Market power and strategic interaction in electricity networks

Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

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A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Stochastic linear Model Predictive Control with chance constraints – A review

This paper implements robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs). The problem involves vehicles with independent dynamics but with coupled constraints to capture required cooperative behavior. Using a recently-developed form of DMPC, each vehicle plans only for its own actions, but feasibility of the sub-problems and satisfaction of the coupling constraints are guaranteed throughout, despite the action of unknown but bounded disturbances. UAVs communicate relevant plan data to ensure that decisions are consistent across the team. Collision avoidance is used as an example of coupled constraints and the paper shows how the speed, turn rate and avoidance distance limits in the optimization should be modified in order to guarantee robust constraint satisfaction. Integer programming is used to solve the non-convex problem of path-planning subject to avoidance constraints. Numerical simulations compare computation time and target arrival time under decentralized and centralized control and investigate the impact of decentralization on team performance. The results show that the computation required for DMPC is significantly lower than for its centralized counterpart and scales better with the size of the team, at the expense of only a small increase in UAV flight times.

Decentralized model predictive control of co-operating UAVs

This paper presents a tracking control method for the lateral motion of an autonomous land vehicle (ALV). This method is based on active disturbance rejection control (ADRC) scheme and differential flatness theory. The lateral motion is hard to control since it is underactuated, nonlinear, and with large uncertainties. By making a small-angle approximation, the dynamic model is linearized. The flatness of the linear model is proved and a flat output is found. An equivalent form of the model is obtained based on the flat output and its derivatives only. Moreover, an ADRC is adopted to guarantee both control accuracy and strong robustness. Simulation results are presented and the results show the effectiveness of the control strategies.

Lateral Path Tracking Control of Autonomous Land Vehicle Based on ADRC and Differential Flatness

The paper is concerned with the problem of distributed model predictive control (DMPC) for formation of multiple linear second-order agents with collision avoidance and obstacle avoidance. All the agents are permitted to implement optimization simultaneously at each time step. The assumed input trajectory and state trajectory are introduced to obtain a computationally tractable optimization problem in a distributed manner. As a result, a compatibility constraint is required to ensure the consistency between each agent׳s real operation and its plan and to establish the agreement among agents. The terminal ingredients are tailored by making use of the specific form of the system model and the control objective. The terminal set is ensured to be positively invariant with the designed terminal controller. The collision avoidance constraint and the obstacle avoidance constraint are satisfied for any state in the terminal set. The weighted matrix of the terminal cost is determined by solving a Lyapunov equation. Moreover, recursive feasibility of the resulting optimization problem is guaranteed and closed-loop stability of the whole system is ensured. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance

This paper develops a disturbance rejection model predictive control (MPC) scheme for tracking nonholonomic vehicle with coupled input constraint and matched disturbances. Two disturbance observers (DOBs) are designed to estimate the unknown disturbances and the disturbances with known harmonic frequencies, respectively. By combining the DOB with MPC, a compound controller is presented. Recursive feasibility of the optimization problem is guaranteed by tightening the terminal region and the input constraint. We show that the system is input-to-state stable if no information about the disturbance is available and can reach an offset-free tracking performance if the harmonic frequencies of the disturbance are known. Finally, simulations and experiments are provided to show the efficiency of the proposed approaches.

Disturbance Rejection MPC for Tracking of Wheeled Mobile Robot

This paper studies the event-based model predictive control (EMPC) for tracking of nonholonomic mobile robot with coupled input constraint and bounded disturbances. First, an event-triggering mechanism is presented by designing a threshold for the error between the actual trajectory and the predicted one, aiming at reducing the computational load. Second, a model predictive control strategy is developed based on the event-triggering mechanism. Recursive feasibility is guaranteed by designing a robust terminal region and the proper parameters. We show that the tracking system is practically stable and also provides a convergence region for the tracking error. The convergence region indicates that the tracking performance is negatively related to the minimal interevent time as well as the bound of the disturbances. Finally, simulation results show that the computation load is significantly reduced and illustrate the efficiency of our proposed strategy.

Event-Based Model Predictive Tracking Control of Nonholonomic Systems With Coupled Input Constraint and Bounded Disturbances

The problem of position tracking for a tank gun control system with inertia uncertainty and external disturbance is investigated in this paper. The tank gun control system, demanding high tracking precision and stabilization precision, is a nonlinear system. Classical control methods are commonly used in the actual system, which is difficult to ensure high precision and high disturbance rejection capability. An active disturbance rejection control (ADRC) scheme is applied to guarantee the state variables of the closed loop system to converge to the reference state with the help of the extended state observer by estimating the inertia uncertainty and external disturbance. The basic theory of the ADRC is introduced here. According to the mathematical model, the parameters of ADRC are designed. Also, simulation results show that ADRC controller has advantages of high precision and high disturbance rejection ability. A comparison between ADRC and PID is also presented to show the effectiveness of the ADRC control strategy.

Li Dai

Papers

Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance

Disturbance Rejection MPC for Tracking of Wheeled Mobile Robot

Event-Based Model Predictive Tracking Control of Nonholonomic Systems With Coupled Input Constraint and Bounded Disturbances

Application of active disturbance rejection control in tank gun control system

Robust MPC for tracking constrained unicycle robots with additive disturbances