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We present a novel distributed primal-dual active-set method for model predictive control. The primaldual active-set method is used for solving model predictive control problems for large-scale systems with quadratic cost, linear dynamics, additive disturbance, and box constraints. The proposed algorithm is compared with dual decomposition and an alternating direction method of multipliers. Theoretical and experimental results show the effectiveness of the proposed approach for large-scale systems with communication delays. The application to building control systems is thoroughly investigated. [ABSTRACT FROM AUTHOR]

This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equality-based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC. [ABSTRACT FROM PUBLISHER]

Marine vessels with dynamic positioning (DP) capability are typically equipped with sufficient number of thrusters to make them overactuated and with satellite navigation and other sensors to determine their position, heading, and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a DP control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider a model-predictive control (MPC) algorithm that combines positioning control and TA into a single algorithm, which should theoretically yield a near-optimal controller output. This paper explores the advantages and disadvantages of using MPC compared with the traditional algorithms. [ABSTRACT FROM PUBLISHER]

We propose a learning-based driver modelling approach which can identify manoeuvres performed by drivers on the highway and predict the future driver inputs. We show how this approach can be applied to provide personalised driving assistance. In a first example, the driver model is used to predict unintentional lane departures and a model predictive controller is used to keep the car in the lane. In a second example, the driver model estimates the preferred acceleration of the driver during lane keeping, and a model predictive controller is implemented to provide a personalised adaptive cruise control. For both applications, we use a combination of real data and simulation to evaluate the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests. [ABSTRACT FROM AUTHOR]

We present the design of an analog circuit which solves linear programming (LP) or quadratic programming (QP) problem. In particular, the steady-state circuit voltages are the components of the LP (QP) optimal solution. The paper shows how to construct the circuit and provides a proof of equivalence between the circuit and the LP (QP) problem. The proposed method is used to implement an LP-based Model Predictive Controller by using an analog circuit. Simulative and experimental results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the control of distributed storage networks with guarantees of constraints satisfaction and asymptotic stability. We consider two problems: network capacity maximization and network balancing. In the first part of the paper we describe the two problems, highlight their importance in a wide number of engineering applications, and compare them by analyzing the properties of their solutions. In the second part we present algorithms for solving both problems by using a convex one-step model predictive controller (MPC) which guarantees persistent state and flow constraints satisfaction. We present simple conditions which link the network topology, the MPC weights and the asymptotic stability of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approach. [Copyright &y& Elsevier]

We study the problem of heating, ventilation and air conditioning (HVAC) control in a typical commercial building. We propose a model predictive control (MPC) approach which minimizes energy cost while satisfying occupant comfort and control actuator constraints, using a simplified system model and incorporating predictions of future weather and occupancy inputs. In simplified physics-based models of HVAC systems, the product between air temperatures and flow rates arising from energy balance equations leads to a non-convex MPC problem. Fast computational techniques for solving non-convex optimization can only provide certificates of local optimality. Local optima can potentially cause MPC to have worse performance than existing control implementations, so deserve careful consideration. The objective of this article is to investigate the phenomenon of local optima in the MPC optimization problem for a simple HVAC system model. In the first part of the article, simplified physics-based models and MPC design for two common HVAC configurations are introduced. In the second part, simulation results exhibiting local optima for both configurations are presented. We perform a detailed analysis on the different types of local optima and their physical interpretation. We then use this analysis to derive physics-based rules to exclude classes of locally optimal control sequences under specific conditions. [ABSTRACT FROM AUTHOR]

This brief presents a model-based predictive control (MPC) approach to building cooling systems with thermal energy storage. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. First, simplified models of chillers, cooling towers, thermal storage tanks, and buildings are developed and validated for the purpose of model-based control design. Then an MPC for the chilling system operation is proposed to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy. The controller is experimentally validated at the University of California, Merced. The experiments show a reduction in the central plant electricity cost and an improvement of its efficiency. [ABSTRACT FROM PUBLISHER]

Abstract: Finite-time optimal control problems with quadratic performance index for linear systems with linear constraints can be transformed into Quadratic Programs (QPs). Model Predictive Control requires the on-line solution of such QPs. This can be obtained by using a QP solver or evaluating the associated explicit solution. The objective of this note is twofold. First, we shed some light on the computational complexity and storage demand of the two approaches when an active set QP solver is used. Second, we show the existence of alternative algorithms with a different tradeoff between memory and computational time. In particular, we present an algorithm which, for a certain class of systems, outperforms standard explicit solvers both in terms of memory and worst case computational time. [Copyright &y& Elsevier]

Abstract: This work addresses the problem of offset-free Model Predictive Control (MPC) when tracking an asymptotically constant reference. In the first part, compact and intuitive conditions for offset-free MPC control are introduced by using the arguments of the internal model principle. In the second part, we study the case where the number of measured variables is larger than the number of tracked variables. The plant model is augmented only by as many states as there are tracked variables, and an algorithm which guarantees offset-free tracking is presented. In the last part, offset-free tracking properties for special implementations of MPC schemes are briefly discussed. [Copyright &y& Elsevier]

In this paper, we propose a path following Model Predictive Control-based (MPC) scheme utilising steering and braking. The control objective is to track a desired path for obstacle avoidance manoeuvre, by a combined use of braking and steering. The proposed control scheme relies on the Nonlinear MPC (NMPC) formulation we used in [F. Borrelli, et al., MPC-based approach to active steering for autonomous vehicle systems, Int. J. Veh. Autonomous Syst. 3(2/3/4) (2005), pp. 265-291.] and [P. Falcone, et al., Predictive active steering control for autonomous vehicle systems, IEEE Trans. Control Syst. Technol. 15(3) (2007), pp. 566-580.]. In this work, the NMPC formulation will be used in order to derive two different approaches. The first relies on a full tenth-order vehicle model and has high computational burden. The second approach is based on a simplified bicycle model and has a lower computational complexity compared to the first. The effectiveness of the proposed approaches is demonstrated through simulations and experiments. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads. [ABSTRACT FROM AUTHOR]

Abstract: We present a detailed study on the design of decentralized receding horizon control (RHC) schemes for decoupled systems. We formulate an optimal control problem for a set of dynamically decoupled systems where the cost function and constraints couple the dynamical behavior of the systems. The coupling is described through a graph where each system is a node, and cost and constraints of the optimization problem associated with each node are only function of its state and the states of its neighbors. The complexity of the problem is addressed by breaking a centralized RHC controller into distinct RHC controllers of smaller sizes. Each RHC controller is associated with a different node and computes the local control inputs based only on the states of the node and of its neighbors. We analyze the properties of the proposed scheme and introduce sufficient stability conditions based on prediction errors. Finally, we focus on linear systems and show how to recast the stability conditions into a set of matrix semi-definiteness tests. [Copyright &y& Elsevier]

Buildings that use radiant slab systems with evaporative cooling sources have shown to be energy efficient. However, control of the systems is challenging because of the slow response of the slab and the limited capacity of cooling sources. The objectives of this paper are to: (1) create a simplified dynamic model of radiant slab system for implementation in real-time model predictive controller (MPC); and (2) test the MPC energy and thermal comfort performance in a case study building. A calibrated EnergyPlus model of the building was developed as the testbed. The MPC is compared with the existing rule-based control method for a cooling season in a dry and hot climate. The results indicated that the MPC controller was able to maintain zone operative temperatures at EN 15251 Category II level more than 95% of the occupied hours for all zones, while with the rule-based method, only the core zone were maintained at this thermal comfort level. Compared to the rule-based method, MPC reduced the cooling tower energy consumption by 55% and pumping power consumption by 25%. [ABSTRACT FROM AUTHOR]