Real-time Control Algorithm Research Articles

Analytic Modeling for Precise Speed Tracking of Multilink Robotic Fish

In this paper, a novel analytical-based kinematic and dynamic modeling and control approach is developed for reference speed tracking of a multijoint robotic fish. The kinematic model is improved by the introduction of input signal for controlling body undulation amplitude without distorting the model structure. Next, the dynamic model is developed in the discrete-time domain primarily because fish swimming pattern generates oscillating continuous thrust where the effect of thrust over one cycle of undulation can be calculated through averaging. Hence, the dynamics can be discretized according to the undulation rate. The novelty in developing the discrete-time dynamical model is three fold. First, the thrust gradient is derived with assistance of Lighthill's large amplitude elongated body theory. More importantly, affine-in-control dynamical model form, essential for designing a real-time model-based control algorithm, is derived by inclusion of the updated kinematic model. Second, the thrust delay is studied via pulse input response of the motion system, and incorporated in the dynamical model. Third, drag force coefficient calculation is also proposed by comparing the thrust model and empirically recorded steady-state speeds of the robotic fish. Finally, discrete-time model-based sliding-mode control (SMC) is designed for speed tracking. Experimental results verify that SMC based on the proposed dynamical model, can significantly improve speed tracking performance of a multilink robotic fish.

Fast forward kinematics algorithm for real-time and high-precision control of the 3-RPS parallel mechanism

A new forward kinematics algorithm for the mechanism of 3-RPS (R: Revolute; P: Prismatic; S: Spherical) parallel manipulators is proposed in this study. This algorithm is primarily based on the special geometric conditions of the 3-RPS parallel mechanism, and it eliminates the errors produced by parasitic motions to improve and ensure accuracy. Specifically, the errors can be less than 10–6. In this method, only the group of solutions that is consistent with the actual situation of the platform is obtained rapidly. This algorithm substantially improves calculation efficiency because the selected initial values are reasonable, and all the formulas in the calculation are analytical. This novel forward kinematics algorithm is well suited for real-time and high-precision control of the 3-RPS parallel mechanism.

Characterization of different bubble formulations for blood-brain barrier opening using a focused ultrasound system with acoustic feedback control

Focused ultrasound combined with bubble-based agents serves as a non-invasive way to open the blood-brain barrier (BBB). Passive acoustic detection was well studied recently to monitor the acoustic emissions induced by the bubbles under ultrasound energy, but the ability to perform reliable BBB opening with a real-time feedback control algorithm has not been fully evaluated. This study focuses on characterizing the acoustic emissions of different types of bubbles: Optison, Definity, and a custom-made nanobubble. Their performance on reliable BBB opening under real-time feedback control based on acoustic detection was evaluated both in-vitro and in-vivo. The experiments were conducted using a 0.5 MHz focused ultrasound transducer with in-vivo focal pressure ranges from 0.1–0.7 MPa. Successful feedback control was achieved with all three agents when combining with infusion injection. Localized opening was confirmed with Evans blue dye leakage. Microscopic images were acquired to review the opening effects. Under similar total gas volume, nanobubble showed a more reliable opening effect compared to Optison and Definity (p < 0.05). The conclusions obtained from this study confirm the possibilities of performing stable opening using a feedback control algorithm combined with infusion injection. It also opens another potential research area of BBB opening using sub-micron bubbles.

Reprint of: A practical algorithm for real-time active sound control with preservation of interior sound

In the active sound control problem a bounded domain is protected from noise generated outside via implementation of secondary sound sources on the perimeter. In the current paper we consider a quite general formulation in which sound sources are allowed to exist in the region to be shielded. The sound generated by the interior sources is considered as desired. It is required to remain it unaffected by the control in the protected area. This task proves to be much more complicated than the standard problem of active sound control because of the reverse effect of the controls on the input data. A novel practical algorithm is proposed that can be used for a real-time control. It accepts a preliminary tuning of the control system. In the algorithm the only input information eventually needed is the total acoustic field near the perimeter of the region to be shielded. It includes the contribution from both primary and secondary sources. In the algorithm the noise component to be attenuated is automatically extracted from the total acoustic field. The control system can potentially operate in a real-time regime since it only requires a consequent solution of a quadratic programming problem.

TORBEAM 2.0, a paraxial beam tracing code for electron-cyclotron beams in fusion plasmas for extended physics applications

The paraxial WKB code TORBEAM (Poli, 2001) is widely used for the description of electron-cyclotron waves in fusion plasmas, retaining diffraction effects through the solution of a set of ordinary differential equations. With respect to its original form, the code has undergone significant transformations and extensions, in terms of both the physical model and the spectrum of applications. The code has been rewritten in Fortran 90 and transformed into a library, which can be called from within different (not necessarily Fortran-based) workflows. The models for both absorption and current drive have been extended, including e.g. fully-relativistic calculation of the absorption coefficient, momentum conservation in electron–electron collisions and the contribution of more than one harmonic to current drive. The code can be run also for reflectometry applications, with relativistic corrections for the electron mass. Formulas that provide the coupling between the reflected beam and the receiver have been developed. Accelerated versions of the code are available, with the reduced physics goal of inferring the location of maximum absorption (including or not the total driven current) for a given setting of the launcher mirrors. Optionally, plasma volumes within given flux surfaces and corresponding values of minimum and maximum magnetic field can be provided externally to speed up the calculation of full driven-current profiles. These can be employed in real-time control algorithms or for fast data analysis.

The interaction between scheduling and control of semi-cyclic hybrid systems

In this paper a new iterative approach is proposed for the design of a combined real-time scheduling and control algorithm that can be applied to industrial systems that are described by a hybrid model with a (semi-)cyclic behavior. Traditionally scheduling and control problems are considered in a sequential way. First the scheduling problem is solved and subsequently the control problem. This may result in inconsistent solutions such that the system may not operate adequately and does not reach the desired operational targets.In our approach scheduling is done with model predictive control using a switching max-plus linear model of the discrete event part of the system. The interface with a reference generator determines whether the computed reference signal will lead to a feasible response. Furthermore, it estimates the duration of the operations in the system based on the actual state, and communicates that with the scheduler. In an iterative procedure the optimal and feasible schedule can be computed. In a case study the railway traffic on a single track is considered, showing that updating the schedule results in feasible local speed profiles for the trains and less delay in the overall system in case of a delay.

Verification and online updating of decision making control logic for onboard real-time control systems

The errors during the decision making process in the control system of a modern complex technical system such as a ship, a plane, a spaceship or a power station can lead to unacceptable consequences. Meanwhile, decision making is based on so-named “control logic” described in dedicated specification documents and then implemented by the hardware and software in the real time mode. There are some problems in this process, caused by contradictions and incompletenesses in the specification documents written in natural language, and misunderstanding between specialists in onboard systems, operational engineers, and programmers. In this paper, two practical examples of verification and online updating of spacecraft control logic are described. The approaches we used allow avoiding the mentioned problems. The theoretical basis for verification is Real-Time Control Algorithms Logic RTCAL. Using this, we have developed and successfully applied software tools and domain-specific languages used at the design and operational stages of spacecraft control. The ongoing work includes introducing SMT solvers into our approach, and automatic generation of valid control logic.

Exploring Numerically the Benefits of Water Discharge Prediction for the Remote RTC of WDNs

This paper explores numerically the benefits of water discharge prediction in the real time control (RTC) of water distribution networks (WDNs). An algorithm aimed at controlling the settings of control valves and variable speed pumps, as a function of pressure head signals from remote nodes in the network, is used. Two variants of the algorithm are considered, based on the measured water discharge in the device at the current time and on the prediction of this variable at the new time, respectively. As a result of the prediction, carried out using a polynomial with coefficients determined through linear regression, the RTC algorithm attempts to correct the expected deviation of the controlled pressure head from the set point, rather than the currently measured deviation. The applications concerned the numerical simulation of RTC in a WDN, in which the nodal demands are reconstructed stochastically through the bottom-up approach. The results prove that RTC benefits from the implementation of the prediction, in terms of the closeness of the controlled variable to the set point and of total variations of the device setting. The benefits are more evident when the water discharge features contained random fluctuations and large hourly variations.

The optimal online control of the instantaneous power and the multiphase source’s current

AbstractThe paper presents the new optimal real-time control algorithm of the power source. The minimum of the square-instantaneous current was assumed as an optimal criterion, with an additional constraint on source instantaneous power. The mathematical model of a multiphase source was applied as a voltage-current convolution in the discrete time domain. The resulting control algorithm was the recursive digital filter with infinite recursion.

Combining Model Predictive Control with a Reduced Genetic Algorithm for Real-Time Flood Control

AbstractThis paper presents a novel meta-heuristic approach for real-time flood control. The approach combines model predictive control (MPC) with a reduced genetic algorithm (RGA) to quickly find ...

Real-time algorithm for self-reflective model predictive control

This paper is about a real-time model predictive control (MPC) algorithm for a particular class of model based controllers, whose objective consists of a nominal tracking objective and an additional learning objective. Here, the construction of the learning term is based on an economic optimal experiment design criterion. It is added to the MPC objective in order to excite the system on purpose thereby improving the accuracy of the state and parameter estimates in the presence of incomplete or noise affected measurements. The focus of this paper is on so-called self-reflective model predictive control schemes, which have the property that the additional learning term can be interpreted as the expected loss of optimality of the controller in the presence of random measurement errors. The main contribution is a formulation-tailored algorithm, which exploits the particular structure of self-reflective MPC problems in order to speed-up the online computation. It is shown that the proposed algorithm can solve the self-reflective optimization problems with reasonable additional computational effort and in real-time.

Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Considering the possible overvoltage caused by the high proportion of photovoltaic systems (PVs) accessing distribution networks in the future, traditional centralized control methods will be too complex to satisfy the control response time demands. To solve this problem this paper presents a two-level voltage control method. At the day-ahead level, based on the PV-output and load-demand forecast, a community detection algorithm using an improved modularity index is introduced to divide the distribution network into clusters; a day-ahead optimal scheduling is drawn up on the basis of the network partition, and the objective is to minimize the operation costs of the distribution networks. At the real-time level, under the day-ahead optimal scheduling and network partition of the upper level, a real-time optimal voltage control algorithm is proposed based on the real-time operation data of the distribution networks, and the objective is to correct the day-ahead optimal scheduling through modifications. Thus, the algorithm realizes the combination of day-ahead scheduling and real-time control and achieves complete zonal voltage control for future distribution networks with high proportion of PVs. The proposed method can not only optimize the tap operation of an on-load tap changer (OLTC), improving the PV hosting capacity of the distribution network for a high proportion of PVs, but can also reduce the number of control nodes and simplify the control process to reduce the optimization time. The proposed approach is applied to a real, practical, 10 kV, 62-node feeder in Zhejiang Province of China to verify its feasibility and effectiveness.

Model‐driven real‐time control coordination for distribution grids with medium‐scale photovoltaic generation

Increasing penetration of photovoltaic (PV) generation may significantly impact existing voltage regulation schemes. In particular, the lack of coordination between control devices and fast change in PV generation can cause voltage magnitude to go beyond limits. This study addresses the issue by providing a novel real-time centralised voltage control algorithm to tackle voltage fluctuations caused by variability of PV power generation. The new method considers autonomous response of upstream voltage regulation devices and provides optimised set-points for the chosen downstream controllers. Furthermore, the investigated method was tested in the real-time digital simulator on a model of 11kV grid located in a semi-rural area of Queensland, Australia. The control algorithm was designed in MATLAB with a multidimensional, non-linear, mixed-integer optimisation. The parallel computing implementation of the KNITRO optimiser helped to provide quicker and more accurate response than the existing uncoordinated control schemes. As a result, optimised control coordination leads to a substantial reduction in voltage fluctuations and decrease in regulation time. Moreover, the proposed method detects and eliminates unnecessary control operations. Thus, it can also prevent voltage hunting in distribution networks.

A practical algorithm for real-time active sound control with preservation of interior sound

In the active sound control problem a bounded domain is protected from noise generated outside via implementation of secondary sound sources on the perimeter. In the current paper we consider a quite general formulation in which sound sources are allowed to exist in the region to be shielded. The sound generated by the interior sources is considered as desired. It is required to remain it unaffected by the control in the protected area. This task proves to be much more complicated than the standard problem of active sound control because of the reverse effect of the controls on the input data. A novel practical algorithm is proposed that can be used for a real-time control. It accepts a preliminary tuning of the control system. In the algorithm the only input information eventually needed is the total acoustic field near the perimeter of the region to be shielded. It includes the contribution from both primary and secondary sources. In the algorithm the noise component to be attenuated is automatically extracted from the total acoustic field. The control system can potentially operate in a real-time regime since it only requires a consequent solution of a quadratic programming problem.

Real-Time Control Algorithms for a Hybrid Electric Race Car Using a Two-Level Model Predictive Control Scheme

Since 2014, the Formula 1 power unit has consisted of an internal combustion engine equipped with an electrified turbocharger and a powerful electric motor with regenerative braking and boosting capabilities. This hybrid electric powertrain is controlled by a dedicated energy management system, whose decisions significantly influence the achievable lap time, as well as the fuel and electric energy consumption. Therefore, feedback control algorithms must be designed in order to follow lap time optimal strategies and properly react to disturbances. In this paper, we propose a real-time control algorithm based on a two-level model predictive control (MPC) scheme. A high-level controller, based on a convex model of the system, is used to periodically update the optimal strategies, whereas a zone MPC scheme is designed as a linear program to follow these trajectories in an optimal way. Each iteration of the low-level MPC can be calculated within few milliseconds, thus allowing for a suitable update frequency. The optimality of the presented controller is verified using a benchmark simulator, and its performance is finally tested on a third-party high-fidelity nonlinear simulator of the race car.

Parallel Explicit MPC for Hardware with Limited Memory

This paper proposes a real-time model predictive control (MPC) algorithm for problems with convex quadratic objectives, linear dynamic systems, as well as polytopic state-and control constraints. The method features, on the one hand, explicit model predictive control algorithms and parametric optimization, but, on the other hand, also uses ideas from iterative distributed optimization. This leads to an explicit real-time algorithm with constant and verifiable run-time bounds that scale linearly with the prediction horizon length of the MPC problem. Moreover, unlike standard explicit MPC algorithms, the memory requirements of the proposed algorithm do not dependent on the prediction horizon length. The practical advantages of the method are illustrated with a numerical example.

Stiffness and damping real-time control algorithms for adjustable suspensions : A strategy to reduce dynamical effects on vehicles in off-road conditions

In this paper, the problem of dynamical effects of ground irregularity or stability of vehicles in off-road conditions is addressed thanks to a dynamic analysis of the vehicle. In particular, the choice of suitable real-time values for the stiffness and the damping of adjustable suspensions, in order to reduce dynamical effects, is investigated thanks to the analysis of a Laplace transfer function describing the mechanical behaviour of the vehicle. Moreover, a decoupled load transfer computation to identify the suspended masses on each suspension is used and a way to modify in real time suspension parameters by modifying the settling time value in order to drive over obstacles is proposed. This new point of view may permit to use effectively adjustable suspensions in off-road context. Previous approaches indeed only focus on the control of adjustable suspensions on road or by using an actuator in parallel of the suspension and are often dedicated to the driver comfort.

Model predictive controller design for boost DC–DC converter using T–S fuzzy cost function

ABSTRACTThis paper proposes a Takagi–Sugeno (T–S) fuzzy method to select cost function weights of finite control set model predictive DC–DC converter control algorithms. The proposed method updates the cost function weights at every sample time by using T–S type fuzzy rules derived from the common optimal control engineering knowledge that a state or input variable with an excessively large magnitude can be penalised by increasing the weight corresponding to the variable. The best control input is determined via the online optimisation of the T–S fuzzy cost function for all the possible control input sequences. This paper implements the proposed model predictive control algorithm in real time on a Texas Instruments TMS320F28335 floating-point Digital Signal Processor (DSP). Some experimental results are given to illuminate the practicality and effectiveness of the proposed control system under several operating conditions. The results verify that our method can yield not only good transient and steady-state responses (fast recovery time, small overshoot, zero steady-state error, etc.) but also insensitiveness to abrupt load or input voltage parameter variations.

Hierarchical control for DC microgrid clusters with high penetration of distributed energy resources

In this paper, a real-time tertiary control algorithm for DC microgrids will be developed and implemented. Tertiary control, in the context of this paper, refers to the control layer that transcends the boundaries of a single microgrid, and hence supervises a group of microgrid central controllers (MGCC). Microgrids are considered as dynamic resources and loads that can inject or draw active and/or reactive power to/from the grid, depending on their loading condition. This paper focuses on microgrids with high penetration of renewable resources. To overcome renewable energy intermittency, the developed algorithm virtually aggregates neighboring microgrids into clusters, which virtually exchange energy through the distribution grid infrastructure stabilizing the main power infeed. For improved short-term planning, the developed algorithm periodically forecasts the net available energy from each microgrid. In addition, it solves a set of load flow equations, governed by predefined constraints, to determine (near-) optimal set points to the various microgrids, which consequently improves the efficiency and voltage stability of the distribution network, and increase its level of survivability. This study highlights some key examples of how the microgrids can be used to support the operation of the main grid, e.g. enhancing the voltage profile on a distribution feeder. The algorithm was validated experimentally using a laboratory scale testbed setup.

A technology and software toolset for design and verification of real-time control algorithms

This paper presents a methodology and a specially developed software toolset for design and verification of real-time control algorithms and programs. The real-time control algorithm must timely execute required actions in accordance with the current situation reflected by the parameters checked. This kind of algorithm is an example of a mission-critical application. Unfortunately, these algorithms suffer from mistakes and errors resulting in disasters, large material losses, etc. That is why the process of design, development, and verification of real-time control algorithms is rather time- and labor-consuming. Verification usually involves many-staged debugging on special testbeds. Some errors in control algorithms are due to misunderstandings between the participants of the control-system development process: specialists in particular subsystems, system engineers, and programmers. The proposed methodology makes it possible to exclude programmers from the process of control-software development, thereby reducing the number of errors and the labor cost. The main idea is to design and use a special visual notation intuitively understandable for system engineers. This feature should reduce the number of errors in control algorithms due to eliminating the “broken phone effect” when the programmer can misunderstand a program specification and implement it in the wrong software. The toolset consists of special software modules, including modules for visualization, construction, and verification of onboard real-time control algorithms. All modules are executed from an integrated development environment.