We consider constrained Model Predictive Control (MPC) as a local control law for application in coordinated control of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. Restricted communication bandwidth is available and forces the subsystems to avail themselves of limited-quality estimates of their neighbors’ states in computing this con...

In this paper, we present in a tutorial fashion the implementation of Model Predictive Control (MPC) on a virtual model of the modified quadruple tank system. The modified quadruple tank system is an example of a multi-input-multi-output (MIMO) system with known and unknown disturbances. Model Predictive Control is well known for its ability to deal with multivariable and complex control proble...

This paper investigates the problem of designing data-driven stochastic Model Predictive Control (MPC) for linear time-invariant systems under additive disturbance, whose probability distribution is unknown but can be partially inferred from data. We propose a novel online learning based risk-averse MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are require...

A neural model-based predictive control scheme is proposed for dealing with steady-state offsets found in standard MPC schemes. This structure is based on a constrained local instantaneous linear model-based predictive control methodologies together with a static offset pre-filter for assuring free tracking errors and disturbance rejection features. A non-linear state-space neural network archi...

State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. The periodic systems’ states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, bounded and subject to periodically time-dependent bounds. The objective is to designMPC laws that robustly enforce constraint satisfaction in...

A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA...

This work presents a fault-tolerant flight control system using model predictive control (MPC). The proposed technique, named feasible target-tracking MPC, filters the reference demand to guarantee feasibility of the constrained optimization. This architecture is also capable of redistributing, in a stable manner, the control efforts among healthy actuators, respecting their limitations. A traj...

The goal of multi-parametric quadratic programming (mpQP) is to compute analytic solutions to parameter-dependent constrained optimization problems, e.g., in the context of explicit linear MPC. We propose an improved combinatorial mpQP algorithm that is based on implicit enumeration of all possible optimal active sets and a simple saturation matrix pruning criterion which uses geometric propert...

Two formulations of the stochastic model predictive control (SMPC) problem for the control of large-scale drinking water networks are presented in this chapter. The first approach, named chance-constrained MPC, makes use of the assumption that the uncertain future water demands follows some known continuous probability distribution while at the same time certain risk (probability) for the state...