bipedal walking robot with a torso based on model predictive control

In our previous work, we have studied a bipedal walking model with a torso, in which the gait evolves from the stable period-1 pattern directly into the Neimark–Sacker bifurcation pattern. Using the Ott–Grebogi–Yorke method, the bifurcation gait could be suppressed into the period-1 gait with higher energy efficiency and walking speed. However, the disturbance rejection ability of the obtained period-1 gait was insufficient, i.e., the basin of attraction was small. In this paper, a new suppression method based on the idea of model predictive control is proposed. Because of the design of the new walking model, which has a time window for computation, and the ability to calculate the walking map quickly, the optimal parameter perturbation can be generated in real time during walking. As a result, the suppression of the bifurcation gait for our bipedal robot can be achieved on-line. This new method not only makes the gait of the controlled model converge to the target period-1 gait with desired high performance, but also guarantees that the obtained gait is better able to reject disturbances.

Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions.

Multiple-Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth limited communication network using adaptive sampling intervals. The controller is centralized and computes at every sampling instant not only the new control command for a process, but also decides the time interval to wait until taking the next sample. The approach relies on model predictive control ideas, where the cost function penalizes the state and control effort as well as the time interval until the next sample is taken. The latter is introduced in order to generate an adaptive sampling scheme for the overall system such that the sampling time increases as the norm of the system state goes to zero. The paper presents a method for synthesizing such a predictive controller and gives explicit sufficient conditions for when it is stabilizing. Further explicit conditions are given which guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved off-line and that the controller can be implemented as a lookup table of state feedback gains. Simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains.

Packetized MPC with dynamic scheduling constraints and bounded packet dropouts

We study a Networked Control System architecture which uses a communication network in the controller–actuator links. The network is affected by packet dropouts and allows access to only one plant input node at each time instant. This limits control performance significantly. To mitigate these limitations we propose a control and network protocol co-design method. Succinctly, the underlying features of the proposed method are as follows: a sequence of predicted optimal control values over a finite horizon, for an optimally chosen input node, is obtained using Model Predictive Control ideas; the entire resulting sequence is sent to the chosen input node; a smart actuator is used to store the predictions received and apply them accordingly. We show that if the number of consecutive packet dropouts is uniformly bounded, then partial nonlinear gain ℓ2 stability and also a more traditional linear gain ℓ2 stability can be ensured via appropriate choice of design parameters and the right assumptions. Whilst our results apply to general nonlinear discrete-time multiple input plants affected by exogenous disturbances, for a disturbance-free case we prove that Global Asymptotic Stability follows from our main result. Moreover, we show that by imposing stronger assumptions, Input-to-State Stability is achievable as well. Finally we demonstrate the potential of the proposed method via simulations.

STOCHASTIC MODEL PREDICTIVE CONTROL AND PORTFOLIO OPTIMIZATION

This paper proposes a solution method for the discrete-time long-term dynamic portfolio optimization problem with state and asset allocation constraints. We use the ideas of Model Predictive Control (MPC) to solve the constrained stochastic control problem. MPC is a solution technique which was developed to solve constrained optimal control problems for deterministic control applications. MPC solves the optimal control problem with a receding horizon where a series of consecutive open-loop optimal control problems is solved. The aim of this paper is to develop an MPC approach to the problem of long-term portfolio optimization when the expected returns of the risky assets are modeled using a factor model based on stochastic Gaussian processes. We prove that MPC is a suboptimal control strategy for stochastic systems which uses the new information advantageously and thus is better than the pure optimal open-loop control. For the open-loop optimal control optimization, we derive the conditional portfolio distribution and the corresponding conditional portfolio mean and variance. The mean and the variance depend on future decision about the asset allocation. For the dynamic portfolio optimization problem, we consider constraints on the asset allocation as well as probabilistic constraints on the attainable values of the portfolio wealth. We discuss two different objectives, a classical mean–variance objective and the objective to maximize the probability of exceeding a predetermined value of the portfolio. The dynamic portfolio optimization problem is stated, and the solution via MPC is explained in detail. The results are then illustrated in a case study.

Optimal State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors

We study a linear discrete-time partially observed system perturbed by a white noise. The observations are transmitted to the estimator via communication channels with irregular transmission times. Various measurement signals may incur independent delays, arrive at the estimator out of order, and be lost or corrupted. The estimator is given a dynamic control over the sensors by taking part in producing the signals to be sent via the channels. The minimum variance state estimate and the optimal sensor control strategy are obtained. Ideas of model predictive control are applied to derive a nonoptimal but implementable in real time method for sensor control.

Discussion on: “Improved MPC Design based on Saturating Control Laws”

This paper provides an interesting contribution on the integration of model predictive control (MPC) with control Lyapunov functions (CLF). Such an integration, as shown in Ref. [1], yields control strategies outperforming control design based on either MPC or CLF. In particular, the authors address the design of MPC strategies for linear discrete-time systems subject to input and/or state constraints with a large domain of attraction and a relatively low computational burden. Standard MPC with guaranteed stability either requires large control horizons or typically yields a much smaller domain of attraction. Fixed the length of the prediction horizon N, the domain of attraction can be enlarged by increasing the size of the terminal set without increasing the on-line computational burden. The authors combine the computation of an estimate of the domain of attraction, denoted as C1, for the saturated LQR regulator via linear difference inclusions (LDI) proposed in Ref. [2] and model predictive control ideas. The main contribution of the paper consists of a dual-mode algorithm that switches among two MPC algorithms with different terminal cost/set according to the feasibility. More precisely, a standard MPC algorithm with terminal cost/set related to the unsaturated LQR terminal control law and a modified MPC strategy employing as terminal set the estimate of the domain of attraction of the saturated LQR control law and as terminal cost the associated quadratic CLF, are used. The proposed terminal cost is an upper bound of the optimal one, and the resulting optimization problem amounts to the solution of a quadratic programming problem. The proposed procedure can encompass multivariable constrained control problems in the presence of state constraints. A first remark to be made is that although the authors consider only systems without uncertainty, the extension of the approach to the case of systems affected by additive unknown bounded noise and/or described by an uncertain polytopic model is rather straightforward [3]. According to the practitioners, what limits the performance and applicability of MPC are difficulties in modeling. Then it becomes fundamental to implement MPC algorithms for uncertain models with large domain of attraction and relatively low computational burden. However, the choice of presenting the results for systems without any kind of uncertainty is welcome from the point of view of simplicity. A second remark can be made in relation to the choice of the adopted LDI in order to describe the saturated system. The authors claim that the present results can be easily extended to any LDI description of the saturated system. The usage of an LDI in describing the closed-loop system clearly introduces some conservatism and then an interesting topic for future investigation would be the study of which is the best LDI description in order to reduce the conservatism.