Structure-specified H∞ loop shaping control for balancing of bicycle robots: A particle swarm optimization approach

In this paper, the particle swarm optimization (PSO) algorithm was used to design the structure-specified H∞ loop shaping controllers for balancing of bicycle robots. The structure-specified H∞ loop shaping controller design normally leads to a complex optimization problem. PSO is an efficient meta-heuristic search which is used to solve multi-objectives and non-convex optimizations. A model-based systematic procedure for designing the particle swarm optimization-based structure-specified H∞ loop shaping controllers was proposed in this research. The structure of the obtained controllers are therefore simpler. The simulation and experimental results showed that the robustness and efficiency of the proposed controllers was gained when compared with the proportional plus derivative (PD) as well as conventional H∞ loop shaping controller. The simulation results also showed a better efficiency of the developed control algorithm compared to the Genetic Algorithm based one

Extremum Seeking for Dead-Zone Compensation

13301甲第4148号博士（学術）金沢大学博士論文本文Full 以下に掲載：Journal of Automation and Control Engineering 3(4) pp.265-269 2015. Engineering and Technology Publishing. 共著者：Dessy Novita and Shigeru Yamamoto

Complex Fractional-Order LQIR for Inverted-Pendulum-Type Robotic Mechanisms: Design and Experimental Validation

This article presents a systematic approach to formulate and experimentally validate a novel Complex Fractional Order (CFO) Linear Quadratic Integral Regulator (LQIR) design to enhance the robustness of inverted-pendulum-type robotic mechanisms against bounded exogenous disturbances. The CFO controllers, an enhanced variant of the conventional fractional-order controllers, are realised by assigning pre-calibrated complex numbers to the order of the integral and differential operators in the control law. This arrangement significantly improves the structural flexibility of the control law, and hence, subsequently strengthens its robustness against the parametric uncertainties and nonlinear disturbances encountered by the aforementioned under-actuated system. The proposed control procedure uses the ubiquitous LQIR as the baseline controller that is augmented with CFO differential and integral operators. The fractional complex orders in LQIR are calibrated offline by minimising an objective function that aims at attenuating the position-regulation error while economising the control activity. The effectiveness of the CFO-LQIR is benchmarked against its integer and fractional-order counterparts. The ability of each controller to mitigate the disturbances in inverted-pendulum-type robotic systems is rigorously tested by conducting real-time experiments on Quanser single-link rotary pendulum system. The experimental outcomes validate the superior disturbance rejection capability of the CFO-LQIR by yielding rapid transits and strong damping against disturbances while preserving the control input economy and closed-loop stability of the system

Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%

Swarm Intelligence Autotune For Differential Drive Wheeled Mobile Robot

Differential Drive Wheeled Mobile Robot (DDWMR) is a nonholonomic robot with constrained movement. Such constraint makes robot position control more difficult. A closed-loop control system such as PID can control robot position. However, DDWMR is a Multiple-Input-Multiple-Output system. There will be many feedback gains to be tuned, and the wrong value will make the system unstable. Therefore this research proposes an offline autotune method to choose optimal feedback gain that minimizes a fitness function. The fitness function uses Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). These works propose to autotune feedback gain for DDWMR Jetbot, which implements a PI control system with six feedback gains. The methods used to tune the feedback gain are Particle Swarm Optimization (PSO) and Bird Swarm Algorithm (BSA). There are four different scenarios to do the autotune. The autotune result performance shows that those two methods can find an optimal gain to make the robot follow four different continuous trajectories without much trajectory deformation. PSO and BSA can do an autotune PI gain with six variables to minimize the Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE)

A new device laboratory: Experimental validation

Postprint (published version

Fuzzy-Immune-Regulated Adaptive Degree-of-Stability LQR for a Self-Balancing Robotic Mechanism: Design and HIL Realization

This letter formulates a fuzzy-immune adaptive system for the online adjustment of the Degree-of-Stability (DoS) of Linear-Quadratic-Regulator (LQR) procedure to strengthen the disturbance attenuation capacity of a self-balancing mechatronic system. The fuzzy-immune adaptive system uses pre-configured control input-based rules to alter the DoS parameter of LQR for dynamically relocating the closed-loop system's eigenvalues in the complex plane's left half. The corresponding changes in the eigenvalues are conveyed to the Riccati equation, which eventually yields the self-adjusting LQR gains. This arrangement allows for the flexible manipulation of the applied control effort and the response speed as the error conditions change. The efficacies of the self-tuning LQR scheme are verified by performing custom-designed hardware-in-the-loop experiments on the Quanser rotary inverted pendulum system. As compared to the DoS-LQR, the proposed controller improves the pendulum's transient recovery time, overshoots, input demands, and offsets by 32.3%, 50.5%, 33.9%, and 33.3%, respectively, under disturbances. These experimental outcomes verify that the proposed self-tuning LQR law considerably improves the system's disturbance attenuation capability

Self-Balancing Two Wheeled Robot

Automation is increasingly becoming a larger part of daily life. From automated telephone calls to machines in manufacturing, robots are generally an effective and efficient way to reduce overhead costs, increase consistency in products and services, and perform tasks that may be hazardous to humans. The successful design and building of a two-wheeled balancing robot demonstrates a knowledge of control systems and sensor interfacing that can translate to real world applications. Helping seniors live on their own, performing dangerous mining work, repeatedly screwing the same piece in an assembly line, are great examples of a controls automation system freeing time up for a person to perform more important or more complex tasks, and all of these tasks use design techniques similar to that of a balancing robot. The robot will balance on two wheels and be able to have loads of varying weight and size (up to 5lbs) placed on the top platform. It will be capable of handling disturbances including bumps from humans or running into stationary objects and it can accommodate flooring changes (carpet, tile etc.) while maintaining balance. An accelerometer and a gyroscope feed information back to a pic microcontroller which feeds a PWM signal to two motors that drive the wheels so they stay under the center of mass of the robot