PID control with gravity compensation for hydraulic 6-DOF parallel manipulator

Abstract A novel model-based controller for 6 degree-of-freedom (DOF) hydraulic driven parallel manipulator considering the nonlinear characteristic of hydraulic systems-proportional plus derivative with dynamic gravity compensation controller is presented, in order to improve control performance and eliminate steady state errors. In this paper, 6-DOF parallel manipulator is described as multi-rigid-body systems, the dynamic models including mechanical system and hydraulic driven system are built using Kane method and hydromechanics methodology, the numerical forward kinematics and inverse kinematics is solved with Newton-Raphson method and close-form solutions. The model-based controller is developed with feedback of actuator length, desired trajectories and system states acquired by forward kinematics solution as the input and servovalve current as its output. The hydraulic system is decoupled by local velocity compensation in inner control loop prerequisite for the controller. The performance revolving stability, accuracy and robustness of the proposed control scheme for 6-DOF parallel manipulator is analyzed in theory and simulation. The theoretical analysis and simulation results indicate the controller can improve the control performance and eliminate the steady state errors of 6-DOF hydraulic driven parallel manipulator

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.Comment: 33 pages, 11 figure

Comparative stability analysis of droop control approaches in voltage-source-converter-based DC microgrids

Droop control has been widely applied in DC microgrids (MGs) due to its inherent modularity and ease of implementation. Among the different droop control methods that can be adopted in DC MGs, two options have been considered in this paper; I-V and V-I droop. I-V droop controls the DC current depending on the DC voltage whilst V-I droop regulates the DC voltage based on the output current. The paper proposes a comparative study of V-I/I-V droop control approaches in DC MGs focusing on steady-state power sharing performance and stability. The paper presents the control scheme for current-mode (I-V droop) and voltage-mode (V-I droop) systems, derives the corresponding output impedance of the source subsystem including converters dynamics and analyzes the stability of the power system when supplying constant power loads. The paper investigates first the impact on stability of the key parameters including droop gains, local control loop dynamics and number of sources and then performs a comparison between current-mode and voltage-mode systems in terms of stability. In addition, a generalized analytical impedance model of a multi-source, multi-load power system is presented to investigate stability in a more realistic scenario. For this purpose, the paper proposes the concept of “global droop gain” as an important factor to determine the stability behaviour of a parallel sources based DC system. The theoretical analysis has been validated with experimental results from a laboratory-scale DC MG

Sensorless multi-loop control of phase-controlled series-parallel resonant converter

This paper proposes a multi-loop controller for the phase-controlled series-parallel resonant converter. Output voltage is solely measured for control and inner loop is used to enhance closed loop stability and dynamic performance compared to single-loop control. No additional sensors are used for inner loop variables. These are estimated using a Kalman filter, based on a linearized converter model. The advantage of this sensorless scheme is not only reducing the number of sensors but more significantly providing an alternative to sensing high frequency resonant tank variables which require high microcontroller resolution in real time. First, the converter non-linear large signal behavior is linearized using a state feedback based scheme. Consequently, the converter preserves its large signal characteristics while modeled as a linear system. Comparison is made between the most suitable state variables for feedback, according to a stability study. Finally, simulation and experimental results are demonstrated to validate the improved system performance in contrast with single-loop control

A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software

Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software. Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis. In this paper, we present a Map-Reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis. We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS.Comment: To be submitted to TACAS 2013. arXiv admin note: substantial text overlap with arXiv:1207.4474, arXiv:1207.409

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

Linearized large signal modeling, analysis, and control design of phase-controlled series-parallel resonant converters using state feedback

This paper proposes a linearized large signal state-space model for the fixed-frequency phase-controlled series-parallel resonant converter. The proposed model utilizes state feedback of the output filter inductor current to perform linearization. The model combines multiple-frequency and average state-space modeling techniques to generate an aggregate model with dc state variables that are relatively easier to control and slower than the fast resonant tank dynamics. The main objective of the linearized model is to provide a linear representation of the converter behavior under large signal variation which is suitable for faster simulation and large signal estimation/calculation of the converter state variables. The model also provides insight into converter dynamics as well as a simplified reduced order transfer function for PI closed-loop design. Experimental and simulation results from a detailed switched converter model are compared with the proposed state-space model output to verify its accuracy and robustness

Dynamic Control Flow in Large-Scale Machine Learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure