Combination Of Model Predictive Control Research Articles

Combined economic and regulatory predictive control

A combined economic and regulatory Model Predictive Control (MPC) framework, referred to as dual-objective MPC, is presented. Economic and regulatory cost objectives are combined using an explicitly defined dynamic weight function. The features embedded in the weight function allow for increased economic online performance during process transients, while simultaneously steering the process to an optimal economic steady-state. The proposed strategy gives an online optimization problem of same type as typical nonlinear MPC formulations, with the same number of variables. Increased economic transient performance is illustrated for an acetylene hydrogenation case, when compared to regulatory MPC. A link to dissipativity theory for the presented dual-objective MPC strategy concludes the work.

Solving Multi-UAV Dynamic Encirclement via Model Predictive Control

In order for teams of unmanned aerial vehicles (UAVs) to collaborate and cooperate to perform challenging group tasks, intelligent and flexible control strategies are required. One of the complex behaviors required of a team of UAVs is dynamic encirclement, which is a tactic that can be employed for persistent surveillance and/or to neutralize a target by restricting its movement. This tactic requires a high level of cooperation such that the UAVs maintain a desired and proper encirclement radius and angular velocity around the target. In this paper, model predictive control (MPC) is used to model and implement controllers for the problem of dynamic encirclement. The linear and nonlinear control policies proposed in this paper are applied as a high-level controller to control multiple UAVs to encircle a desired target in simulations and real-time experiments with quadrotors. The nonlinear solution provides a theoretical analysis of the problem, while the linear control policy is used for real-time operation via a combination of MPC and feedback linearization applied to the nonlinear UAV system. The contributions of this paper lie in the implementation of MPC to solve the problem of dynamic encirclement of a team of UAVs in real time and the application of theoretical stability analysis to the problem.

A Two-Stage Design of Two-Dimensional Model Predictive Iterative Learning Control for Nonrepetitive Disturbance Attenuation

Combining model predictive control (MPC) with iterative learning control (ILC) is a widely applied strategy in controlling repetitive processes. Currently, there are two approaches for designing such a controller, namely the separate design and the integrated design. The separate design has better performance on nonrepetitive disturbance rejection, while the integrated design has faster convergent rate. In this paper, by borrowing the idea of the two-stage optimization, a new design has been proposed for enhancing the integrated control of combining MPC with ILC. In this way, good performance and fast convergent rate can be obtained simultaneously. Simulations are given to illustrate the effectiveness of the proposed algorithm on disturbance attenuation.

Power Admission Control With Predictive Thermal Management in Smart Buildings

This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First, the power budget with a given thermal comfort constraint is optimized through budget-schedulability analysis which amounts to solving a constrained linear programming problem. Second, the effective peak power demand is reduced by means of the optimal scheduling and cooperative operation of multiple thermal appliances. The performance of the proposed control scheme is assessed by simulation based on the thermal dynamics of a real eight-room office building located at Danish Technical University.

Combining Model Predictive Control with Constraint-satisfaction Formulation for the Operative Pumping Control in Water Networks

This paper proposes a method to combine linear Model Predictive Control (MPC), a Constraint Satisfaction Problem (CSP) formulation and a Network Aggregation Method (NAM) for the predictive operational control of water pumping in DWNs. The proposed method can produce optimal pumping strategies for complex DWNs in short computation times, while avoiding the need for non- linear programming techniques to cater for non-linear flow-head equations. The proposed approach is simulated using Epanet to represent the hydraulic DWNs. The D-Town benchmark water network is used as a case study.

Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

This paper presents a novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation of classical artificial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation of evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear model predictive control scheme. Real-time avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.

State Constrained Model Predictive Static Programming: A Slack Variable Approach

Abstract An incorporation of state variable inequality constraint for well established Model Predictive Static Programming(MPSP) is proposed in this work. A transformation technique by using slack variables to transform the State Variable Inequality Constraints into an equality constraints, and thereby converting the constraint control problem into an unconstraint one of higher dimension, is incorporated into MPSP. MPSP is a suboptimal control design technique which is applicable for finite horizon nonlinear problems with terminal constraints for class of optimal nonlinear control problems. MPSP is basically a combination of model predictive control and approximate dynamic programming. MPSP is inherently silent on handling the state variable inequality constraint along the trajectory and no augmentation has been proposed till date to handle the same. In this work, we propose an augmentation to handle the state variable inequality constraint in MPSP framework by using slack variable transformation technique. Control problem with state variable inequality constraint is first transformed into an unconstrained problem, then it is formulated in MPSP framework, with q th time derivative of slack variables as control variable. The proposed philosophy, for verification purpose, is tried on air to air missile guidance problem through simulations. State variable inequality constraint on missile is to keep its altitude within certain limit. It is observed from the extensive simulations that State Constrained MPSP works better as compared to MPSP in terms of keeping the altitude of the missile within constraint.

A New Nonlinear Suboptimal Control Design Approach for Milling Circuits

A combined nonlinear model predictive control and nonlinear dynamic inversion design approach is proposed as a new effective control design philosophy for complex milling circuits. Nonlinear interaction between various states in the mill and the sump as well as significant time delays make the milling circuit difficult to control. The proposed innovative approach, which uses a suboptimal nonlinear model predictive control for the overall circuit with a fast acting dynamic inversion control for controlling the sump level, addresses the ambitious objectives of high product quality, high throughput, manageable loads in the mill as well as minimum power consumption.

ON/OFF strategy based minimum-time control of continuous Petri nets

This paper focuses on the target marking control problem of timed continuous Petri nets (TCPN), aiming to drive the system from an initial state to a desired final one. This problem is similar to the set-point control problem in a general continuous-state system. In a previous work, a simple and efficient ON/OFF controller was proposed for Choice-Free nets, and it was proved to be minimum-time (Wang, 2010). However, for general TCPN the ON/OFF controller may bring the system to “blocking” situations due to its “greedy” firing strategy, and the convergence to the final state is not ensured. In this work the ON/OFF controller is extended to general TCPN by adding more “fair” strategies to solve conflicts in the system: the ON/OFF+ controller is obtained by forcing proportional firings of conflicting transitions. Nevertheless, such kind of controller might highly slow down the system when transitions have flows of different orders of magnitude, therefore a balancing process is introduced, leading to the B-ON/OFF controller. A third approach introduced here is the MPC-ON/OFF controller, a combination of Model Predictive Control (MPC) and the ON/OFF strategy; it may achieve a smaller number of time steps for reaching the final states, but usually requires more CPU time for computing the control laws. All the proposed extensions are heuristic methods for the minimum-time control and their convergences are proved. Finally, an application example of a manufacturing cell is considered to illustrate the methods. It is shown that by using the proposed controllers, reasonable numbers of time steps for reaching the final state can be obtained with low computational complexity.

Rejection of Periodic Disturbances Based on Adaptive Repetitive Model Predictive Control

The paper presents an adaptive strategy to reject periodic disturbances with unknown period based on a combination of model predictive control and repetitive control. A novel period estimator is presented. For the integer period case, the estimator is designed based on integer programming. For the non-integer period case, it is designed based on a two-step optimization, namely integer programming followed by a constrained least square method. With the estimated period, feedforward compensation is made to improve the tracking performance asymptotically. Simulation results are given to show the effectiveness of the algorithm.

Decentralized-coordinated model predictive control for a hydro-power valley

This paper aims at improving control systems for hydro-power production, by combining model predictive control techniques with decomposition–coordination methods for a global optimization over a whole hydro-power valley. It first recalls the model predictive control formulation for a centralized solution presented as the reference for comparison, and emphasizes the possible use of explicit solutions in the considered problem, making easier its practical use. It then highlights the further interest of such solutions in a decomposition–coordination approach, allowing to reduce the computational cost even more with a purpose of real-time implementation, and at the same time to take advantage of the distributed nature of the considered system. The results are illustrated on the basis of a real-data-based case-study provided by EDF11Electricité de France. group.

Model Predictive Pulse Pattern Control

Industrial applications of medium-voltage drives impose increasingly stringent performance requirements, particularly with regard to harmonic distortions of the phase currents of the controlled electrical machine. An established method to achieve very low current distortions during steady-state operation is to employ offline calculated optimized pulse patterns (OPP). Achieving high dynamic performance, however, proves to be very difficult in a system operated by OPPs. In this paper, we propose a method that combines the optimal steady-state performance of OPPs with the very fast dynamics of trajectory tracking control. A constrained optimal control problem with a receding horizon policy, i.e., model predictive control (MPC), is formulated and solved. Results show that the combination of MPC with OPPs satisfies both the strict steady-state as well as the dynamic performance requirements imposed by the most demanding industrial applications. The estimation of the fundamental components of the machine variables separately from their respective harmonic components is not required. As a result, complicated structures such as observers can be avoided, contrary to state-of-the-art methods. A further advantage of the MPC method is the use of a receding horizon policy, which provides feedback and a high degree of robustness.

Predictive Control Allocation for a Thermal Management System Based on an Inner Loop Reference Model—Design, Analysis, and Experimental Results

This paper addresses the challenge of controlling an overactuated engine thermal management system where two actuators, with different dynamic authorities and saturation limits, are used to obtain tight temperature regulation. A modular control strategy is proposed that combines model predictive control allocation (MPCA) with the use of an inner loop reference model. This results in an inner loop controller that closely matches a dynamic specification for input-output performance while addressing actuator dynamics and saturation constraints. This paper presents the design and implementation strategy and illustrates the effectiveness of the proposed solution through real-time simulation and experimental results.

Ship motion stabilizing control using a combination of model predictive control and an adaptive input disturbance predictor

When ships travel on the high seas, changes in the sea states and the sailing conditions induce significant uncertainties in the hydrodynamics, leading to deterioration in the performance of traditional stabilization systems. To overcome this problem, a combination of model predictive control (MPC) and an adaptive input disturbance predictor is proposed. This combination predicts the wave disturbance force by using an auto-regressive model of the input disturbance and then compensating for the predicted disturbance in the MPC framework. MPC is more appropriate than classical control when dealing with constraints. The adaptive disturbance model enhances not only the ship adaptability when travelling in varying sea conditions but also the robustness of the control system with model uncertainties. This combines the advantages of MPC and the adaptive model, and avoids performance degradations caused by estimation errors of the state observer which is commonly used within the MPC framework to estimate output disturbances. A numerical simulation shows that this combination works very well.

Networked Predictive Control of Uncertain Constrained Nonlinear Systems: Recursive Feasibility and Input-to-State Stability Analysis

In this paper, the robust state feedback stabilization of uncertain discrete-time constrained nonlinear systems in which the loop is closed through a packet-based communication network is addressed. In order to cope with model uncertainty, time-varying transmission delays, and packet dropouts (typically affecting the performances of networked control systems), a robust control scheme combining model predictive control with a network delay compensation strategy is proposed in the context of non-acknowledged UDP-like networks. The contribution of the paper is twofold. First, the issue of guaranteeing the recursive feasibility of the optimization problem associated to the receding horizon control law has been addressed, such that the invariance of the feasible region under the networked closed-loop dynamics can be guaranteed. Secondly, by exploiting a novel characterization of regional Input-to-State Stability in terms of time-varying Lyapunov functions, the networked closed-loop system has been proven to be Input-to-State Stable with respect to bounded perturbations.

Reference governors for controlled belt restraint systems

Today's restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. For the class of real-time controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. This paper presents a novel control strategy for these systems. The control strategy developed here is based on a combination of model predictive control and reference management, in which a non-linear device – a reference governor (RG) – is added to a primal closed-loop controlled system. This RG determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimisation problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on past crash data, using linear regression techniques. Simulation results with MADYMO models show that, with ideal sensors and actuators, a significant reduction (45%) of the peak chest acceleration can be achieved, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented online.

Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell

AbstractA novel combination of model predictive control (MPC) and iterative learning control (ILC), referred to learning‐type MPC (L‐MPC), is proposed for closed‐loop control in an artificial pancreatic β‐cell. The main motivation for L‐MPC is the repetitive nature of glucose‐meal‐insulin dynamics over a 24‐h period. L‐MPC learns from an individual's lifestyle, inducing the control performance to improve from day to day. The proposed method is first tested on the Adult Average subject presented in the UVa/Padova diabetes simulator. After 20 days, the blood glucose concentrations can be kept within 68–145 mg/dl when the meals are repetitive. L‐MPC can produce superior control performance compared with that achieved under MPC. In addition, L‐MPC is robust to random variations in meal sizes within ±75% of the nominal value or meal timings within ±60 min. Furthermore, the robustness of L‐MPC to subject variability is validated on Adults 1–10 in the UVa/Padova simulator. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Input Constraints Handling in an MPC/Feedback Linearization Scheme

Input Constraints Handling in an MPC/Feedback Linearization SchemeThe combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

A combined model predictive control and time series forecasting framework for production-inventory systems

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.

Efficient Low-cost Controllers for Constrained Manipulators with Uncertainties and Disturbances

This work presents an efficient control strategy for robot manipulators with constraints and affected by uncertainties and disturbances. The controller uses a combination of model predictive control (MPC) with an adaptive robust feedforward term. The predictive controller is based on interpolations between different simple solutions to guarantee the feasibility of the final solution applied to the manipulator. The proposed method improves the existing techniques in terms of robust capabilities. Feasibility is preserved with the MPC and applicability is also guaranteed as the computational load of the interpolation algorithm is low. The benefits of the strategy, compared with other existing controllers, are shown with simulation results obtained with a PUMA-560 manipulator.