Control of a hydraulically-actuated quadruped robot leg

An experimental approach to multi-input multi-output nonlinear active vibration control of a clamped sandwich beam

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.

Higher order nonlinear systems with prevailing delay have been very challenging in significance with stability and process performance. This paper investigates the performance of a type-2 Mamdani intelligent controller implemented a delay dominant system which is modelled using black box approach identified to be a second order nonlinear model for a dual spherical tank liquid level system (DSTLLS) under LabVIEW environment. The adaptive approach of the intelligent Mamdani-based fuzzy controller proves itself to be very competent compared with the earlier experimented methods that already exist. The performance indices like integrated absolute error (IAE) and integrated squared error (ISE) are also calculated for different varying set point changes of DSTLLS. The response and error reduction efficiency of this method of adaptive type-2 intelligent fuzzy (ATIF) controller has been experimented for different flow configurations of this DSTLLS, like multiple input single output (MISO), multiple input multiple output (MIMO) and single input single output (SISO).

A typical section analysis is employed to provide an understanding of the fundamental mechanisms and limitations involved in performing aeroelastic control. The effects of both articulated aerodynamic control surfaces and induced strain actuators are included in the model. The ability of these actuators to effect aeroelastic control is studied for each actuator individually as well as in various combinations. The control options available are examined for single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) classical and optimal control laws. A state-cost-vs-control-cost analysis is performed to assess the effectiveness of optimal linear quadratic regulator (LQR) control laws for different actuators and actuator combinations. The cost comparisons show that strain actuation is an effective means of achieving aeroelastic control and a viable alternative to articulated control surface methods. In addition, the advantages of using multiple actuators to avoid limitations associated with single-actuator systems are demonstrated.

The linear quadratic regulator (LQR) is a classical optimal control approach that can regulate gait dynamics about target kinematic trajectories. Exoskeletons to restore gait function have conventionally utilized time-varying proportional-derivative (PD) control of leg joints. But, these PD parameters are not uniquely optimized for whole-body (full-state) performance. The objective of this study was to investigate the effectiveness of LQR full-state feedback compared to PD control to maintain bipedal walking of a sagittal-plane computational model against force disturbances. Several LQR controllers were uniquely solved with feedback gains optimized for different levels of tracking capability versus control effort. The main implications to future exoskeleton control systems include (1) which LQR controllers out-perform PD controllers in walking maintenance and effort, (2) verifying that LQR desirably produces joint torques that oppose rapidly growing joint state errors, and (3) potentially equipping accurate sensing systems for nonjoint states such as hip-position and torso orientation. The LQR controllers capable of longer walk times than respective PD controllers also required less control effort. During sudden leg collapse, LQR desirably behaved like PD by generating feedback torques that opposed the direction of leg-joint errors. Feedback from nonjoint states contributed to over 50% of the LQR joint torques and appear critical for whole-body LQR control. While LQR control poses implementation challenges, such as more sensors for full-state feedback and operation near the desired trajectories, it offers significant performance advantages over PD control.

Visible Light Communication (VLC) has emerged as a powerful technique for wireless communication systems. Providing high data rate and increasing capacity are the major problems in VLC. Recent evidence suggests that Multiple Input Multiple Output (MIMO) technique can offers improved data rates and increased link range. This paper describes the design and implementation of visible light communication system in indoor environment exploring the benefits of MIMO. The specific objective of this research was to implement a 4×4 Multiple Input (LEDs) Multiple Output (photodetectors)-VLC communication system, where a 16 white power LEDs in four arrays are setting up at transmitter and four RX modules are setting up at receiver side without the need for additional power or bandwidth as well as analyze a mathematical model for a VLC- 4×4 MIMO with different modes such as the suitable angles to cover the entire room. The research designs practically an electronic circuit for the transmitter and receiver with inexpensive components. The power distribution of different propagation modes is calculated for Single Input Single Output (SISO) and MIMO channels in typical room dimensions. The results in this paper indicate that the power is distributed uniformly for entire room when implemented VLC-MIMO as compared to VLC-SISO. Furthermore, a 4×4 MIMO implementing VLC is compared in term of BER vs. SINR with SISO communication system for both Line of Sight (LOS) and Non Line of Sight (NLOS) propagation modes. Comparing the two results of LOS and NLOS, it can be seen that a 4×4 MIMO implementing VLC for LOS perform better than the same system with NLOS due to decreasing in the received power resulted from the multipath effect.

In a steam power generator, boilers produce steam uninterruptedly in large amounts. The drum boiler system is extremely nonlinear with multi-variable parameters rendering it a very difficult process control problem. The boiler is also inherently unstable due to the effect of the dynamic shrink/swell phenomenon in the drum. Drum boiler systems have been controlled by Single-Input, Single-Output (SISO) Proportional-Integral-Differential (PID) controllers. Such SISO control systems do not take into consideration the interaction between the controlled variables or the effect of demanding load change on boiler dynamics. In this work, we present a hardware-based multi-variable Linear Quadratic Regulator (LQR) controller of the boiler-turbine system. Reconfigurable hardware has been recently used to implement a number of complex control systems due to the performance and flexibility gains of such platforms. The proposed controller is implemented and validated on a Xilinx Artix-7 FPGA AC701 Evaluation Kit. Testing results depict a significant improvement in the control system characteristics compared to the classical PID control techniques.

This paper intends to compare controllers used for non-isolated (transformer less) interleaved double dual boost (IDDB) converter which provide high voltage gain and can be used for high-power applications. A control subsystem is required to improve dynamic behaviour of converter and to have a better output response. In most cases small signal model of converter is obtained which are linearized at an operating point to derive a controller which cannot take uncertainties into consideration. Robust control and Nonlinear control methods are probably areas which can handle uncertainties and non-linearities effectively. Conventional linear quadratic regulator (LQR) controller cannot assure stability if system is highly uncertain. A convex model of converter dynamics is obtained taking uncertainties into account. To apply LQR control in uncertain converter case linear matrix inequalities(LMIs) are used to optimize the performance index. A comparative study of LMI-LQR control, LQR Controller and PID controller is done.

In this paper, the differences between decoupled and coupled control of bank-to-turn (BTT) missiles have been studied in detail from three aspects: stabilizability, linear quadratic regulator (LQR) control and tracking control. It is found that for some parameters the BTT missile can be controlled by a decoupled way, especially for stabilizability and LQR control. However, for some others, the BTT missile cannot be decoupled and a coupled control method is needed. Therefore, for a practical flight control system, the decouplability should be studied before giving a decoupled control method. In addition, in order to avoid the disadvantages of LQR control method, an improved H∞ controller design method is given. Combined with tracking control problem, a new H∞ control method is established for trajectory tracking.

For a class of multi input and multi output nonlinear uncertain systems, a feedback linearization method combined with minimax linear quadratic regulator (LQR) control is proposed. The nominal nonlinear model is assumed to be feedback linearizable with full relative degree. The uncertainties are assumed to be parametric or parameterized and satisfy a certain integral quadratic constraint condition. The proposed method systematically incorporates the parametric uncertainty bound in the design procedure and gives a locally stabilized closed loop system. This procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating points. This two-stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided on longitudinal stabilization of an air-breathing hypersonic flight vehicle in the presence of uncertainties.

A linear quadratic regulator (LQR) of a power converter connected to a doubly fed induction generator (DFIG) of a wind energy conversion system (WECS) is presented. Active and reactive powers tracking of the grid side converter (GSC) is focused. The main future of this work is to present a robust stabilizing LQR controller that has no dependence on the parametric variation. Generally, the tracking control laws are calculated based on the value of their desired outputs. The standard LQR controllers give good performances in terms of stability margin and certain performance cost. However, LQR control does not ensure maintaining these performances mainly in case of parametric or reference variations. On the one hand, the WECS functioning is conditioned by the uncertain variations of wind velocity and on the other hand by load perturbation. In this paper, an invariant LQR compensator is calculated independently from parametric and reference changes. Moreover, the control problem involves Lyapunov stability condition that ensures the system stability over the admissible ranges of output references and parametric variations. Linear matrix inequalities (LMIs) formulation of the problem is established in order to optimize the LQR performance index. As a consequence, a new formulation of the stabilizing LQR state control is introduced. Finally the performances of the controller are discussed for different references of active and reactive powers.

The demand for ubiquitous telecommunications services forces operators to have a special concern about signal quality and the coverage area they offer to their customers. This was usually checked by using suitable propagation models for Single Input Single Output (SISO) systems, which are no longer the case for new and future mobile generations, such as 5G and beyond. To guarantee good signal quality coverage, operators started to replace these models with Multiple Input Multiple Output (MIMO) ones. To achieve the best results, these models are usually calibrated with Drive Test (DT) measures; however, the DTs available for MIMO propagation models are sparse, in contrast to SISO ones. The main contribution presented in this paper is a methodology to extend the propagation models of SISO systems so they can be applied in MIMO sytems with Single-Carrier and Frequency-Domain Equalization (SC-FDE), while still using DTs acquired for SISO systems. This paper presents the impact on Bit Error Rate (BER) performance and its coverage area resulting from the application of our proposed method. We consider a MIMO SC-FDE system with an Iterative Block Decision Feedback Equalization (IB-DFE) receiver and we present the improvement expressions for the BER that we illustrate with some simulations.

Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance

A recurrent dynamic neural network (RDNN) based on Hopfield model is applied to linear quadratic regulator controller of an irrigation canal. The Saint-Venant equations of open-channel flow are linearized using the Taylor series and a finite difference approximation of the original nonlinear, partial differential equations. Using the linear optimal control theory, a Linear Quadratic Regulator (LQR) controller is developed for an irrigation canal with a single pool. A Hopfield recurrent dynamic neural network is applied to solution of Riccati equation for calculating the LQR control gains. The main advantage of Hopfield neural network model is its robustness and flexibility when dealing with unknown perturbations and parameters. The results of this study shows that an RDNN based LQR controller (RDNN/LQR) for irrigation canals offers an alternative to traditional LQR controller when dealing with the disturbances in the irrigation canals.

Inverted pendulum mounted on a cart is an inherently unstable system which requires control effort to stabilize it at its upright position. In this single input multi output system the pendulum is balanced by controlling the horizontal motion of the cart. Various controller design techniques have been developed to control such systems like PID controllers, LQR controllers, Neural networks etc. The performance of the system for controlling methods of PD (Proportional Derivative) controller and LQR (Linear Quadratic Regulator) controller are compared by simulating in MATLAB SIMULINK and the results are validated through results obtained from a real inverted pendulum system.