Conflicting Control Objectives Research Articles

Model Predictive Control in buildings with thermal and visual comfort constraints

Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2% to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints.

Design considerations for a hybrid wind-wave platform under energy-maximising control

The environmental impact of emissions of fossil fuels and their rising prices, together with countries' commitment to mitigate the effect of the alarming and rapid climate changes, have been a crucial thrust to investigate new solutions to have an energy supply depending on renewable energies. In this scenario, offshore wind-wave hybrid platforms have been recently promoted: sharing facilities, infrastructure, and grid connections, give these systems the potential to increase energy production at a lower cost. However, an efficient realisation of these two combined technologies requires two potentially conflicting control objectives: On the one hand, for the wind turbine, a reduced movement of the platform is required, which essentially translates to enhanced stability of the structure, so that its behaviour resembles standard onshore wind technologies. On the other hand, to maximise the energy produced, wave energy converters (WECs) require optimal control technology, which often leads to large amplitude motion, potentially conflicting with the stability requirement for the wind turbine.
 The aim of this study is to investigate the effects of design changes on the dynamics of the hybrid wind-wave platform under energy-maximising control, which can be analysed in terms of the principle of impedance-matching. A semi-submersible platform with an incorporated flap-type WEC will be analysed both from a closed-loop and open-loop perspective, and the control system will be designed to maximise the energy produced by the WEC. Design changes on the wind-wave conversion platform will be in terms of flap dimensions, starting from a nominal geometry based on the so-called Oyster system. Analyses will be conducted by both increasing and decreasing the flap depth from the nominal case, to investigate the effect of the different geometries on the interactions between the WEC and the platform. A frequency-domain analysis of the overall input/output (velocity) system will be presented, highlighting the situations that can enhance the potential of both devices and exploit their synergies.

Coordinated optimization of robustness and flexibility of building heating systems for demand response control considering prediction uncertainty

Robust strategies are essentially needed in building energy systems for meeting load demands stably under uncertainty, whereas demand response (DR) can serve the grid through flexible operation and achieve remarkable economic benefits. However, formulating optimal control strategies that enable weighing robustness and flexibility for energy systems is quite challenging and rarely addressed owing to their conflicting control objectives. In this paper, a dual-objective coordinated optimization approach is proposed and applied to the heating system of an office building equipped with heat pumps, to balance the robustness and flexibility of DR control under building load uncertainty. The load distribution interval is predicted by the quantile regression neural network model. And optimal DR strategies are performed via modulating indoor temperature set-points. Using the genetic algorithm and multi-objective decision-making optimization framework, the optimal strategy can be obtained by weighing the confidence level of load prediction interval and the operating cost of flexible operation. The results show that, compared with the robust optimal strategy, the coordinated optimal strategy reduces the operating cost by 35.8% with robustness that meets actual demands in 80% of the cases. Compared to the flexible optimal strategy, it sacrifices only 0.05% cost but increases the load guaranteed hours by 33.3%.

Integrating H2 synthesis and Dynamic Error Budgetting for improved Gravitational Wave detection

Dynamic error budgets are an essential tool in identifying opportunities for improvements in a control system for Gravitational Wave detectors, but their potential is often not fully utilized in the control design. This paper presents a model and dynamic error budget for a challenging nested control system in the Advanced Virgo detector in combination with a systematic control design framework for one of the controllers. This framework fully utilizes the dynamic error budget by using H2 synthesis to allow for fast iterations in the control design when dealing with conflicting control objectives. Simulations together with experimental results on Advanced Virgo illustrate the effectiveness of the presented framework.

Voltage regulation and current sharing for multi-bus DC microgrids: A compromised design approach

It is well known that accurate current sharing and voltage regulation are both important, yet conflicting control objectives in multi-bus DC microgrids. In this paper a distributed control scheme is proposed, which simultaneously considers these two control objectives via a trade-off factor. This factor permits to adjust the degree of compromise between accurate voltage regulation and current sharing. At the same time, the voltage of a critical node can be precisely regulated. A sufficient condition for closed-loop stability is given and it is shown that the control parameters can always be chosen, such that stability is guaranteed. In addition, the steady state voltage and current deviations relative to their rated values are quantified via suitable metrics. For a given topology and settings of a DC microgrid, a sufficient condition for the existence of the trade-off factor is provided. The results are illustrated by simulation examples.

Multi-objective model predictive control with gradient eigenvector algorithm

Multi-objective model predictive control (MMPC) is an effective method to solve the problem of nonlinear systems with multiple conflicting control objectives. However, the MMPC method usually suffers from the challenge of high computational cost for the determining the optimal objective weights with the process of transforming multiple objectives into a single objective. For low computational cost, an MMPC method with gradient eigenvector algorithm (MMPC-GEA) for nonlinear systems is proposed to comprehensively deal with multiple conflicting control objectives. The proposed MMPC-GEA in the framework of MMPC is composed of a fuzzy neural network (FNN) identifier and a receding optimization algorithm. For the proposed MMPC-GEA, FNN with an adaptive learning algorithm is devised to capture the nonlinear characteristic of systems. Moreover, a gradient eigenvector algorithm (GEA) is designed to gain the optimization solution of the control objectives for nonlinear systems. Specifically, GEA can reduce the computationally demanding by avoiding the determination of the objective weights. Furthermore, the stability and control performance analysis of the MMPC-GEA scheme is provided. Finally, the effectiveness of the proposed MMPC-GEA approach is demonstrated using a numerical simulation and wastewater treatment process.

Experimental Validation of LQR Weight Optimization Using Bat Algorithm Applied to Vibration Control of Vehicle Suspension System

To deal with multiple constraints of vehicle active suspension system (ASS) including road handling and passenger safety, this paper presents an optimal linear quadratic regulator (LQR) approach which employs bat algorithm (BA) for selection of optimal state and input penalty matrices of LQR. We formulate the conflicting control objectives of ASS, namely, ride comfort and passenger safety as a multi-constraint optimization problem and employ the BA for weight selection of LQR. The key advantage of the proposed approach is that the local optima problem is avoided by utilizing the frequency tuning and random walk technique in BA. The performance of the proposed approach is experimentally tested using hardware in loop (HIL) testing on a quarter car ASS for realistic road profiles. Moreover, the performance is benchmarked against grey wolf optimization tuned LQR. Experimental results assessed based on ISO 2631 standards highlight the significant improvement in the ride comfort and passenger safety.

Multi-Objective Model Predictive Control for Vehicle Active Suspension System

This paper puts forward a multi-objective model predictive control scheme in order to address the conflicting control objectives of a vehicle active suspension system. The key problem in designing an active suspension controller is that the controller has to realize a feasible control input that can satisfy the competing control requirements such as ride comfort, suspension travel and road handling. Hence, in this work, these constraints are integrated into an optimal control framework and a finite horizon model predictive controller is used to solve the multi-objective cost function. The key advantage of the proposed scheme is that the model predictive control design finds the optimal control input by solving the discrete time algebraic Riccati equation. This guarantees not only a robust closed loop system but also a realizable control effort, without violating the hard constraints of the active suspension system. The proposed model predictive control design is experimentally validated on a laboratory scale quarter car suspension system using hardware-in-loop testing. The performance of the model predictive control scheme is compared with the one of the unconstrained linear quadratic regulator and tested for four realistic road profiles. The experimental results substantiate that the suspension system controlled by the model predictive controller offers better ride comfort and road handling features when compared to the conventional linear quadratic regulator.

Voltage regulation and current sharing for multi-bus DC microgrids

It is well known that accurate voltage regulation and current sharing are conflicting control objectives for DC microgrids. By taking electrical network into consideration, this paper analyzes the relation between voltage regulation and current sharing. Based on this relationship, a novel control scheme, which simultaneously considers both voltage regulation and current distribution, is proposed. It significantly simplifies the design complexity and is able to adjust the degree of compromise between accurate voltage consensus and accurate current sharing.

Commercial Vehicle-Based Robust Control of Seated Whole-Body Vibration Using Adaptive Indirect Type-2 Fuzzy Neural Network

Drivers of commercial vehicles are invariably subject to chronic diseases such as back pain as a result of exposure to severe cabin excitations. In this paper, a six degrees of freedom (6-DOF) coupled human-body and seat suspension system is modeled in order to reduce the vibrations transmitted to the head, seat, and the relative seat and cabin floor displacement. The contributions of the present paper are: 1) two significant but inherently conflicting control objectives are employed, namely the seat acceleration and the relative displacement between seat and cabin floor to account for the effect of seat endstops, 2) A novel learning rate gradient descent based neural network approximator algorithm coupled to an adaptive indirect type-2 fuzzy neural network (T2FNN) controller to converge the controller to the ideal parameters of the uncertain model. 3) The controller model takes into account the seat suspension nonlinearities due to the nonlinear asymmetric piecewise damper and the cubic hardening of the suspension spring. The proposed controller employs the principle of type-2 fuzzy systems with interval membership function and unknown specifications. The effectiveness of the closed-loop system is validated regarding the uncertainties compared to observer-based sliding mode controller (SMC) and a high-fidelity virtual lab MSC.ADAMS-Simulink platform to validate the results in practical scenarios.

Distributed Control of VSC-MTDC Systems Considering Tradeoff Between Voltage Regulation and Power Sharing

This paper presents a novel distributed voltage control method for multi-terminal direct current (MTDC) systems based on the average consensus algorithm. The proposed method combines the idea of secondary integral voltage control and regulator synchronization problem. It realizes adjustable tradeoff between two conflicting control objectives, that is, voltage regulation and power sharing. The proposed control algorithm only needs a low-bandwidth communication between two adjacent voltage source converters (VSCs) and local information to work effectively. Moreover, regarding the system dynamic response, a parameter tuning method for the proposed controller is provided to achieve the fastest convergence of the distributed control algorithm. This paper also develops a small-signal dynamic model for VSC-MTDC systems with distributed controllers to evaluate the system dynamic performance. The proposed method is validated by time-domain simulation of a 4-terminal VSC-MTDC system. The simulation results show that flexible tradeoff between voltage regulation and power sharing can be achieved under necessary constraints on the DC bus voltages and power sharing ratio.

Data-Driven Multiobjective Predictive Control for Wastewater Treatment Process

To comply with the effluent standards and growing demands for safety and reliability, the operation of wastewater treatment processes (WWTPs) has been considered as a multiobjective control problem. In this article, a data-driven multiobjective predictive control (MOPC) method is developed to deal with the conflicting control objectives to improve the operation performance of WWTPs. The main contributions of MOPC are three folds: first, a multiobjective control strategy is developed in the design of MOPC. And an adaptive fuzzy neural network identifier, using the relevant process data, is designed to catch the nonlinear behaviors of WWTPs. Second, a transfer multiobjective optimization algorithm (TMOOA) is developed to obtain the optimal solutions of the conflicting control objectives. The major advantage of TMOOA is its low computational cost, which is realized by avoiding the computation of Pareto fronts. Third, the stability of MOPC has been given in detail. Meanwhile, the benefits and feasibility of MOPC are confirmed on the benchmark simulation model no. 2. The results further demonstrate the effectiveness of the proposed control method.

Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control

To enhance the 48 V mild hybrid electric vehicle performance using a smaller capacity and lower voltage battery than the full hybrid electric vehicle, a novel power management strategy needs to be established that considers the characteristics and limitations of the components. This paper proposes a charge-sustaining control strategy as a ground principle of the 48 V hybrid electric vehicle control for managing the battery state-of-charge (SOC) to stay near the most efficient regime. The base efficiency characteristics of the component models including engine, motor/generator, and battery are determined in the form of efficiency maps using the powertrain analysis tool. Then the control strategy is formulated as a nonlinear optimal regulation problem that meets two conflicting control objectives, such as fuel efficiency improvement and state-of-charge maintenance. The optimal regulation problem implements a discrete-time Hamilton-Jacobi-Bellman approach. The proposed strategy is evaluated by comparing with the reference strategy applying the Dynamic Programming (DP), i.e. a global optimal result, under urban dynamometer driving schedule and worldwide harmonized light duty test cycle. Through the evaluation, the fuel efficiency of the proposed strategy with three different electrical loads is slightly deteriorated at most by 5.03 % from the DP results with staying within a desirable SOC. This suggests that the proposed strategy is operating very closely to global optimal performances.

Control Barrier Function Based Quadratic Programs for Safety Critical Systems

Safety critical systems involve the tight coupling between potentially conflicting control objectives and safety constraints. As a means of creating a formal framework for controlling systems of this form, and with a view toward automotive applications, this paper develops a methodology that allows safety conditions -- expressed as control barrier functions -- to be unified with performance objectives -- expressed as control Lyapunov functions -- in the context of real-time optimization-based controllers. Safety conditions are specified in terms of forward invariance of a set, and are verified via two novel generalizations of barrier functions; in each case, the existence of a barrier function satisfying Lyapunov-like conditions implies forward invariance of the set, and the relationship between these two classes of barrier functions is characterized. In addition, each of these formulations yields a notion of control barrier function (CBF), providing inequality constraints in the control input that, when satisfied, again imply forward invariance of the set. Through these constructions, CBFs can naturally be unified with control Lyapunov functions (CLFs) in the context of a quadratic program (QP); this allows for the achievement of control objectives (represented by CLFs) subject to conditions on the admissible states of the system (represented by CBFs). The mediation of safety and performance through a QP is demonstrated on adaptive cruise control and lane keeping, two automotive control problems that present both safety and performance considerations coupled with actuator bounds.

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an implicit feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an explicit function of the state and reference vectors. However, MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].

PSO and Improved BSFS Based Sequential Comprehensive Placement and Real-Time Multi-Objective Control of Delta-Connected Switched Capacitors in Unbalanced Radial MV Distribution Networks

Delta-connected switched capacitors are common in three-wire MV distribution systems as found in Europe and Australia but the majority of past studies focus on star-connected capacitors as applied in North America. This study presents a comprehensive optimization based sequential strategy and a multi-objective optimization based real-time strategy for the optimal placement and control of delta-connected switched capacitors. Considering load variations, a comprehensive optimization for capacitor placement is formulated to maximize the net annual returns from network loss reduction and capacity release. A sequential strategy based on loss sensitivity analysis is presented for efficient capacitor placement on large-scale unbalanced distribution networks. Then a capacitor control strategy is proposed to simultaneously minimize network losses, voltage deviations and voltage unbalance factor (VUF) while a weighted sum method reflects the preferences on the mutually conflicting control objectives. For the mixed integer programming (MIP) problems of capacitor placement and control, an improved backward/forward sweep (BSFS) load flow based particle swarm optimization (PSO) method is used. Finally, the robustness of the proposed capacitor placement and control strategies are verified by comparison against existing popular schemes through detailed simulations over 24 h on a real unbalanced Australian MV network.

Multi-objective nonlinear predictive control of process systems: A dual-mode tracking control approach

There typically exist different and often conflicting control objectives, e.g., reference tracking, robustness and economic performance, in many chemical processes. The current work considers the multi-objective control problems of continuous-time nonlinear systems subject to state and input constraints and multiple conflicting objectives. We propose a new multi-objective nonlinear model predictive control (NMPC) design within the dual-mode paradigm, which guarantees stability and constraint satisfaction. The notions of utopia point and compromise solution are used to reconcile the confliction of the multiple objectives. The designed controller minimizes the distance of its cost vector to a vector of independently minimized objectives, i.e., the steady-state utopia point. Recursive feasibility is established via a particular terminal region formulation while stabilizing the closed-loop system to the compromise solution via the dual-mode control principle. In order to derive the terminal region as large as possible, a terminal control law with free-parameters is constructed by using the control Lyapunov functions (CLFs) technique. Two examples of multi-objective control of a CSTR and a free-radical polymerization process are used to illustrate the effectiveness of the new multi-objective NMPC and to compare their performance.

On the Mixed <inline-formula><tex-math notation="TeX">${{\rm H}_2}/{{\rm H}_\infty }$</tex-math></inline-formula> Loop-Shaping Tradeoffs in Fractional-Order Control of the AVR System

This paper looks at frequency domain design of a fractional-order (FO) proportional-integral differential (PID) controller for an automatic voltage regulator (AVR) system. Various performance criteria of the AVR system are formulated as system norms and are then coupled with an evolutionary multiobjective optimization (MOO) algorithm to yield Pareto optimal design tradeoffs. The performance measures consist of mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="TeX">${\bf H}_{\bf 2}/{{\bf H}_\infty}$</tex-math></inline-formula> design objectives, such as the set-point tracking, load disturbance, and noise rejection controller effort, which are an exhaustive set of conflicting control objectives. A fuzzy logic-based mechanism is used to identify the best compromise solution on the Pareto fronts. The advantages and disadvantages of using an FOPID controller over the conventional PID controller, which are popular for industrial use, are enunciated from the presented simulations. The relevance and impact of FO controller design from the perspective of the dynamics of AVR control loop is also discussed.

Tuning of NMPC controllers via multi-objective optimisation

Nonlinear Model Predictive Control (NMPC) is a powerful technique that can be used to control many industrial processes. Different and often conflicting control objectives, e.g., reference tracking, disturbance rejection and minimum control effort, are typically present. Most often these objectives are translated into a single weighted sum (WS) objective function. This approach is widespread because it is easy to use and understand. However, selecting an appropriate set of weights for the objective function is often non-trivial and is mainly done by trial and error. The current study proposes a systematic procedure for tuning Nonlinear MPC based on multi-objective optimisation methods. Advanced methods allow an efficient solution of the multi-objective problem providing a systematic overview of the controller behaviour. Moreover, through analytic relations it is possible to link a solution obtained with these novel methods to a set of weights for a weighted sum objective function. Applying this set of weights causes the WS to generate the same solution as obtained with the advanced method. Hence, an appropriate controller can be selected based on the alternatives generated by the advanced method, while the corresponding weights for a WS can be derived for implementing the controller in practice. The procedure is successfully tested on two benchmark applications: the Van de Vusse reactor and the Tennessee Eastman plant.

Adaptive Control of a Solid Oxide Fuel Cell Ultra-Capacitor Hybrid System

Solid oxide fuel cells (SOFCs) offer a number of advantages beyond those of most other fuel cells. However, like other fuel cells, rapid load following is difficult, and can lead to fuel starvation and consequently fuel cell damage. Mitigating fuel starvation and improving load following capabilities are conflicting control objectives. However, the issue can be addressed by the hybridization of the system with an energy storage device. In this paper, a steady-state property of the SOFC, combined with a current regulation strategy, is used to manage transient fuel utilization and thereby address fuel starvation. Meanwhile, an overall system strategy is employed to manage energy sharing in the hybrid system for load following as well as for maintaining the state-of-charge of the energy storage device. This work presents an adaptive control algorithm that uses online parameter estimation to update the controller. The control design is validated on a hardware-in-the-loop setup and experimental results are provided.