CN112034828A - Discrete integral sliding mode control device and method of brain-controlled mobile robot - Google Patents
Discrete integral sliding mode control device and method of brain-controlled mobile robot Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0016—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement characterised by the operator's input device
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
Abstract
The invention provides a discrete integral sliding mode control device and a discrete integral sliding mode control method of a brain-controlled mobile robot, wherein the control device comprises a brain-computer interface, a speed interface, a zero-order retainer, a discrete integral sliding mode controller and a speed sensor communicated with the discrete integral sliding mode controller which are sequentially connected; the control method comprises the following steps: initializing the brain-controlled mobile robot, and detecting the state of a speed sensor; receiving a control decision of a brain-controlled operator through a brain-computer interface; converting the control decision of the brain control operator through a speed interface and a zero-order retainer and outputting an expected control speed signal; and according to the expected control speed signal and the real-time speed of the speed sensor, carrying out control input solving through a discrete integral sliding mode controller to obtain a new control signal, and acting on the brain-controlled mobile robot. After the brain-computer interface outputs the expected robot speed signal, the controller is designed to enable the control signal to complete the tracking realization of the expected speed, and the robustness of the system is ensured.
Description
Technical Field
The invention relates to comprehensive application in the fields of human-computer interaction, robot technology, information technology and automatic control, in particular to a discrete integral sliding mode control device and method of a brain-controlled mobile robot.
Background
Wheeled mobile robots are an important application in the field of human-computer interaction. To improve mobility and quality of life in patients suffering from motor neuron diseases (e.g., amyotrophic lateral sclerosis, multiple sclerosis), researchers have developed brain-controlled mobile robotic systems based on brain-computer interface (BMI) technology. The brain-computer interface provides a direct real-time information exchange and control channel for the user and external physical equipment, and can directly decode the brain activity of the user from the neurophysiological signals into a control instruction for the external equipment. Electroencephalogram (EEG) -based brain-computer interface technology has been widely used to develop brain-controlled cursors, brain-controlled virtual keyboards, brain-controlled web browsing, brain-controlled artificial limbs, brain-controlled wheelchairs, brain-controlled vehicles, brain-controlled robots, and other systems.
The mobile robot has the characteristics of high maneuverability, strong traction and simple wheel configuration. The brain-controlled mobile robot is a system (which can complete manned tasks) in which a brain-controlled operator directly uses the brain to control the wheel type mobile robot to move straight and rotate through a brain-computer interface. The appearance and development of the robot greatly expand the activity space of a patient with limited limb movement, and simultaneously provide a novel control mode different from the traditional robot control mode for healthy users.
Researchers at millan et al first proposed electroencephalogram-based brain-controlled mobile robots in 2004, since which the brain-controlled mobile robots have been greatly developed. The key technology of the brain-controlled mobile robot system is the brain-computer interface technology and the control method of the system. The brain-computer interface technology finishes acquisition of electroencephalogram signals, preprocessing of data, feature extraction, classification and command quantization (command is controlled to a desired speed). The control method of the system is to complete the further processing and control realization of the desired speed.
In the development of brain-controlled mobile robot systems, on the one hand, early researchers and system developers focused on directly using brain-computer interfaces to manipulate mobile robots, making great efforts on brain-computer interface technology. Although the performance of the brain-computer interface is improved to a certain extent, the development of the brain-computer interface technology still cannot reach an ideal level due to the unstable characteristics of the brain-computer signal and the limitations of the command number, the response time, the accuracy rate and the like of the brain-computer interface. On the other hand, in view of the safety of the system, developers have devised different controllers to assist users in controlling the mobile robot through a brain-computer interface. When a user uses the brain-controlled mobile robot system, due to the limitation of a brain-computer interface technology, the variation degree of the expected speed of a given robot between the response times of the brain-computer interfaces is still large, and because the manned system of the brain-controlled mobile robot has a large activity space and a large motion range, the motion of the manned system is easily interfered by the outside, so that the brain-controlled mobile robot system has a high requirement on robustness, but the speed control of the current brain-controlled mobile robot still continues the PID control realization of the traditional robot, the bottom layer speed control realization facing the brain-controlled robot is not emphasized, and the robustness and the overall performance of the system are also limited.
Disclosure of Invention
The invention aims to provide a discrete integral sliding mode control method of a brain-controlled mobile robot, which is characterized in that after a brain-computer interface and a speed interface output expected robot speed signals, a controller is designed to enable control signals to complete the tracking realization of the expected speed, and the resistance to system parameter perturbation and external interference is kept, so that the robustness of a brain-controlled mobile robot system is ensured
In order to achieve the purpose, the invention provides the following scheme: the invention provides a discrete integral sliding mode control device of a brain-controlled mobile robot, which is characterized by comprising a brain-computer interface, a speed interface, a zero-order retainer, a discrete integral sliding mode controller and a speed sensor, wherein the brain-computer interface, the speed interface, the zero-order retainer, the discrete integral sliding mode controller and the speed sensor are sequentially connected;
the brain-computer interface is used for translating brain-controlled operators according to the environment information and electroencephalogram signals reflected by the state information of the brain-controlled mobile robot and outputting control decision output control commands;
the speed interface and the zero order keeper are used for receiving the control command and outputting a desired control speed signal;
and the discrete integral sliding mode controller is used for receiving the expected control speed signal and the real-time speed measured by the speed sensor and solving to obtain a new control signal.
Preferably, the environment information includes obstacle information and boundary information in an environment in which the brain-controlled mobile robot is located;
the state information of the brain-controlled mobile robot comprises lateral coordinates, longitudinal coordinates, an orientation angle, a lateral corner speed and a longitudinal straight speed of the brain-controlled mobile robot.
The control method of the discrete integral sliding mode control device of the brain-controlled mobile robot is characterized by comprising the following steps of:
s1, initializing the brain-controlled mobile robot, and detecting the state of the speed sensor;
s2, giving a control decision command of a brain control operator through the brain-computer interface;
s3, converting a control decision command of the brain control operator through the speed interface and the zero-order retainer and outputting an expected control speed signal;
and S4, according to the expected control speed signal and the real-time speed measured by the speed sensor, carrying out control input solving through the discrete integral sliding mode controller to obtain a new control signal, and acting the new control signal on the brain-controlled mobile robot.
Preferably, the speed interface and the zero-order keeper convert the control decision of the brain control operator into: recognizing an operator control command output by the brain-computer interface through the speed interface, and converting the control command into a controlled speed signal increment to obtain a current expected control speed signal; and processing the current expected control speed signal through the zero-order controller to obtain a continuous expected control speed signal.
Preferably, the speed interface defines the operator control command by setting a speed limit value.
Preferably, the solution process of the discrete integral sliding mode controller is as follows: calculating a speed error according to the expected control speed signal and the real-time speed measured by the speed sensor; constructing a discrete integral sliding mode surface according to the speed error, and controlling the speed error through the discrete integral sliding mode surface; and calculating equivalent control input according to the discrete integral sliding mode surface, and designing a control switching term to finally obtain the control torque.
The invention provides the following technical effects:
after the brain-computer interface outputs an expected robot speed signal, a discrete integral sliding mode controller is designed, the discrete integral sliding mode controller obtains the speed information of the robot through a speed sensor inside the brain-controlled mobile robot, and gives a robot control signal at the bottom layer according to the given expected speed signal, so that the implementation of robust control of the speed is completed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic structural diagram of a discrete integral sliding mode control device of a brain-controlled mobile robot according to the present invention;
fig. 2 is a schematic flow chart of the discrete integral sliding mode control method of the brain-controlled mobile robot of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention provides a discrete integral sliding mode control apparatus for brain-controlled robot, including a brain-computer interface, a speed interface, a zero-order retainer, a discrete integral sliding mode controller, and a speed sensor communicating with the sliding mode controller, which are connected in sequence.
The brain-computer interface is used for translating the electroencephalogram signals reflected by the brain-controlled operator according to the current environmental information and the state information of the brain-controlled robot and outputting control commands; the state information of the brain-controlled robot comprises the lateral coordinate, the longitudinal coordinate, the orientation angle, the lateral rotation angular velocity and the longitudinal straight-moving velocity of the robot. The environment information refers to obstacle information and boundary information in the environment where the robot is located.
The speed interface and the zero-order keeper are used for receiving the control command and outputting a desired control speed signal.
And the discrete integral sliding mode controller receives the control speed of the brain-controlled robot and the current moving speed of the brain-controlled robot measured by the speed sensor, performs control input solving to obtain a new control signal, and drives the brain-controlled robot to move.
As shown in fig. 2, there is also provided a discrete integral sliding mode control method for a brain-controlled robot, including the following steps:
and S1, initializing a clock, keeping the acquisition starting time of the speed sensor and the starting time of the discrete integral sliding mode controller synchronous and consistent, clearing data caches in the speed sensor and the discrete sliding mode controller, and designing an input/output port and a register of the discrete integral sliding mode controller.
After the speed sensor of the mobile robot is selected, whether the mobile robot works normally can be judged through the output of the speed sensor. And if the system is in a normal working state, performing the next step, otherwise, reinitializing. Here, the sampling time of the speed sensor is required to be smaller than that of the discrete integration sliding mode controller.
And S2, staring at a corresponding stimulation interface (or performing related motor imagery) to generate a corresponding electroencephalogram signal according to the own control intention of the brain control operator, analyzing the electroencephalogram signal by the brain-computer interface to obtain the control intention of the operator, and outputting a corresponding identification control command.
S3, the speed interface converts the identified control command (left turn, right turn, non-control, acceleration and deceleration, etc.) into the controlled speed signal increment (-0.05rad/S, 0.05rad/S and 0rad/S at the lateral corner speed, 0.025m/S, -0.025m/S at the longitudinal straight speed), and the control signal is applied to the control speed at the previous moment to obtain the current expected control speed signal. The speed interface, as a module for quantizing the command to the speed signal, defines a limit value for controlling the speed, which is specifically:
wherein l (n) is a discrete control command input to the interface model; -2, -1,0,1,2 represent control commands for deceleration, left turn, right turn and acceleration, respectively, in the uncontrolled state the operator wants the robot to maintain the last moment of longitudinal straight speed movement; u (n) and omega (n), u (n-1) and omega (n-1) respectively represent the expected speed of the robot at the time n and the time n-1, and the initial value of the expected speed is 0; delta u and delta omega are control increments of the linear speed and the angular speed of the robot; u. ofmaxAnd omegamax，uminAnd omegaminThe maximum and minimum values of the straight-line or rotation angular speed of the robot.
The output of the speed interface is the brain-controlled robot control speed which the brain-controlled operator expects to output through the brain-controlled interface after passing through the zero-order retainer.
S4, calculating a speed error by the discrete integral sliding mode controller according to the obtained real-time speed information and the expected speed signal, and realizing the design of a discrete integral sliding mode surface, wherein the mathematical form of the obtained speed error e (n) is as follows:
e(n)＝νm(n)-νd(n)＝[um-ud ωm-ωd]T (2)
wherein: v. ofm(n) the mobile robot speed information obtained by the speed sensor; u. ofm、ωmRepresenting brain-controlled mobile robotLongitudinal straight-moving speed and lateral turning speed; v. ofd(n) is the desired control speed signal output by the speed interface via the zero-order keeper; u. ofd、ωdThe expected longitudinal straight-ahead speed and lateral rotation angular speed of the brain-controlled mobile robot are shown.
In the design of the sliding mode surface, an integral link can reduce the tracking steady-state error of speed control, so that the accurate tracking of the speed is realized, and the mathematical form of the discrete integral sliding mode surface is as follows:
wherein: s (n) is a discrete integral sliding mode surface of a discrete integral sliding mode controller; e (0) is the initial speed error at the beginning of each control loop; h (n) represents an integration link, and the steady-state error of the system can be reduced; C. m is a design parameter for a discrete integral synovial controller.
The selection of the design parameter M of the discrete integral synovial controller in this embodiment is determined by the model parameter and the C parameter of the brain-controlled mobile robot, and the mathematical form of the dynamic model state space expression v (n +1) of the brain-controlled mobile robot is as follows:
v(n+1)＝Av(n)+Bτ(n)+d(n) (4)
wherein: A. b is a system matrix and an input matrix of the discrete dynamic model of the mobile robot; v is the speed of the mobile robot; d is the external interference possibly suffered by the mobile robot in motion.
The mathematical form of the M parameter thus designed is as follows:
M＝-C(A-I-BN) (5)
wherein: i is an identity matrix; and N is a designed parameter matrix used for adjusting a closed-loop pole of the controller to ensure the stability of the system.
And obtaining the output of the controller according to the design theory of the discrete sliding mode controller.
Under the condition of not considering the interference d (n) suffered by the system, obtaining equivalent control input by enabling the designed discrete sliding mode surface to be 0 at the next control moment, wherein the mathematical form S (n +1) of the discrete sliding mode surface at the moment of n +1 is as follows:
the mathematical form of the designed equivalent control input is then:
τ(n)eq＝-(CB)-1{(CA+M)v(n)-(C+M)vd(n)-Ce(0)+H(n)} (7)
wherein: tau (n)eqIs an equivalent control signal.
The embodiment designs a mathematical form of a switching item by adding a control signal switching item for processing interference, specifically:
τ(n)sw＝(CB)-1{λS(n)-ηSat(S(n),)} (8)
wherein: tau (n)swIs a switching control signal; λ is a parameter for increasing the convergence speed of the sliding mode controller; eta is a product factor parameter of the interference suffered by the compensation system; the Sat (s (n) function is a switching term to reduce the drive control input chatter, and is mathematically expressed as follows:
therefore, the finally obtained control torque is the sum of the equivalent control signal and the switching control signal, and specifically comprises the following steps:
τ(n)＝τ(n)eq+τ(n)sw (10)
and S5, applying the obtained control torque to the driving wheel of the brain-controlled mobile robot, repeating the step S4 at the next sampling moment, solving the new measured speed information and the new expected control speed generated by the control command of the brain-controlled operator again, and applying a new control signal to the driving wheel.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (6)
1. A discrete integral sliding mode control device of a brain-controlled mobile robot is characterized by comprising a brain-computer interface, a speed interface, a zero-order retainer, a discrete integral sliding mode controller and a speed sensor communicated with the discrete integral sliding mode controller, wherein the brain-computer interface, the speed interface, the zero-order retainer, the discrete integral sliding mode controller and the speed sensor are sequentially connected;
the brain-computer interface is used for translating brain-controlled operators according to the environment information and electroencephalogram signals reflected by the state information of the brain-controlled mobile robot and outputting control decision output control commands;
the speed interface and the zero order keeper are used for receiving the control command and outputting a desired control speed signal;
and the discrete integral sliding mode controller is used for receiving the expected control speed signal and the real-time speed measured by the speed sensor and solving to obtain a new control signal.
2. The discrete integral sliding mode control device of the brain-controlled mobile robot according to claim 1, wherein the environment information includes obstacle information and boundary information in an environment in which the brain-controlled mobile robot is located;
the state information of the brain-controlled mobile robot comprises lateral coordinates, longitudinal coordinates, an orientation angle, a lateral corner speed and a longitudinal straight speed of the brain-controlled mobile robot.
3. The method for controlling the discrete integral sliding mode control device of the brain-controlled mobile robot according to claim 1, comprising the steps of:
s1, initializing the brain-controlled mobile robot, and detecting the state of the speed sensor;
s2, giving a control decision command of a brain control operator through the brain-computer interface;
s3, converting a control decision command of the brain control operator through the speed interface and the zero-order retainer and outputting an expected control speed signal;
and S4, according to the expected control speed signal and the real-time speed measured by the speed sensor, carrying out control input solving through the discrete integral sliding mode controller to obtain a new control signal, and acting the new control signal on the brain-controlled mobile robot.
4. The discrete integral sliding mode control method for the brain-controlled mobile robot according to claim 3, wherein the control decision transformation process of the speed interface and the zero-order keeper to the brain-controlled operator is as follows: recognizing an operator control command output by the brain-computer interface through the speed interface, and converting the control command into a controlled speed signal increment to obtain a current expected control speed signal; and processing the current expected control speed signal through the zero-order controller to obtain a continuous expected control speed signal.
5. The discrete-integral sliding-mode control method of the brain-controlled mobile robot according to claim 4, wherein the speed interface defines the operator control command by setting a speed limit value.
6. The discrete integral sliding mode control method of the brain-controlled mobile robot according to claim 3, wherein the solving process of the discrete integral sliding mode controller is as follows: calculating a speed error according to the expected control speed signal and the real-time speed measured by the speed sensor; constructing a discrete integral sliding mode surface according to the speed error, and controlling the speed error through the discrete integral sliding mode surface; and calculating equivalent control input according to the discrete integral sliding mode surface, and designing a control switching term to finally obtain the control torque.
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