Ant colony optimization for model predictive control for blood glucose regulation

Numerical Methods for Solution of the Extended Linear Quadratic Control Problem

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which optimally plans the host car's speed trajectory for higher energy efficiency through taking the surrounding traffic information and the road conditions ahead into account. To cope with the nonlinearity of the system dynamics, nonlinear MPC (NMPC) is designed in this paper. Afterwards, the nonlinear equality constraints are relaxed so that the optimization problem has a convex feasible set. Moreover, it is proven that the nonlinear problem can be recast as a second-order cone programming (SOCP) problem. The performance of NMPC is evaluated in the real driving condition and compared to both linear MPC and receding horizon dynamic programming (RH-DP). In order to enable real-time control, the interior point method is used to solve both linear and nonlinear MPC problems and the computation time is investigated in this paper.

In this paper, a new coordinated control method for Battery Energy Storage Systems (BESS) to smooth long-term and short-term wind power fluctuations is proposed. Model Predictive Control (MPC) optimization is integrated with Low Pass Filter (LPF) in the proposed method. To enhance the robustness of MPC optimization, which is adversely impacted by wind power prediction errors, Taguchi's Orthogonal Array Testing (TOAT) is utilized to generate representative testing scenarios for the MPC optimization. Case study demonstrates that the new method enhances the controllability of SOC of the BESS and the effectiveness of smoothing of wind power, and the usage of TOAT increases the robustness of MPC optimization.

Model Predictive Control (MPC) has proven to be successful in numerous advanced high-level process control applications based on software implementation in PC/server technology. However, for challenging applications offshore and subsea, ultra-reliable industrial embedded hardware, such as Programmable Logic Controllers (PLCs), are usually more suitable. The limited computational resources in low power industrial embedded devices, in combination with increased demands for computational speed and system reliability, motivate the development of new solution methods and software tools that enable the use of MPC in industrial embedded realtime applications. This tutorial aims at presenting different approaches for both formulating and efficiently solving industrial MPC problems. A brief review of existing methods will be covered, and new results that expand the computational toolbox for solving step response MPC problems will be presented. The new results include a novel MPC scheme that incorporates step response data in a traditional manner, while facilitating the use of block factorization in efficient QP solution methods. The new MPC scheme introduces a stage-wise prediction formulation that enables the use of both tailored Riccati recursion and condensing algorithms that can be embedded into an interior-point method. Implementation aspects necessary for high performance on embedded platforms are discussed, and results on a PLC are presented. Efficient MPC implementations for a subsea compact separation process application, using fast QP solvers such as HPMPC, qpOASES, FiOrdOs, and CVXGEN, will be used in the discussions.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

Learning affine predictors for MPC of nonlinear systems via artificial neural networks

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

We propose a traffic control approach that can reduce both traffic emissions and travel times based on model predictive control (MPC). We approximate the traffic flow and emission models into a linear parameter varying (LPV) form, which leads to an LPV-MPC control approach. We consider two objective functions and formulate them as convex functions, so that convex optimization methods can be used to generate the optimal control sequences of the LPV-MPC control approach. The resulting LPV-MPC solutions can next be used as a good initial point in the nonlinear-nonconvex optimization of the nonlinear original MPC problem.

Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.

Robust Output Feedback MPC for Linear Systems via Interpolation Technique

This paper deals with the application of interval arithmetic to non linear model predictive control (NMPC) problem. The NMPC problem is expressed as a constraints satisfaction problem (CSP) which can be solved by interval analysis techniques. We present the classical interval techniques to build a nonlinear model predictive control law and propose some improvements in order to adapt interval tools to the context of control. Moreover, in order to reduce the pessimism introduced by interval state estimation, we propose a spatial discretisation of the input. These methods are illustrated on a inverted pendulum model.

In this paper, a model predictive control (MPC) strategy for nonlinear homogeneous reaction systems is proposed. The nonlinear system is first transformed to a linear parameter varying (LPV) system by means of a linear transformation known as extents decomposition. The resulting LPV system is further converted to a linear time invariant (LTI) system by means of a parametric state feedback and feedforward control laws. The use of this description results in linear MPC with a quadratic performance index and nonlinear state parameter constraints. However, based on the polytopic nature of the parameter of the LPV systems, the constraints are transformed to a set of intersected polyhedrons. The final result is a linear MPC problem with linear constraints that can be easily converted and solved as a quadratic programming problem. Finally, the performance of the control strategy is illustrated in simulation and compared with a controller based on a constant-parameter LTI model.

In this paper, we provide a method for a quadrotor to track a ground moving target (GMT). The least squares method is used to obtain a series of reference trajectories generated by the GMT. The tracking is accomplished by applying a novel nonlinear model predictive control (MPC) scheme. The nonlinear MPC scheme applied can deal with the dynamic trajectory tracking problem of nonlinear systems by using reference generic terminal ingredients. After obtaining the reachable reference trajectories and calculating the terminal ingredients of the nonlinear MPC problem, the nonlinear MPC problem can be solved online to obtain the control inputs that enable the quadrotor to accurately track the GMT. A simulation experiment is conducted to verify the efficacy of this method.

This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle's dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver's commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., "Nor", "Eco", and "Spt". Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance.

Performance evaluation of isolated three-phase voltage source inverter with LC filter adopting different MPC methods under various types of load