Robust Nonlinear Model Predictive Control with Model Predictive Sliding Mode for Continuous-Time Systems

This paper presents a robust, tube-based nonlinear model predictive controller for continuous-time systems with additive disturbances which cascades two sampled-data model predictive controllers: the first creates a desired path using nominal dynamics and the second maintains the true state close to the nominal state by regulating a sliding variable designed on the error between the true and nominal states. The sampled-data model predictive approach permits easy incorporation of continuous-time sliding mode dynamics, allowing a dynamic boundary layer and tube design to be included. In this way, the control applied to the system capitalizes on the robustness properties of traditional sliding mode control while incorporating system constraints. Stability analysis is presented in the context of input-to-state stability for continuous-time systems. The proposed controller is implemented on two case studies, is compared to benchmark tube-based model predictive controllers, and is evaluated using average root mean square values on the state and input variables, in addition to average integral square and integral absolute error values on the position states. Results reveal the proposed technique responds to higher levels of disturbance with significant increases in control effort; eliminates constraint violation by using of constrained SMC as the secondary controller; and maintains similar tracking performance to benchmark controllers at lower levels of control effort.

Joint voltage and frequency predictive controllers for distributed generation plants

The paper determines the effect of proposed joint voltage and frequency predictive controllers for distributed generation (DG) plants on quality indicators characterizing the control process in different operating modes of power supply systems. The studies are conducted in the MatLab environment (Simulink and SimPowerSystems simulation packages) employing control engineering methods. It is proposed to design and adjust joint predictive controllers by determining the resonant frequency of oscillations for the master generator rotor. This approach provides better quality indicators of voltage and frequency control in power supply systems while maintaining the same settings for the controllers of DG plants. With an additional load in an isolated power supply system, the maximum voltage sag is found to be 1.75 times lower than for local predictive control and 3.5 times lower as compared to the use of conventional controllers. For the specified mode, predictive controllers enable a threefold reduction in the transient time between rotor rotational speeds in a synchronous generator. In the start mode of a powerful electric motor, the predictive controllers of synchronous generators in the power supply system enable a 1.5 times reduction in voltage sag, with a 1.4 times reduction in overvoltage following its start. In the case of a short-term three phase short-circuit, joint predictive controllers allow a 1.5 times decrease in transient time and a 2.3 times decrease in the overshoot of power line frequency as compared to local control. In addition, frequency oscillation in the power system is also reduced. Similar effects are observed in other operating modes of the considered power supply systems equipped with DG plants. The performed dynamic simulation confirms the effectiveness of using joint voltage and frequency predictive controllers for DG plants, which consists in a positive impact on the quality of processes involved in controlling the parameters of power supply systems in various operating modes.

Can Deep Models Help a Robot to Tune Its Controller? A Step Closer to Self-tuning Model Predictive Controllers

Motivated by the difficulty roboticists experience while tuning model predictive controllers (MPCs), we present an automated weight set tuning framework in this work. The enticing feature of the proposed methodology is the active exploration approach that adopts the exploration–exploitation concept at its core. Essentially, it extends the trial-and-error method by benefiting from the retrospective knowledge gained in previous trials, thereby resulting in a faster tuning procedure. Moreover, the tuning framework adopts a deep neural network (DNN)-based robot model to conduct the trials during the simulation tuning phase. Thanks to its high fidelity dynamics representation, a seamless sim-to-real transition is demonstrated. We compare the proposed approach with the customary manual tuning procedure through a user study wherein the users inadvertently apply various tuning methodologies based on their progressive experience with the robot. The results manifest that the proposed methodology provides a safe and time-saving framework over the manual tuning of MPC by resulting in flight-worthy weights in less than half the time. Moreover, this is the first work that presents a complete tuning framework extending from robot modeling to directly obtaining the flight-worthy weight sets to the best of the authors’ knowledge.

Start-up system design for small scale autonomous DFIG wind turbine

Autonomous wind systems are becoming promising since they provide new and innovative solutions to non-electrified areas. Start-up procedures for this wind systems requires some care to ensure electricity availability, to ovoid overloads and to reduce electrical and mechanical stress that affect lifetime of system components. In this context, this paper deals with an enhanced start-up system for small scale autonomous wind systems based on doubly fed induction generator. With the proposed start-up system, transient magnetizing current value is maintained under rated rotor current value to ensure smooth start-up process. Start-up algorithm guarantees the reach of a stable system functioning. Suggested topology, that includes a supercapacitor as a supplementary source, can be disconnected once wind system boots. Predictive controllers are used to ensure fast response. An energy management system is developed to monitor power flow. Considered model and control strategy of each system component, as well as start-up process are presented. Simulation results prove the effectiveness of the proposed start-up system.

Research of Operation of Predictive Controllers of Distributed Generation Plant in Power Supply System with Energy Storage

The article discusses the power supply system of an industrial enterprise, which included a turbine generator plant operating on the basis of a synchronous generator equipped with predictive voltage and rotor speed controllers, as well as a high-power electric energy storage device. A description of the models of this plant, predictive controllers and energy storage, as well as the results of modeling when the system goes into an isolated mode of operation are given. Simulation was performed in MATLAB environment using Simulink and SimPowerSystems packages. The purpose of the work was to study the behavior of the proposed predictive controllers during the transition of the power supply system to the island (isolated) mode. Based on the results of computer simulation, it was concluded that the use of predictive controllers improves the damping properties of the system. The use of an energy storage device that is automatically connected to the network when the voltage drops, allows to reduce the overvoltage at the terminals of the generator during its unloading, as well as to reduce the required mechanical power on the turbine shaft in comparison with a permanently connected device. Predictive controllers can be recommended to increase the stability of distributed generation plants when switching to an isolated mode. It is advisable to conduct further research in the direction of creating algorithms for coordinated operation of controllers