Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations

Tube-based robust model predictive control for spacecraft proximity operations in the presence of persistent disturbance

Rendezvous and Proximity Operations (RPOs) of two autonomous spacecraft have been extensively studied in the past years, taking into account both the strict requirements in terms of spacecraft dynamics variations and the limitations due to the actuation system. In this paper, two different Model Predictive Control (MPC) schemes have been considered to control the spacecraft during the final phase of the rendezvous maneuver in order to ensure mission constraints satisfaction for any modeled disturbance affecting the system. Classical MPC suitably balances stability and computational effort required for online implementation whereas Tube-based Robust MPC represents an appealing strategy to handle disturbances while ensuring robustness. For the robust scheme, the computational effort reduction is ensured adopting a time-varying control law where the feedback gain matrix is evaluated offline, applying a Linear Matrix Inequality approach to the state feedback stabilization criterion. An extensive verification campaign for the performance evaluation and comparison in terms of constraint satisfaction, fuel consumption and computational cost, i.e. CPU time, has been carried out on both a three degrees-of-freedom (DoF) orbital simulator and an experimental testbed composed by two Floating Spacecraft Simulators reproducing a quasi-frictionless motion. Main conclusions are drawn with respect to the mission expectations

Automating control system design via a multiobjective evolutionary algorithm

This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints

Fast‐converging robust PR‐P controller designed by using symmetrical pole placement method for current control of interleaved buck converter‐based PV emulator

In this study, the interleaved buck converter-based photovoltaic (PV) emulator current control is presented. A proportional-resonant-proportional (PR-P) controller is designed to resolve the drawbacks of conventional PI controllers in terms of phase management, which means balancing currents evenly between active phases to avoid thermally stressing and provide optimal ripple cancelation in the presence of parameter uncertainties. The resonant path of the controller (PR) with a constant proportional unity gain is designed considering the changing dynamics of a notch filter by pole placement method (adding mutually complementary poles to the notch transfer function) at PWM switching frequency. The proportional gain path (P) of the controller is used to determine the compatibility of the controller with parameter uncertainty of the phases and designed by utilizing loop-shaping method. The proposed controller shows superior performance in terms of 10 times faster-converging transient response, zero steady-state error with significant reduction in current ripple. Equal load sharing that constitutes the primary concern in multiphase converters is achieved with the proposed controller. Implementing of robust control theory involving comprehensive time and frequency domain analysis reveals 13% improvement in the robust stability margin and 12-degree bigger phase toleration with the PR-P controller. In addition to these, the proposed unconventional design process of the controller reduces the computational complexity and provides cost-effectiveness and simple implementation. Moreover, implementing of auxiliary resistor-capacitor (RC) circuits parallel with the inductors to sense the current in each phase removes the need for current measurement sensors that contribute to overall cost of the system

MPC for a low consumption electric vehicle with time-varying constraints

International audienceA multi-phase optimization problem in terms of consumption of an electric vehicle involved in the European Shell Eco-marathon race is formulated. An open-loop optimal driving strategy is derived. Next, a time-varying Model Predictive real-time controller is developed to track the optimal solution and to achieve the minimum consumption. The stability and the convergence of the time-varying Model Predictive controller is proved. The convergence is guaranteed despite the variation of the MPC constraints in time. An example emulating an actual race illustrates the effectiveness of the approach

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

TOWARDS OPTIMAL OPERATION AND CONTROL OF EMERGING ELECTRIC DISTRIBUTION NETWORKS

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics