A Coalitional Model Predictive Control Approach for Heterogeneous Cellular Networks

In this paper two model predictive control (MPC) approaches, an on-line and an off-line MPC approach, for constrained uncertain continuous-time systems with piecewise constant control input are presented. Both MPC approaches guarantee robust asymptotic stability by taking into account the hybrid dynamics of the closed-loop system, resulting from the continuous-time system dynamics and the discrete-time model predictive controller dynamics. Examples are presented to demonstrate the applicability of the proposed MPC approaches.

The problem of path following for marine surface vessels using the rudder control is addressed in this paper. The need to enforce the roll constraints and the fact that the rudder actuation is limited in both amplitude and rate make the model predictive control (MPC) approach a natural choice. The MPC design is based on a linearized model for computational and implementation considerations, while the evaluation of the performance of MPC controller is performed on a nonlinear 4 degree of freedom surface vessel model. The simulation results are presented to verify the effectiveness of the resulting controller and a simulation based tuning process for the controller is also presented. Furthermore, the performance of the path following MPC control in wave fields is evaluated using an integrated maneuvering and seakeeping model, and the simulation confirms its robustness.

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

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.

Preliminary study for motion sickness reduction in autonomous vehicles: an MPC approach

Renewable energies are a good option to face the growing demand of energy consumption in the next generation cellular systems, where is expected a significant increase in the number of connected devices. However, there are still major challenges related to the optimization of grid consumption and energy management in cellular networks with hybrid energy sources (grid and renewable energy). In this paper, we compare two control schemes to reduce grid consumption in a heterogeneous cellular network (HetNet) powered by hybrid energy sources. Both schemes are based on a traffic flow method that offloads traffic to base stations powered by green energy. The first one is a reactive strategy that minimizes the grid consumption in each time slot. The second scheme uses a model predictive controller to incorporate forecast of green energy source behaviour. These schemes are evaluated in a simulation scenario, where it is possible to observe that the model predictive controller has a better response reducing grid consumption.

This paper presents a practical off-line approach for tuning Model Predictive Control (MPC) parameters with constraints. The MPC parameters values are computed from the expected closed-loop performances. Our original approach based on the exponential data weighting can be applied to a wide set of controllable Multi-Input Multi-Output (MIMO) processes and plants. It guarantees the stability of closed-loop systems and permits the controller's designer to set a trade-off between accuracy and rapidity. Some simulation results shown emphasize its effectiveness when compared to several existing methods.

THIS dissertation addresses the technical challenges associated with the operation and control of high-power modular multilevel converters. To improve the performance of modular multilevel converter (MMC), a generalized three-phase mathematical model with common-mode voltage (CMV) is proposed. By using the proposed mathematical model, the magnitude of circulating currents, capacitors voltage ripple, and the ripple in DC-link current during balanced and unbalanced operating conditions can be minimized. The modulation scheme and switching frequency are directly affected the output power quality and the performance of the converter and control method. In this dissertation, a novel sampled average and space vector modulation scheme is proposed. These modulation schemes are suitable to control the MMC with any number of submodules (without modifications), operates at low switching frequency, minimizes the ripple in output current and voltage harmonic distortion, and reduces the output filter size. For reliable operation of MMC, the voltage balancing among submodules is mandatory. This dissertation proposes a generalized single-stage balancing approach with reduced current sensors to control the MMC. The proposed balancing approach is suitable to implement with both phase-shifted and level-shifted pulse width modulation schemes. With the proposed approach, it is also possible to control the MMC with half-bridge and three level flying capacitor submodules. Also, an improved balancing approach often referred as the dual-stage balancing approach is proposed to minimize the voltage harmonic distortion and device power losses. This dissertation also proposes a direct model predictive control (D-MPC) approach to minimize the ripple in submodule capacitors voltage. To implement D-MPC approach, a discrete-time model of MMC with CMV is proposed. With the use of proposed model, the D-MPC approach does not require a cost function to minimize the circulating currents. The computational complexity is one of the major issues in the implementation of D-MPC approach for MMC. In this dissertation, a novel reduced computational MPC approaches named as dual-stage D-MPC and indirect model predictive control (I-MPC) approach are proposed. These approaches significantly minimize the computational complexity and, improve the voltage and current waveform quality while operating at the low switching frequency. Finally, the simulation and experimental studies are presented to validate the dynamic and steady-state performance of proposed methodologies. Index Terms • Modular Multilevel Converters. • Capacitors Voltage Balancing. • Pulse Width Modulation Schemes. • Circulating Currents. • Capacitors Voltage Ripple • Direct Model Predictive Control. • Dual-Stage Direct Model Predictive Control. • Indirect Model Predictive Control. • Total Harmonic Distortion.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

An improved model predictive control approach for fuel efficiency optimization of vessel propulsion systems

The evolution of cellular networks from one generation to another has led to the deployment of multiple radio access technologies (such as 2G/2.5G/3G/4G) in the same geographical area. This scenario is termed heterogeneous cellular networks. In heterogeneous cellular networks, radio resources can be jointly or independently managed. When radio resources are jointly managed, joint call admission control algorithms are needed for making radio access technology selection decisions. This chapter gives an overview of joint call admission control in heterogeneous cellular networks. It then presents a model of load-based joint call admission control algorithm. Four different scenarios of call admission control in heterogeneous cellular networks are analyzed and compared. Simulations results are given to show the effectiveness of call admission control in the different scenarios. The coexistence of different cellular networks in the same geographical area necessitates joint radio resource management (JRRM) for enhanced QoS provisioning and efficient radio resource utilization. The concept of JRRM arises in order to efficiently manage the common pool of radio resources that are available in each of the existing radio access technologies (RATs) (Perez-Romero et al, 2005). In heterogeneous cellular networks, the radio resource pool consists of resources that are available in a set of cells, typically under the control of a radio network controller or a base station controller. There are a number of motivations for heterogeneous wireless networks. These motivations are (1) limitation of a single radio access technology (RAT), (2) users’ demand for advanced services and complementary features of different RATs, and (3) evolution of wireless technology. Every RAT is limited in one or more of the following: data rate, coverage, security-level, type of services, and quality of service it can provide, etc. (Vidales et al, 2005). A motivation for heterogeneous cellular networks arises from the fact that no single RAT can provide ubiquitous coverage and continuous high QoS levels across multiple smart spaces, e.g. home, office, public smart spaces, etc. Moreover, increasing users’ demand for advanced services that consume a lot of network resources has made network researchers developed more and more spectrally efficient multiple access and modulation schemes to support these services. Consequently, wireless networks have evolved from one generation to another. However, due to huge investment in existing RATs, operators do not readily discard their existing RATs when they acquire new ones. This situation has led to coexistence of multiple RATs in the same geographical area.

A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.

A novel Model Predictive Control (MPC) approach for Permanent Magnet Synchronous Machines (PMSMs) is proposed in this paper. It consists of choosing a number of MPC cost functions appropriately, in accordance with PMSM control strategy and torque requirements. These are then minimized following an appropriate order of priority, thus enabling enhanced MPC performances and flexibility compared to those achievable by employing weighting factors. Such a good result is achieved also by suitably managing PMSM operating constraints, over both dynamic and steady state operation. The effectiveness of the proposed MPC approach is validated through an experimental study, which refers to a Surface-Mounted Permanent Magnet Synchronous Machine (SPM) driven by means of a suitable control board. This consists mainly of Field Programmable Gate Arrays (FPGA), whose computational performances enable the real-time implementation of the proposed MPC algorithm.

Identification of the optimal control strategies for the energy-efficient ventilation under the model predictive control

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.