Multi-input Multi-output Model Predictive Control Research Articles

Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures

Improving the geometric accuracy of the deposited component is essential for the wider adoption of wire arc additive manufacturing (WAAM) in industries. This paper introduces an online layer-by-layer controller that operates robustly under various welding conditions to improve the deposition accuracy of the WAAM process. Two control strategies are proposed and evaluated in this work: A PID algorithm and a multi-input multi-output model-predictive control (MPC) algorithm. After each layer of deposition, the deposited geometry is measured using a laser scanner. These measurements are compared against the CAD model, and geometric errors are then compensated by the controller, which generates a new set of welding parameters for the next layer. The MPC algorithm, combined with a linear autoregressive (ARX) modelling process, updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables and predicted responses. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. The performance of the designed control architecture is investigated through both simulation and experiments. Results show that the real-time control performance is improved by increasing the complexity of implemented control algorithm: controlled geometric fluctuations in the test component were reduced by 200% whilst maintaining fluctuations within a 3 mm limit under various welding conditions. In addition, the adaptiveness of designed control strategy is verified by accurately controlling the fabrication of a part with complex geometry.

Handling Enhancement of Autonomous Emergency Steering for Reduced Road Friction Using Steering and Differential Braking

Steering has more potential than braking to prevent rear-end collisions at higher relative velocities. A path tracking controller based on multi-input multi-output (MIMO) model predictive control (MPC) is proposed to enhance the handling performance of autonomous emergency steering in this paper. A six-state MIMO bicycle model including actuator dynamics of steering and differential braking is used for model prediction. Two control inputs are front wheel steering angle and direct yaw moment. Two model outputs are lateral displacement and heading angle. According to the work load ratios at four wheels, control allocation is used to determine the optimal braking force distribution to prevent tire force saturation. The performance of a single-input single-output (SISO) MPC that uses only steering angle control to track the lateral displacement of the desired path is employed to benchmark the performance of the proposed algorithm. Simulation results show that both SISO MPC and MIMO MPC can track the path on nominal road surface with high road friction coefficient of 0.9. For a road surface with medium road friction coefficient of 0.7, the SISO MPC is unable to track the path and loses directional stability. However, the MIMO MPC can still track the path and demonstrate robust path tracking and handling enhancement against model uncertainty due to reduced road friction.

Improved 3D Imaging Performance of AFM

Over the last three decades the Atomic Force Microscopy (AFM) has played an important role in the field of nanotechnology for imaging and manipulating samples. However, the acquisition of a high-quality imaging performance by an AFM is significantly influenced by its key scanning element called its Piezoelectric Tube Actuator (PTA). In this article, to improve its performance, a multi-input multi-output model predictive control scheme for achieving improved 3D scanned image of sample is proposed. The proposed controller achieves this by greatly overcoming the problem of tilted characters and nonlinearity effects in the AFM scanned images. The experimental outcomes are demonstrating the effectiveness of the proposed control technique.

Actuator Fault Tolerant Control Based on a MIMO-MPC: Application in a Double-Pipe Heat Exchanger

In this work, an experimental fault tolerant control (FTC) implementation is presented. The FTC is based on a multi-input multi-output (MIMO) model predictive control (MPC). The aim of the FTC is to keep on operating a double-pipe counter-current heat exchanger even if the main actuator of the heat exchanger is stuck open. To develop the FTC, an adaptive observer was implemented in order to design a fault detection and isolation (FDI) system. In the FDI system, the cold and hot water flow rate estimations by the adaptive observer are compared to the control signals provided by the MPC. The results of the implementation of the FTC using a MIMO model predictive control were compared to the results obtained in a previous work which was developed using model-following control.

Error compensation in atomic force microscope scanned images

The design of a multi-input–multi-output (MIMO) model predictive control (MPC) framework for reducing errors in images scanned by an atomic force microscope (AFM) is presented. To improve the damping capability of the proposed control framework, it is augmented with a damping compensator. The MIMO form of this control framework compensates the tilted natures of the scanned images by compensating the cross-coupling effect while its augmented damping compensator reduces the vibration effect by improving damping in the resonant mode of the AFM's piezoelectric tube scanner. Experimental results using the existing AFM proportional–integral controller and single-input–single-output MPC are also presented to show the effectiveness of the MIMO MPC controller. This Letter is an extension of an authors’ earlier published work.

Nonlinearity Effects Reduction of an AFM Piezoelectric Tube Scanner Using MIMO MPC

The design of a controller which compensates for the effects of creep, hysteresis, vibration, and cross-coupling in a piezoelectric tube scanner (PTS) is presented in this paper. The PTS is a key nanopositioning component installed in a commercial atomic force microscope (AFM) to perform scanning. The impediments to fast scanning due to PTS dynamics are: 1) the presence of mechanical resonances; 2) nonlinearities due to the piezoelectric characteristics; and 3) the cross-coupling effect between ${x}$ - and ${y}$ -axes in the PTS. In this paper, a multi-input multi-output model predictive control (MPC) scheme is designed to counteract the effects of creep, hysteresis, vibration, and cross-coupling in a PTS. Also, a damping compensator is included to suppress the vibration effect at its resonance frequency. The proposed controller achieves a high closed-loop bandwidth and significant damping of the resonant mode. To evaluate the performance improvement using the proposed control scheme, an experimental comparison with the existing AFM proportional-integral (PI) controller and a single-input single-output (SISO) MPC is conducted. Enhancement in the scanning speed up to 125 Hz is observed with the proposed controller.

Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor

In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA) and reactor temperature (T). NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.

Indoor Temperature Control of an Air-Conditioning System Using Model Predictive Control

The mathematical model of a air-conditioning system is generally very complex and difficult to apply to controller design. In this paper, simple models applicable to the controller design are obtained by modeling the air-conditioning system by single-input single-output between compressor speed and indoor temperature, and by multi-input single-output between compressor speed, indoor fan speed and indoor temperature. Using these empirical models, model predictive control(MPC) technique was implemented for indoor temperature control of the air-conditioning system. It has been shown from various experiments that the indoor temperature control based on the MPC scheme yields reasonably good tracking performance with smooth changes in plant inputs. this multi-input multi-output MPC approach can be extended to multi air- conditioning systems where the conventional PID control scheme is very difficult to apply.