Contents Contributors Preface Acknowledgments Acronyms 1 Introduction 1.1 Background and motivation 1.2 Model predictive control 1.3 MPC in the presence of uncertainties 1.4 Event-based MPC 1.5 Book outline 2 Two-phase event-based MPC for continuous-time systems 2.1 Problem statement 2.2 Two-phase event-triggered MPC algorithm 2.

2.1 Optimiaztion formulation 2.2.2 Event based α − β strategy 2.2.3 Generalization of the α − β Strategy 2.3 Feasibility and Stability 2.3.

1 Recursive feasibility of optimization 2.3.2 Stability of the closed-loop system 2.4 Simulation examples 2.4.1 Undamped Oscillator 2.4.2 Simplified spring-damper element in vehicle suspension system 2.

5 Conclusion 3 Event-Triggered MPC for Discrete-Time Systems with Aperiodic Sampling 3.1 Problem statement 3.1.1 Plant to be controlled 3.1.2 Formulation of optimization 3.2 Aperiodic triggering mechanism 3.2.

1 Triggering mechanism 3.2.2 Stability and feasibility 3.3 Improved Aperiodic Triggering Mechanism 3.3.1 Statement of improved triggering mechanism 3.3.2 Feasibility and stability concerning the improved aperiodic triggering mechanism 3.

4 A simulation example 3.5 Conclusion 4 Composite Event-Triggered MPC based on Disturbance Compensation 4.1 Problem Statement 8 4.2 Composite event-triggered MPC mechanism 4.2.1 Disturbance Compensation Controller Design 4.2.2 Model predictive controller design 4.

2.3 Composite event-triggered MPC 4.2.4 Event-triggered mechanism with estimation 4.3 Feasibility and stability 4.3.1 Recursive feasibility 4.3.

2 Closed-loop stability 4.4 A simulation example 4.5 Conclusion 5 Event-Triggered MPC with Periodic Sampling for Multi-Agent Systems 5.1 Problem statement 5.2 Distributed MPC design 5.2.1 Terminal set and auxiliary terminal control design 5.2.

2 Distributed MPC framework 5.3 Periodic event-triggering mechanism design 5.4 Feasibility and stability 5.5 A simulation example 5.6 Conclusion 6 Concluding remarks and future directions Bibliography Contents Contributors Preface Acknowledgments Acronyms 1 Introduction 1.1 Background and motivation 1.2 Model predictive.